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EX-99.1 70 s002218x4_ex99-1.htm EXHIBIT 99.1 Virgin Trains USA LLC - Brightline Ridership and Revenue Study by Louis Berger U.S., Inc., dated September 2018

Exhibit 99.1
 

 

 

 

   
  Brightline Ridership and
Revenue Study
   
  Phase I Study: Southeast Florida and Orlando
Phase II Study: Extension to Tampa
   
  September 2018

 

Disclaimer

 

This Report was prepared by The Louis Berger U.S., Inc., (LB) for the benefit of All Aboard Florida Operations, LLC (Client) pursuant to a Professional Services Agreement dated May 17, 2017.

 

LB has performed its services to the level customary for competent and prudent engineers performing such services at the time and place where the services to our Client were provided. LB makes or intends no other warranty, express or implied.

 

Certain assumptions regarding future trends and forecasts may not materialize, which may affect actual future performance and market demand, so actual results are uncertain and may vary significantly from the projections developed as part of this assignment. The data used in the Report was current as of the date of the Report and may not now represent current conditions.

 

The Report is provided for information purposes only. LB makes no representations or warranty that the information in the Report is sufficient to provide all the information, evaluations, and analyses necessary to satisfy the entire due diligence needs of a reader of this report.

 

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Table of Contents

             
Executive Summary 7
  ES-1   Overview of the Investment Grade Study Process 8
  ES-2   Study Process 9
  ES-3   Overview of the Brightline Rail Service 10
  ES-4   Relevant Market for High Speed Rail 11
  ES-5   Key Assumptions 13
  ES-6   Key Findings and Ridership and Revenue Forecast 15
1.0    

Introduction

 19
  1.1    

Organization of Report

 20
2.0    

Travel Market Socioeconomic and Demographic Conditions

 21
  2.1    

Population

 22
  2.2    

Population Forecasts

 24
  2.3    

Employment

 28
  2.4    

Employment Forecasts

 30
  2.5    

Income

 31
  2.6    

Travel and Tourism

 32
  2.7    

Domestic Visitation

 34
  2.8    

International Visitation

 34
3.0    

Intercity Travel Market for Brightline

 36
  3.1    

Addressable Market Geography for Brightline

 37
  3.2    

Travel Market for Brightline Phase I Study

 38
    3.2.1     Bus 38
    3.2.2     Long-Distance Rail (Amtrak) 41
    3.2.3     Short-Distance Rail (Tri-Rail) 42
    3.2.4     Air 45
    3.2.5     Auto 47
  3.3    

Travel Market for Brightline Phase II Study

 54
    3.3.1     Bus 54
    3.3.2     Rail (Amtrak)  56
    3.3.3     Air  57
    3.3.4     Auto  59
    3.3.5     Airport Access Trips  64
4.0    

Brightline Travel Demand Model

  66
  4.1    

Overview of Methods

 66

 

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  4.2    

Survey Design

 66
    4.2.1     Screening 66
    4.2.2     Reference Trip 67
    4.2.3     Choice Exercise 67
    4.2.4     Induced Travel 69
    4.2.5     Socioeconomic Characteristics 69
  4.3    

Survey Implementation and Summary

 69
    4.3.1     Survey Implementation and Summary for Brightline Phase I Study 69
    4.3.2     Survey Implementation and Summary for Brightline Phase II Study 72
  4.4    

Mode Choice Model Estimation

 75
    4.4.1     Conceptual Overview 75
    4.4.2     Summary of Model Estimation Process (Phase I Study) 77
    4.4.3     Summary of Model Estimation Process (Phase II Study) 80
  4.5    

Travel Demand Model Development

 80
    4.5.1     Level of Service Assumptions 81
    4.5.2     Model Calibration 91
    4.5.3     Induced Ridership 92
5.0    

Brightline Ridership and Revenue Forecast

 94
  5.1    

Overall Level of Ridership and Revenue

 94
    5.1.1     Ramp-Up 96
    5.1.2     Methodological Overview 96
  5.2    

Fare Revenue Estimation

 97
  5.3    

Network Model Ridership & Revenue Forecasts

 98
    5.3.1     Market Capture and Compatibility with Existing Modes of Travel 98
  5.4    

Overall Forecast Summary

 103
    5.4.1     Forecast Growth Comparison 103
  5.5    

Segment Loading and Boardings & Alightings

 103
  5.6    

Phase II Study Lakeland Station Impacts

 105
6.0    

Forecast Sensitivity

 106
  6.1    

Brightline Travel Time

 106
  6.2    

Brightline Frequency

  106
  6.3    

Intercity Travel Time by Auto

 107
  6.4    

Auto Fuel Prices

  107
  6.5    

Air Fares

 107
7.0    

Conclusion

 108

 

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Appendix A Population Density 109
Appendix B Employment Density 114
   
List of Figures and Tables
   
Figure ES-1 The Study Area of Phase I and Phase II of the Study 7
Figure ES-2 Brightline Annual Ridership Forecast, 2018-2040 16
Figure ES-3 Brightline Fare Revenue Forecast, 2018-2040 (2016 $) 17
Figure ES-4 Brightline Long-Distance Market Share, 2023 17
Figure ES-5 Brightline Short-Distance Market Share, 2023 18
Figure 1-1 Proposed Route and Stations 19
Figure 2-1 Study Area 21
Figure 2-2 Study Area Population Density 22
Figure 2-3 POPULATION, 1975-2015 (IN THOUSANDS) 23
Figure 2-4 COMPOUND ANNUAL GROWTH RATE IN POPULATION 24
Figure 2-5 MPO POPULATION FORECAST BY COUNTY, 2010-2040 (IN THOUSANDS) 25
Figure 2-6 SOUTHEAST FLORIDA POPULATION FORECAST 26
Figure 2-7 CENTRAL FLORIDA POPULATION FORECAST 26
Figure 2-8 LAKELAND POPULATION FORECAST 27
Figure 2-9 LAKELAND POPULATION FORECAST 27
Figure 2-10 Study Area Employment Density 28
Figure 2-11 Employment 1975-2015 (in Thousands) 29
Figure 2-12 Employment Forecasts by Region 2010-2040 (Thousands) 30
Figure 2-13 REAL PER CAPITA PERSONAL INCOME, 1975-2015 (2017 DOLLARS) 31
Figure 2-14 HISTORICAL TRENDS IN FLORIDA VISITATION: 2006-2015 32
Figure 2-15 TRAVEL RELATED EMPLOYMENT IN FLORIDA 33
Figure 2-16 ANNUAL VISITATION BY THEME PARK 33
Figure 3-1 Travel Time to Nearest Brightline Station 37
Figure 3-2 Market Catchment Areas 38
Figure 3-3 Historical Growth in Intercity Bus Travel in the United States 40
Figure 3-4 Amtrak Florida Station Boardings 41
Figure 3-5 Tri-Rail System Map 43
Figure 3-6 Tri-Rail System Ridership 44
Figure 3-7 Annual Air Traffic Volume between Central and Southeast Florida 45
Figure 3-8 Comparison of Air Traffic Volumes 46
Figure 3-9 Florida Turnpike Central-to-Southeast Florida Historical Traffic Counts 48
Figure 3-10 I-95 Central-to-Southeast Florida Historical Traffic Counts 48
Figure 3-11 Southeast Florida Historical Traffic Counts 50
Figure 3-12 Southeast Florida Historical Traffic Counts 50
Figure 3-13 Florida Turnpike Traffic Model Performance (Actual Vs. Fitted – Monthly) 53
Figure 3-14 Econometric Analysis and Projection of Florida Turnpike Traffic (Monthly) 53
Table 3-13 Estimated Daily Intercity Long-Distance Bus Person Trips 54
Figure 3-15 Ridership Benchmark From RSG 55
Table 3-14 Projected Daily Intercity Bus Person Trips 55
Figure 3-16 Amtrak Florida Station Boardings (Thousands) 56
Table 3-15 Historical and Future Amtrak Passenger Volumes 56

 

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Figure 3-17 Annual Air Traffic Volume between Tampa and Southeast Florida 57
Table 3-16 Annual Air Passenger Volumes, FAA 10% Ticket Sample 57
Figure 3-18 FAA’s Terminal Area Forecasts For Airport Enplanements In Study Area 58
Figure 3-19 Passenger Volume Share Of Enplanement In Origin And Destination Airport 59
Figure 3-20 Prediction Of Future Passenger Volume Between City Pairs 59
Figure 3-21 Counter stations identified for historical traffic volume 61
Figure 3-22 Historical AADT Trend At Counter Stations 62
Table 3-17 Summary Of Historical Aadt And Cagr 62
Table 3-18 Estimated Daily Auto Person Trips By Time Of Day 63
Table 3-19 Annual Auto Person Trips Projection 64
Table 3-20 Current And Future Airport Access Choice Trips By Mode 65
Figure 4-1 Example Stated Choice Exercise 68
Figure 4-2 LONG DISTANCE MODE CHOICE MODEL NESTED LOGIT STRUCTURE 77
Figure 4-3 SHORT DISTANCE MODE CHOICE MODEL NESTED LOGIT STRUCTURE 79
Figure 5-1 Brightline Annual Ridership Forecast – Base Case 95
Figure 5-2 Brightline Annual Revenue Forecast – Base Case 95
Figure 5-3 Comparison of Brightline Fares to Amtrak Northeast Corridor Fare Rates 98
Figure 5-4 Long-Distance Travel Network Model Market Shares, 2023 99
Figure 5-5 Share of Brightline Ridership by Source (Long-Distance) 100
Figure 5-6 Brightline Ridership Market Draw by Source (Long-Distance) 100
Figure 5-7 Share of Brightline Ridership by Source (Short-Distance), 2023 101
Figure 5-8 Share of Brightline Ridership by Source (Short-Distance) 102
Figure 5-9 Brightline Ridership Market Draw by Source (Short-Distance) 102
Figure 5-10 Comparison of Brightline Forecast Growth Rates 103
   
Table ES-1 Brightline Ridership and Revenue Forecast, 2023 (2016 $) 16
Table ES-2 Sensitivity Test Results, Ridership and Revenue % Change, 2023 18
Table 3-1 Estimated Daily Intercity Long-Distance Bus Person Trips 39
Table 3-2 Estimated Daily Short-Distance Bus Person Trips 39
Table 3-3 Projected Daily Intercity Long-Distance Bus Person Trips 40
Table 3-4 Projected Daily Short-Distance Bus Person Trips 40
Table 3-5 Historical and Future Amtrak Passenger Volumes 42
Table 3-6 Current and Projected Daily Short-Distance Rail Trips 44
Table 3-7 Annual Air Passenger Volumes, FAA 10% Ticket Sample 46
Table 3-8 Estimates of Air Passengers in the Brightline Service Travel Market 47
Table 3-9 Estimated Daily Long-Distance Auto Person Trips 51
Table 3-10 Estimated Daily Short-Distance Auto Person Trips 51
Table 3-11 Long-Distance City Pair O-D Shares 52
Table 3-12 Short-Distance City Pair O-D Shares 52
Table 5-1 2023 Ridership & Revenue 94
Table 5-2 Ramp-up Comparisons 96
Table 5-3 Average Fares (Smart Class), 2016 $ 97
Table 5-4 Average Fares (Select Class), 2016 $ 97
Table 5-5 Long-Distance Travel Network Model Market Shares by City Pair, 2023 99
Table 5-6 Short-Distance Travel Network Model Market Shares by City Pair, 2023 101
Table 5-7 Forecast Brightline – Annual Segment Volumes and Revenues (2016 $) 104
Table 5-8 Brightline Daily Boardings and Alightings, 2023 104

 

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Table 5-9 Brightline Daily Boardings and Alightings, 2030 105
Table 5-10 Lakeland Station Ridership and Revenue Impacts 105
Table 6-1 Sensitivity Test Results, Ridership and Revenue % Change, 2023 106

 

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Executive Summary

 

All Aboard Florida Operations, LLC commissioned Louis Berger U.S., Inc. (LB) to develop an investment grade ridership and revenue forecast study for the re-introduction of passenger rail service on its existing right of way. The proposed new passenger rail service, named Brightline, will be a privately owned and operated, intercity service connecting key cities in Southeast Florida with key cities in Central Florida and Tampa Area. This study was conducted in two distinct phases, the Phase I Study evaluated Brightline service between Southeast Florida and Orlando as indicated in Figure ES-1, while the Phase II Study evaluated the incremental ridership and revenue associated with an extension of service to Tampa with an intermediate stop at Disney World.

 

Figure ES-1 The Study Areaof Phase I and Phase II of the Study

 

 

The objective of this study is to provide an independent overview of ridership and revenue that will inform and advance the project planning efforts and decisions of potential investors and funding partners.

 

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ES-1 Overview of the Investment Grade Study Process

 

The ridership and fare revenue forecasts presented in this report are characterized as being investment-grade in that they provide a level of information in data gathering and analysis that is typical of that required to support investment decision-making.1 The comprehensive nature of this study is underpinned by the following key features:

 

● Independent approach by experienced travel demand forecasting consultants.

 

● Forecasting models constructed from the bottom up using data gathered from regional planning agencies, stakeholder organizations, and recognized commercial sources.

 

● Stated Preference Survey designed to measure characteristics of existing intercity travel demand.

 

● Pricing Research survey data to support findings on willingness to pay and induced demand

 

● A critical, benchmarked assessment of economic growth projections that are used to estimate the overall future growth in travel demand.

 

● The development of a forecasting models for Brightline based on current travel, transport system and economic growth data.

 

● Alternative model estimates (sensitivity testing) intended to quantify the impacts of different assumptions of key forecasting inputs on forecast results.

 

● Emphasis on near term forecasts—investment decision makers commonly place greater emphasis on the early years of operation than the later years (which include growth that is expected, but not certain, to occur).

 

Outputs of the forecast that were used to determine the economic, financial, and business planning dimensions of the proposed investment include the following:

 

● Overall ridership demand estimates

 

● Station-station segment ridership estimates

 

● Market share analysis

 

● Market breakdown by user type (business/non-business, etc.) and geography

 

● Ridership demand with respect to level of service

 

● User benefit metrics (values-of-time)

 

Louis Berger segmented its technical approach and analysis into five distinct areas of study outlined below. Each of these study areas are discussed in greater detail within their respective chapters of this report.

 

● Market assessment (Section 2, 3)

 

● Travel demand model development and calibration (Section 4)

 

● Ridership and revenue forecast (Section 5)

 

● Sensitivity testing (Section 6)

 

1 The key features noted in this section are intended to produce reasonable and reliable forecast estimates. However, it is not possible to forecast future events with certainty. Assumptions regarding economic growth, competition between modes, and external factors affecting overall travel demand and Brightline usage may prove inaccurate. Changes from these assumptions could produce lower or higher ridership than the estimates contained in this report. Please see our disclaimer for more information.

 

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ES-2 Study Process

 

To determine the extent and magnitude of the demand for a new mode of travel between Central Florida, Tampa Area and Southeast Florida, Louis Berger undertook a thorough assessment of the existing and potential future intercity travel market, the attributes of the current modes of travel in the corridor, and prospects for future growth. The study included the following key activities.

 

● Research to Establish Market Size and Catchment Area– Residents and visitors to cities in the corridor make hundreds of millions of trips per year, but only a select portion of these trips involve travel between the central business districts and surrounding activity centers that would be served by Brightline stations. To identify the addressable market, Louis Berger gathered extensive data on current levels of travel between the city pairs by mode, trip purpose, and time (time of day, day of week). Louis Berger used vendor-provided mobile phone data and findings from recent primary research on traveler preferences to determine the size of the market. The research established an addressable market of over 413 million intercity trips per year in areas reasonably served by the stations (see Section 3). These findings on the size and characteristics of the market are consistent with previous studies undertaken for rail projects in Florida, and provide a conservative base for the demand forecast.

 

● Identification of Travel Network and Competing Modes of Travel – The demand forecasting process also requires a thorough understanding of the travel network and the schedule, journey time, and cost attributes of all modes of travel using the network. This report outlines the assumptions and data sources Louis Berger used to establish the highway, rail, and air travel network. The report also documents the attributes of each mode of travel used as inputs to the demand forecast (see Section 4).

 

● Assessment of the Prospect for Growth in Travel – An investment grade forecast requires thorough examination of the prospect for growth in the overall travel market. By gathering data from regional transportation planning agencies and other accepted public and commercial sources, Louis Berger established conservative and reasonable growth rates for the overall market based on observed trends in each segment. Based on observed trends in each of the metropolitan regions within the corridor, Louis Berger expects the overall number of long-distance intercity trips between Central and Southeast Florida to grow by 2.9 percent per year; and between Tampa Area and Southeast Florida to grow by 2.2 percent per year. Louis Berger expects the overall number of shorter-distance trips between the cities in Southeast Florida to grow by 1.2 percent per year and between Central Florida and Tampa Area to grow by 2.2 percent per year (see Section 3).

 

● Primary Research on Traveler Preferences and Willingness to Pay – When travelers choose to make a journey by auto or by rail they weigh the time and money cost of travel and make a choice based in part on their travel budget and willingness to pay. Travel behavior is also influenced by trip purpose (e.g., business, leisure, commute, airport access) and other factors such as party size and need for a vehicle at the destination. The Brightline system is an entirely new type of service for the region whose unique features can only be tested in hypothetical scenarios that place Brightline against other competing modes. The current state-of-the-practice uses mode choice Stated Preference survey (SP) as the basis for understanding how individuals (or groups of individuals) value individual attributes, such as access time, in-vehicle travel time, headways, and cost - of a transportation choice (see Section 4 for description of the survey, administration, and analysis). Louis Berger also reviewed findings from a recent Pricing

 

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Research survey conducted by Integrated Insights to benchmark data on traveler trip purpose, travel frequency, and willingness to pay (see Section 5).

 

● Demand Forecasting – The Louis Berger study team employed best practices in discrete choice analysis and network travel demand forecasting to determine diversions from existing modes of travel to Brightline and ridership volumes on the Brightline system by city-pair segment. SP survey responses were used to develop a statistical model of mode choice and estimates of the passenger rail market share and is the basis of the Brightline ridership forecast (see Section 4).

 

● Sensitivity Testing– The report provides the findings of sensitivity tests demonstrating the effect of changes in key forecast assumptions on ridership and revenue (see Section 6). These sensitivity tests are used to establish the stability of the forecast model and inform project planning.

 

This study was carried out in the context of previous public and private sector sponsored rail implementation studies in Florida that attempted to better understand the potential of passenger rail to relieve congestion and promote mobility and economic development. Louis Berger evaluated the following studies and used them as benchmarking references for the findings in this analysis:

 

● Florida Overland Express (FOX): Public-private partnership between FDOT and FOX for high speed rail connecting Tampa, Orlando, and Miami. The State withdrew support for the project, in 1999.

 

● Investment Grade Ridership Study for the Tampa-Orlando corridor: Performed in 2002 on behalf of the Florida High Speed Rail Authority. The Florida High Speed Rail Enterprise published a two-page update to that forecast in September 2009.

 

● In 2006, FDOT prepared the Florida Intercity Passenger Rail Vision Plan, a plan that builds upon previous studies exploring the potential of high speed rail to assist in meeting the State’s mobility needs.

 

ES-3 Overview of the Brightline Rail Service

 

The Phase I of proposed Brightline service will provide new express passenger rail service connecting Orlando with three key urban areas in Southeast Florida – West Palm Beach, Fort Lauderdale, and Miami. The Phase II of proposed Brightline service will provide expanded service beyond Orlando to Lakeland and Tampa Area, with additional stops at Disney World and Meadow Woods. The Brightline service will be privately owned and operated by All Aboard Florida Operations, LLC and will primarily run along the existing transportation corridors including a rail corridor currently used for freight rail operations by Florida East Coast Railway, and an existing highway corridor being accessed in partnership with the State of Florida and other regional governmental entities. Brightline services are unique for Florida: no intercity rail alternative comparable to the proposed Brightline service exists currently. Special features include the following:

 

● Travel time savings: Substantial time savings to current users of auto, bus, traditional rail and even air traveling between the city pairs

 

● Frequency: Consistent, hourly departures seven days per week to fit the schedules of both business and leisure travelers

 

● Booking: Online and mobile booking with reserved coach and business class seating for easy boarding

 

● Amenities: Free Wi-Fi, convenient outlets, comfortable seating, food and beverage service and related amenities on board

 

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● Stations: Modern, centrally located stations in Southeast Florida and Tampa Area cities, and an airport-based station in Orlando, with good intermodal connectivity (i.e. connections to Metrorail, Metromover, Tri-Rail – with direct connection to Miami International Airport – Broward County Transit, The WAVE Streetcar, and SunRail), parking and ridesharing services available

 

In addition to the travel time savings offered by Brightline, the ease of travel and related amenities to the service described above are expected to draw a substantial amount of travelers who attribute a high value to comfort, productivity, and efficiency.

 

ES-4 Relevant Market for High Speed Rail

 

With a population of 6.01 million in 2015, the South Florida metropolitan area is the most populous metropolitan area in the Southeastern United States and the fourth most populous urbanized area in the United States. Main cities include Miami, Fort Lauderdale, Pompano Beach, West Palm Beach, and Boca Raton. Miami International Airport is the busiest airport in Florida (38.6 million passengers in 2015) and ranks second in the United States in terms of international passenger count, with 21.2 million international passengers annually. Central Florida’s main city, Orlando, and the surrounding Greater Orlando region attracted 68 million visitors in 2016. Attractions include Walt Disney World Resort, Universal Orlando Resort and SeaWorld Parks & Entertainment. Convention and trade show attendance at the Orange County Convention Center, in 2015 equaled 1.4 million. In addition, a record 22.6 million people visited Tampa Bay Area; Orlando and Miami are among top three visitor sources, leading a high demand for travelling between Central Florida and Tampa Area and between Southeast Florida and Tampa.

 

Orlando International Airport, a station location, is the second busiest airport in Florida after Miami International Airport with 41.9 million passengers in 2016. Orlando’s secondary airport, Orlando Sanford International Airport had 2.75 million passengers in 2016 while cruise traffic at Port Canaveral accounted for 3.9 million passengers. A total of 18.7 percent of overseas non-resident travelers enter the United States through one of the main South Florida and Central Florida airports: Miami International Airport (12.4 percent); Orlando International Airport (4.1 percent) and Fort Lauderdale International Airport (2.2 percent).

 

Auto vehicles are the dominant mode of intercity travel between Orlando and the Southeast Florida cities that Brightline will serve. The two main routes between the cities are the I-95 and the Florida Turnpike. Free-flow driving times between Miami and Orlando are estimated at approximately 4 hour 15 minutes along the I-95 and at 3 hour 50 minutes along the Florida Turnpike, which is a toll road. Travel times during congested peak periods can be substantially greater. Air, rail and bus account for a small proportion of trips between the Orlando and Miami; most passengers traveling by air on the more than thirty daily flights between Miami and Orlando are connecting to another destination. Two AMTRAK trains, the Silver Meteor and the Silver Star, each run once daily between Orlando and Southeast Florida. The Silver Meteor, which is the fastest because it does not make a detour to Tampa, takes about 3 hour 45 minutes from Orlando to West Palm Beach and 5 hours 35 minutes from Orlando to Miami. In addition, there are a few private bus companies that operate several buses daily between Orlando and Southeast Florida along the Florida Turnpike. Similarly, auto vehicles play a major role in intercity travel between Tampa Area and the Southeast Florida cities. The major route connecting these two areas is I-75, via which the free-flow driving time is

 

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estimated at approximately 4 hours. Air, rail and bus together only account for 10 percent of total trips between these two areas.

 

Travel between Central Florida and Tampa Area is almost exclusively by auto, accounting for 99 percent of total trips. The main alternative mode of travel is bus: private bus companies provide several daily commuting bus service connecting Orlando and Tampa, with travel time up to 70 minutes. Travel within Southeast Florida is also mostly by automobile. Between Miami and West Palm Beach the Florida Turnpike runs parallel with I-95. Driving from Miami to West Palm Beach takes about 1 hour 17 minutes on the I-95 and 1 hour 27 minutes on the Turnpike. Driving time between Miami and Fort Lauderdale is about 35 minutes while the drive from Fort Lauderdale to West Palm Beach takes about 50 minutes. During congested peak periods it is not uncommon for these travel times to increase by 30 to 50 percent due to incidents or weather making journey and arrival times during these key periods unreliable. The main alternative mode of transportation is rail. Tri Rail, a commuter rail line run by the South Florida Regional Transportation Authority (SFRTA) links Miami, Fort Lauderdale, and West Palm Beach. The 71-mile line has 18 stops and an annual ridership of 4.2 million.

 

According to the 2016 INRIX Global Traffic Scorecard, the Central and South Florida highways are the most congested in the State, which results in millions of hours of travel delay and excessive fuel consumption and pollutant emissions. Southeast Florida is ranked as the 10th most congested urban area globally in terms of peak hours spent in congestion and has the 5th worst traffic congestion in the United States2. State and local agencies have been active in evaluating alternatives to the severe congestion on north-south roadway links. In June 2010, FDOT prepared the I-95 Transportation Alternatives Study, in consultation with the Department of Law Enforcement, the Department of Environmental Protection, the Division of Emergency Management, the Office of Tourism, Trade and Economic Development and affected MPOs and regional planning councils located along the corridor. The study, which provides an assessment of concerns and proposed solutions related to I-95, found that “I-95 is overwhelmed with traffic demand” and that “[t]ravel within specific urban areas along the I-95 corridor is highly congested in peak travel periods due to single driver automobile use.” This study concluded, among other things, that “[p]assenger rail service presents a mobility option to serve Florida’s East Coast along the I-95 corridor” with multiple benefits including the reduction of “fossil fuel use and greenhouse gases (GHGs); job creation and economic development around station locations; and, better connectivity between northern and southern sections of Florida.”

 

The potential for intercity rail as a viable alternative has long been recognized by many, including FDOT, which developed the Florida Intercity Passenger Rail “Vision Plan” (FDOT, August 2006). Among other things, the plan found that the state’s intercity travel market would grow at an average annual rate of 3.5% from 2006 to 2040 (FDOT, August 2006). This increase will exacerbate existing transportation problems and require significant development of new infrastructure to meet the needs of this market. In June 2009, FDOT released the 2009 Florida Rail System Plan: Policy Element (FDOT, March 2009), which updated the 2006 Florida Freight and Passenger Rail Plan and built upon previous rail planning efforts, including the 2006 Florida Intercity Passenger Rail Vision Plan to show that:

 

● There is a rising public interest in rail options to meet intercity and regional mobility needs;

 

2http://inrix.com/press-releases/los-angeles-tops-inrix-global-congestion-ranking/

 

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● The existing congestion on Florida’s highways may be mitigated by a passenger rail alternative, which would also serve to increase the mobility of tourists, business travelers, and citizens – especially older Floridians; and

 

● Reliance on alternate transit options is expected to increase in light of growing concerns over dependence on foreign oil, fluctuating gas prices, and fuel supply disruptions as a result of natural disasters.

 

ES-5 Key Assumptions

 

In order to provide the level of information appropriate for evaluation by lenders and investors during the planning stage of project development, Louis Berger made several key assumptions for the Base Case Forecast, as follows:

 

● The forecast study area is limited to the extent of the metropolitan areas in Central and Southeast Florida and Tampa Area. Station market catchment areas and trip filters were developed to establish reasonable boundaries for the addressable market and to eliminate illogical station access patterns. As described in Section 3, this is the basis for establishing the size of the candidate market at over 31 million trips per year for the long-distance journey between Orlando and the three cities in Southeast Florida and between Tampa Area and Southeast Florida. When trips between the three cities in Southeast Florida and trips between Tampa Area and Central Florida are considered along with long distance trips, the number grows to over 413 million.

 

● Base year trip tables used in the model were developed separately for each mode available between each city pair. As noted in Section 3.6, Louis Berger relied on mobile phone location data purchased from a third-party vendor to determine origin-destination patterns and the size of the automobile trip travel market, which is predominant in size within the addressable market geography. This data was calibrated to volumes on the Florida Turnpike and other intercity routes and verified through an econometric model that linked economic variables to traffic growth on the Florida Turnpike and other intercity routes (that basis of the data being a traffic station counter representative of the Central-Southeast Florida traffic). The data was further adjusted downward to capture captive auto users and lower visitation volumes. Trips tables for other modes of travel were based on information obtained from relevant planning agencies and operators.

 

● Brightline fares assumed in the modeling process were provided by All Aboard Florida Operations, LLC and validated by Louis Berger, as outlined in Section 5.2. All fares and competing mode costs were fixed in real terms. For purposes of estimating the future cost of auto travel, gas prices were set at future levels estimated by the U.S. Energy Information Administration (EIA) reference case forecasts referenced in Section 4.5. This report assumes that over the long term, motor fuel will remain in adequate supply and future increases in fuel price will align with the current outlook of these EIA forecasts. In addition, future changes in fares for Brightline, made during the course of operations or otherwise, that differ from those outlined in this Report, may result in levels of ridership and/or revenue that differ from the forecast estimates contained in this Report. Costs associated with competing modes of intercity travel or modes access/egress to Brightline stations different from those assumed in this Report may result in levels of ridership and/or revenue that differ from the forecast estimates contained in this Report.

 

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● The estimation of the future travel market does not include any changes in the location of households or employment related to transit-oriented development in the areas surrounding the stations.

 

● We assume that the trajectory of growth in trips will align with the information and estimates presented in Section 3.

 

● Congested auto travel times were accounted for in estimating station access and long-distance auto travel times, as summarized in Section 4.5. Given the history of growth in highway congestion and challenges in expanding the highway network, regional planners consider it likely that congestion within and between the regions will increase, making non-highway modes of travel more competitive. Changes in highway capacity or capacity or service levels associated with competing modes of intercity travel or modes access/egress to Brightline stations different from those assumed in this Report may result in levels of ridership and/or revenue that differ from the forecast estimates contained in this Report.

 

● Brightline presents users with a premium service unlike any other service in the State of Florida. It is often the case that Stated Preference surveys which underlie the mode choice model and forecast do not fully capture the value that users attribute to the premium nature of services such as Brightline. Our survey research and fare price benchmarking was designed to compensate for this providing the basis for a comprehensive view on traveler willingness to pay.

 

● Induced demand potential was based on a method of evaluating the improvement in the generalized cost of travel that has been accepted in other studies for high speed transportation in the U.S. As a novel form of transportation in Florida, Brightline is likely to experience ridership demand for tourism and leisure travel based on its convenience and amenities.

 

● This report assumes no major recession or significant economic restructuring will occur which could substantially reduce trip-making and traffic in the region.

 

● This report assumes no natural disasters will occur that could significantly alter travel patterns throughout the area served by Brightline.

 

● This report assumes no local, regional, or national emergency will arise which would abnormally restrict Brightline service, use of Brightline stations or connectivity to stations, or the use of motor vehicles, or other modes of travel, for station access and egress.

 

Any significant departure from these basic assumptions could materially affect estimated Brightline ridership and revenue. Assumptions regarding economic growth, competition between modes and external factors affecting overall travel demand and Brightline usage are subject to uncertainty and may prove inaccurate. As noted herein, Louis Berger has relied on information developed by third parties regarding travel patterns and the outlook for economic conditions. Changes from these assumptions could produce lower or higher ridership than the estimates contained in this report. Please see our disclaimer for more information.

 

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ES-6 Key Findings and Ridership and Revenue Forecast

 

Our forecast evaluation revealed that introduction of Brightline service would complement existing modes of travel and draw a substantial number of business and non-business travelers. Station locations offered by Brightline in Miami, Ft. Lauderdale, West Palm Beach, Orlando (International Airport & Disney), and Tampa will provide an alternative source of transportation for travelers with origins or destinations at or near these urban cores. The thorough study effort resulted in the following key findings:

 

● Substantial “Addressable Market” – Hundreds of millions of trips are taken annually between the four cities that will be served by Brightline. Louis Berger’s study included a determination of the portion of these total trips that both originate and terminate within a defined distance of a proposed Brightline station (a station “catchment area”). The Brightline addressable market is assumed to include only those trips beginning and ending within station catchment areas, as further defined in 2.10 of this report. Based upon detailed analysis, Louis Berger concluded that the addressable market for Brightline intercity service amounts to over 413 million trips made by individuals annually.

 

● Demonstrated Market Travel Growth – Intercity travel on the Florida Turnpike between Orlando and Miami grew by an average of 3.2 percent per year from 2001 to 2016. Average annual growth on I-95 from 2001 to 2015 was approximately 2.1 percent.

 

● Demonstrated Market Demographic Growth – In the past 30 years, population in the market area has grown by an annual average of 2.5 percent and employment has grown by an annual average of 3 percent. Within one mile of proposed Brightline stations, annual population growth has ranged from 2 percent to 5 percent since 1990 indicating strong growth in the urban core at the heart of the Brightline alignment.

 

● No Comparable Service – Brightline can provide travel time savings of 25% to 50% when compared to existing surface modes (auto, bus and rail) and with a journey time of around three hours from Orlando to Miami is competitive with air on door-to-door travel times. There is no comparable service to Brightline for intercity travel in the existing market.

 

● Established Willingness to Pay – The fares used in this study are backed up by two primary research efforts – a Stated Preference Survey and a Pricing Research Study commissioned by the project sponsor – which confirmed willingness to pay for the Brightline service at the price points utilized. Fares are highly competitive with existing modes of travel when time, tolls, and travel costs are considered and are comparable to other successful rail services in the U.S.

 

● Long-Standing Interest – Given the profile of the travel market and the central location of the rail line, there has been interest among stakeholders and the public in developing passenger service on the Florida East Coast corridor for decades.

 

Estimated Ridership

Louis Berger prepared estimates for annual ridership and farebox revenue for both the short- and long-distance markets of the Brightline service. This forecast accounts for all elements important to future ridership potential including targeted market segments and induced ridership. Table ES-1 summarizes ridership and revenue for 2023, the first year stabilized ridership for the entire system is achieved.

  

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Table ES-1 Brightline Ridership and Revenue Forecast, 2023 (2016 $)

  Phase I Study (Miami-Orlando) Phase II Study (Extension to Tampa) Grand Total
Short-Distance Long-Distance Subtotal Short-Distance Long-Distance Subtotal
Ridership 3,079,472 3,534,197 6,613,669 1,967,353 935,992 2,903,345 9,517,014
Fare Revenue $100,763,367 $298,773,216 $399,536,583 $34,053,816 $145,891,900 $179,945,716 $579,482,299
(1) Short-distance (Phase I Study): Miami-Ft. Lauderdale, Miami-West Palm Beach (WBP), Ft. Lauderdale – WPB

(2) Long-distance (Phase I Study): Southeast Florida-Orlando

(3) Short-distance (Phase II Study): Disney-Orlando Airport, Tampa-Orlando

(4) Long-distance (Phase II Study): Southeast Florida -Tampa

 

Ridership and revenue for the initial years of Brightline is expected to start at relatively low levels and grow to a stabilized volume after two years for each segment of operations that is introduced. The low initial levels represent the time it takes for ridership to build up to long-term forecast levels as travelers become acquainted with the new rail service and adjust their trip-making habits. During 2017, management has made substantial investment in marketing, pre-launch ticket sales, and corporate block sales prior to the commencement of full-scale revenue service between Miami and West Palm Beach in May 2018. Management also intends to implement reduced price fares during an introductory period following the beginning of revenue service for each segment (see discussion in Section 5.2). Given these plans, for the short-distance trips, Louis Berger assumed, therefore, 40 percent of forecasted volumes in 2018, and 80 percent forecasted volumes in 2019. For the long-distance trips, Louis Berger assumed a two calendar year ramp-up period: ridership volumes for 2021 are 40 percent of forecasted volumes and 80 percent of forecasted volumes in 2022. These ramp-up assumptions are appropriate to estimation of initial year ridership and revenue, and are consistent with previous rail service forecasts in Florida (see discussion in Section 5.1.1 – Ramp-Up). The forecasts include the assumption that long-distance revenue service will begin in first quarter of 2021. The full service will reach stabilized volumes by 2023. Ridership and revenue for the full forecast length is summarized in the two figures below. The values for 2018-2023 account for ramp-up reductions.

 

Figure ES-2 Brightline Annual Ridership Forecast, 2018-2040

 

 

Source: Louis Berger, 2017, 2018

 

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Figure ES-3 Brightline Fare Revenue Forecast, 2018-2040 (2016 $)

 

 

Source: Louis Berger, 2018, 2018

 

Estimated Market Share

 

The forecast indicates that after the initial ramp up period, Brightline will serve approximately 10 percent of the overall market for travel between Southeast Florida and Orlando and approximately 15 percent of the market between Southeast Florida and Tampa. In the short-distance market, Brightline will serve approximately 0.74 percent of the overall market in Southeast Florida, and approximately 11 percent of the market between Tampa and Orlando (this includes the Disney to Orlando Airport travel market).

 

Figure ES-4 Brightline Long-Distance Market Share, 2023

 

 

Source: Louis Berger, 2017, 2018

 

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Figure ES-5 Brightline Short-Distance Market Share, 2023

 

 

Source: Louis Berger, 2017, 2018

 

Louis Berger conducted a series of sensitivity tests to evaluate the impact that changes in key input variables have on the ridership and revenue forecast. Table ES-2 presents the key assumptions that were altered and the corresponding impact on ridership and revenues for both the short- and long-distance market.

 

Table ES-2 Sensitivity Test Results, Ridership and Revenue % Change, 2023

Sensitivity Test Assumption Modified Change in Assumption Phase I Study Phase II Study
Short-Distance Long-Distance Short-Distance Long-Distance
Ridership Effect Revenue Effect Ridership Effect Revenue Effect Ridership Effect Revenue Effect Ridership Effect Revenue Effect
Brightline Travel Time 10% decrease 3.60% 4.00% 6.30% 6.30% 2.93% 3.75% 6.96% 7.06%
10% increase -3.50% -3.80% -5.90% -6.00% -3.47% -4.40% -7.97% -8.08%
Brightline Frequency 20% decrease -3.70% -3.90% -1.50% -1.60% -6.26% -5.41% -1.82% -1.83%
20% increase 3.80% 4.00% 1.50% 1.60% 3.89% 3.36% 1.11% 1.11%
Intercity Travel Time by Auto 20% decrease -10.40% -10.90% -11.20% -11.40% -10.95% -13.01% -11.95% -11.86%
20% increase 11.90% 12.60% 12.60% 12.70% 12.19% 14.99% 13.28% 13.16%
Auto Fuel Prices* Low: (-35% /-31%) -3.30% -3.50% -3.10% -3.10% -1.72% -2.90% -2.29% -2.30%
High: (+79% /+48%) 7.90% 8.40% 7.40% 7.50% 2.81% 4.75% 3.67% 3.68%
Air Fares 20% decrease N/A N/A -0.30% -0.30% N/A N/A -1.94% -2.12%
20% increase N/A N/A 0.10% 0.10% N/A N/A 1.67% 1.85%

 

Source: Louis Berger, 2017, 2018

 

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1.0 Introduction

 

Each year, travelers make hundreds of millions of trips between the communities in Southeast and Central Florida and Tampa Area, making the region one of the most actively traveled areas in the United States. All Aboard Florida Operations, LLC initially commissioned Louis Berger U.S., Inc. (LB) to develop an investment grade ridership and revenue forecast study for the re-introduction of passenger rail service named Brightline.

 

An initial evaluation (hereafter referred to as the Phase I study) evaluated potential Brightline ridership demand associated with an alignment that connected through Miami, Fort Lauderdale and West Palm Beach in Southeast Florida, continued north along an existing rail right-of-way before turning west linking to the Orlando International Airport as shown in Figure 1-1. The initial Phase I Study investment grade analysis effort was conducted using a customized travel demand model based in part on Stated Preference survey research data collected in both Central and Southeast Florida in 2012 and was completed in December of 2017.

 

Brightline commenced passenger rail operations in Southeast Florida in January of 2018 and plans are currently underway to extend service to Orlando with a connection at the Intermodal facility at Orlando International Airport.

 

Figure 1-1 Proposed Route and Stations

 

  

Following the completion of the Phase I Study and the commencement revenue passenger service, Louis Berger was once again commissioned to evaluate the incremental ridership potential of extending service to Tampa with an intermediate stop at Disney World and the consideration of some potential alternative station locations along the

 

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Interstate 4 (I-4) corridor as also indicated in Figure 1-1.This follow up study (hereafter referred to as the Phase II Study) was conducted using a separate travel demand model based on primary market research data collected in 2018 that focused on both the Tampa-Orlando and Tampa/Lakeland-Southeast Florida travel corridors.

 

1.1 Organization of Report

 

Due to the differences in the travel demand models and the time frames of both the Phase I and Phase II Studies, this report presents elements of the analysis with appropriate distinctions of the two efforts. Furthermore, Louis Berger views the estimated Phase II Study ridership and revenue demand as incremental to the Phase I Study estimates.

 

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2.0 Travel Market Socioeconomic and Demographic Conditions

 

Despite the distances between city centers, the communities and economies of Southeast Florida, Central Florida, Lakeland and Tampa are interconnected in many ways. Substantial numbers of people travel between these areas for work, business, recreation, and other purposes. This section of the report describes the underlying socioeconomic and demographic characteristics of the region as they pertain to the overall intercity travel market and an evaluation of prospects for growth. Figure 2-1 presents the travel market study area that is segmented into four regions:

● Southeast Florida
● Central Florida (Orlando area)
● Lakeland (Polk County)
● Tampa

 

Figure 2-1 Study Area

 

 

Source: Louis Berger. 2018

 

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2.1 Population

 

The study area consists of three major metropolitan regions with a total population of 11 million in 2017. Over 6 million people live in Southeast Florida, making it the seventh ranked urbanized area in the nation (behind New York, Los Angeles, Chicago, Dallas, Houston and Washington) and the most populous metropolitan area in the Southeastern U.S. The Tampa Area counted 2.7 million residents in 2017 and is the third ranked urbanized area in the Southeastern U.S. The Central Florida region counted 2.1 million residents in 2017, making it the fourth most populous metropolitan area in the Southeastern U.S. The Figure 2-2 presents the overview of population density in the study area. Figures of population density at a more granular level are included in Appendix A

 

Figure 2-2 Study Area Population Density

 

 

Source: Louis Berger, 2018

 

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The Central region experienced an average of 2.0 percent growth per year in the 2005-2015 period, 0.9 percent and 1 percent higher than the population growth rate of Southeast Florida and Tampa Area over the same period, respectively (Table 2-1).

 

Figure 2-3 POPULATION, 1975-2015 (IN THOUSANDS)

 

 

Source: Louis Berger, 2018 from data provided by Woods & Poole Economics, 2017

 

Table 2–1 Compound Annual Growth Rate in Population

 

Region/County/Area
1975-
2015
1995-
2015
2005-
2015
2010-
2015
Southeast Florida
1.9%
1.4%
1.1%
1.5%
West Palm Beach
2.0%
1.4%
0.8%
1.6%
Broward
1.5%
1.3%
1.2%
1.4%
Miami-Dade
2.8%
1.7%
1.1%
1.4%
Central Florida
3.1%
2.5%
2.0%
2.3%
Orange
2.8%
2.5%
2.0%
2.3%
Osceola
5.4%
4.3%
3.6%
3.7%
Seminole
3.0%
1.5%
1.0%
1.2%
Lakeland Area (Polk County)
2.1%
1.9%
1.7%
1.5%
Tampa Area
1.5%
1.2%
1.0%
1.3%
Hillsborough
2.1%
2.0%
1.7%
1.8%
Pinellas
0.9%
0.3%
0.2%
0.7%
Total Study Area
2.0%
1.6%
1.3%
1.6%

 

Source: Louis Berger, 2017 from data provided by Woods & Poole Economics, 2017

 

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Figure 2-4 COMPOUND ANNUAL GROWTH RATE IN POPULATION

 

 

Source: Louis Berger, 2018

 

Population growth in the study area as a whole has had an average annual gain of 2.0 percent since 1975 (see Table 2-1). In the past 20 years the growth rate has moderated to 1.6 percent.

 

2.2 Population Forecasts

 

In Southeast Florida, three MPO regularly update their long-range transportation plans for the three county urbanized areas. These plans include updates to the outlook on the socioeconomic factors that underpin travel demand. These plans include official forecasts for population through 2040 from a base year of 2010. In Central Florida, MetroPlan Orlando is the MPO for Orange County, Osceola County, and Seminole County. The MPO’s 2040 Long Range Transportation Plan includes population projections for 2040. Similarly, Polk County TPO provides population projections for 2040 for the county (Lakeland Area in this report); Hillsborough MPO along with Forward Pinellas (Pinellas’s MPO) and Sarasota/Manatee’s MPO provides population projections for 2040 for Tampa area. Figure 2-5 shows the levels and rates of growth expected by study areas.

 

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Figure 2-5 MPO POPULATION FORECAST BY COUNTY, 2010-2040 (IN THOUSANDS)

 

 

Source: Louis Berger, 2018 from MPOs’ 2040 Long Range Transportation Plan. Central Florida is composed of Orange, Osceola, and Seminole counties. Southeast Florida is composed of Palm Beach, Broward, Miami-Dade Counties; Lakeland Area is composed of Polk County; Tampa Area is composed of Hillsborough, Pinellas and Manatee Counties.

 

The Southeast Florida forecast calls for the region to experience more moderate levels of increase than in the past, growing at an average annual rate of 0.8 percent from the 2010 Census population count. Regional population is expected to reach 7.0 million in 2040, with over 3.3 million residents in Miami-Dade. The Central Florida MPO forecasts growth at an annual average rate of 1.4 percent from the 2010 Census Population count, reaching 2.8 million residents in the MPO by 2040. The Lakeland area MPO (Polk County MPO) forecasts growth at an annual average rate of 1.8 percent from the 2010 Census Population count, reaching 0.9 million residents in the area by 2040; The Tampa area MPOs forecast growth at an annual average rate of 0.9 percent from the 2010 Census Population count, reaching 2.8 million residents in the area by 2040.

 

In accounting for future growth in intercity trips, Louis Berger utilized the established travel demand forecasts of the MPOs. To ensure that these forecasts represent reasonable levels of growth when compared to more recent projections, Louis Berger undertook a review of alternative population forecast sources.

 

Two alternative sources were available at the county level. The Bureau of Economic and Business Research (BEBR) at the University of Florida produces population projections based on forecasts of natural increase and net migration flows. These projections are updated annually and include three different levels-low, medium and high.

 

Louis Berger also obtained projections developed by Woods & Poole Economics, Inc., a private consulting firm that maintains and annually updates county-level projections for the U.S. (CEDDS - Complete Economic and Demographic Data Source, 2017). With its detail and frequent updates, this source is often used for comparison with official estimates in demand forecasts and due diligence studies.

 

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Figure 2-6 SOUTHEAST FLORIDA POPULATION FORECAST

 

 

Source: Louis Berger, 2018

 

Figure 2-7 CENTRAL FLORIDA POPULATION FORECAST

 

 

Source: Louis Berger, 2018

 

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Figure 2-8 LAKELAND POPULATION FORECAST

 

 

Source: Louis Berger, 2018

 

Figure 2-9 LAKELAND POPULATION FORECAST

 

 

Source: Louis Berger, 2018

 

For the whole study area, BEBR “Low” case is projecting the slowest rate of growth for the region overall; The overall population level in 2040 projected by BEBR “Medium” is 6 percent higher than the MPO forecast. It is important to note that the MPO forecasts for all study areas between the “low” and “medium” case projections provided by BEBR. Woods and Poole see higher prospects for growth for the region in general than the BEBR “Low” and “Medium” case and the MPO forecasts but lower than the BEBR “high” case projection as shown in the Figure 2-6, 2-7, 2-8, and 2-9.

 

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2.3 Employment

 

The study area contains more than two-third of all employment in the state of Florida. The Southeast Florida region in particular is a major employment center in Florida, comprising one third of the state’s total employment base. Over 6.9 million people worked in the study area in 2015. Figure 2-10 depicts the employment density of study area. Figures of employment density at a more granular level were included in the Appendix B.  

 

Figure 2-10 Study Area Employment Density

Source: Louis Berger, 2018

 

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There was a slight dip in employment between 2005 and 2010 due to the recession and credit crisis; however, employment levels appear to have recovered. The Southeast Florida region has experienced substantial growth since 1975 when it had 1.27 million jobs. Employment in Central Florida totaled almost 1.4 million in 2015, up from 278,000 jobs in 1975. Employment in Tampa area totaled more than 1.4 million in 2015, up from 519,000 jobs in 1975 (Figure 2-11). 

 

Figure 2-11 Employment 1975-2015 (in Thousands)

Source: Louis Berger, 2018 from data provided by Woods & Poole Economics, 2017

 
Table 2–2 Compound Annual Growth Rate in Employment

 

Region/County/Area 1975- 1995- 2005- 2010-
  2015 2015 2015 2015
Southeast Florida 2.7% 2.3% 1.6% 3.4%
Palm Beach 3.7% 2.7% 1.3% 3.6%
Broward 3.2% 2.2% 1.3% 3.1%
Miami-Dade 2.1% 2.1% 1.9% 3.5%
Central Florida 4.1% 2.9% 1.8% 3.6%
Orange 3.8% 2.7% 1.9% 3.7%
Osceola 5.7% 4.2% 2.7% 4.5%
Seminole 4.8% 2.8% 0.8% 2.6%
Lakeland Area (Polk County) 2.0% 1.6% 0.5% 1.9%
Tampa Area 2.6% 1.6% 0.5% 2.5%
Hillsborough 2.9% 2.0% 0.9% 3.1%
Pinellas 2.2% 1.0%  -0.1%   1.7%
Total Study Area 2.9% 2.2% 1.3% 3.2%
Source: Louis Berger, 2018 from data provided by Woods & Poole Economics, 2017

 

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Employment growth in the region as a whole has averaged an annual gain of 2.9 percent since 1975 (see Table 2-3). In the past 20 years the growth rate has moderated to 2.2 percent. With the effects of a major recession still being felt, growth since 2005 has averaged 1.3 percent.

 

2.4 Employment Forecasts

 

In Southeast Florida, regional employment is expected to reach 5.4 million in 2040. In Central Florida, employment is expected to grow at 2.1 percent annually, reaching 2.1 million jobs in 2040. In Tampa area, employment is expected to grow at 1.4 percent annually, reaching 2 million jobs (Figure 2-12).

 

Figure 2-12 Employment Forecasts by Region 2010-2040 (Thousands)
 

Source: Louis Berger, 2017 from data provided by Woods & Poole Economics, 2017

 

To ensure that these forecasts represent reasonable levels of growth when compared to more recent projections, Louis Berger undertook a review of alternative employment forecast sources other than Woods & Poole.

 

The other source involved employment projections was developed by MPOs’ Long Range Transportation Plan. These forecast include projections for 2040. MPO forecasts see higher prospects for growth for the region in general

 

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Table 2–3 Alternative Employment Forecast Sources, 2040 (inthousands) 

 

  2010 2040 CAGR
County MPO Woods&Poole MPO Woods&Poole MPO Woods&Poole
Southeast Florida 2,717 3,127 3,681 5,430 1.0% 1.9%
Palm Beach 571 735 820 1,362 1.2% 2.1%
Broward 730 977 806 1,649 0.3% 1.8%
Miami-Dade 1,416 1,415 2,055 2,419 1.2% 1.8%
Central Florida 1,128 1,142 1,679 2,140 1.3% 2.1%
Orange 814 823 1,174 1,511 1.2% 2.0%
Osceola 88 101 140 231 1.5% 2.8%
Seminole 225 217 366 397 1.6% 2.0%
Lakeland Area (Polk County) 243 256 436 388 2.0% 1.4%
Tampa Area 1,228 1,269 1,678 2,028 1.0% 1.6%
Hillsborough 711 752 1,112 1,303 1.5% 1.8%
Pinellas 517 516 566 725 0.3% 1.1%
Total Study Area 5,317 5,793 7,474 9,987 1.1% 1.8%
Source: Louis Berger, 2018 from data provided by MPOs’ 2040 Long Range Transportation Plan and Woods & Poole CEDDS, 2017.

 

With no Census 100% Count available for employment, variations in long-term employment forecasts and base year measurements are not uncommon, especially during periods of volatility in economic conditions.

 

2.5 Income

 

Total personal income in the study area was over $400 billion (2017 dollars) in the year 2015. Per Capita personal income increased by 1.7 percent on average annually in the study area between 1975 and 2015. In particular, Southeast Florida had the largest gains in per capita personal income over this period with average annual increases of 1.8 percent between 1975 and 2015; Central Florida had per capita personal income gains of 1.6 percent on average annually during the 40 year period from 1975 to 2015; Tampa area had per capita personal income gains of 1.9 percent on average annually during this 40 years period. However, there were negative per capita personal income changes between 2005 and 2015, which is likely due to the economic downturn which occurred during this time (Figure 2-13, Table 2-5).

 

Figure2-13 REAL PER CAPITA PERSONAL INCOME, 1975-2015 (2017 DOLLARS)
 
 
Source: Louis Berger, 2018 from data provided by Woods & Poole Economics, 2017

 

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Table 2–4 Average Annual Per Capita Personal Income Change Over Time

 

Region/County/Area 1975- 1995- 2005- 2010-
  2015 2015 2015 2015
Southeast Florida 1.8% 1.2% 0.3% 1.6%
Palm Beach 2.4% 1.4% 0.4% 2.8%
Broward 1.4% 0.8% -0.1% 0.5%
Miami-Dade 1.3% 1.4% 0.6% 1.0%
Central Florida 1.6% 1.2% 0.1% 1.5%
Orange 1.6% 1.4% 0.6% 1.7%
Osceola 1.2% 1.1% 0.1% 1.1%
Seminole 2.0% 1.0% -0.3% 1.6%
Lakeland Area (Polk County) 1.3% 0.8% -0.3% 0.4%
Tampa Area 1.9% 1.4% 0.4% 0.7%
Hillsborough 2.0% 1.5% 0.4% 0.4%
Pinellas 1.8% 1.3% 0.5% 1.0%
Total Study Area Average 1.8% 1.1% -0.6% 1.1%

 Source: Louis Berger, 2018 from data provided by Woods & Poole Economics, 2017

 

2.6 Travel and Tourism

 

Given that the Central and Southern Florida regions and Tampa area are very popular tourism and business travel destinations, a good understanding of this travel market is essential. Louis Berger analyzed data from Visit Florida, the official tourism marketing corporation for the state of Florida. Visitation has been growing in this region in the past years. According to the latest data from Visit Florida, the state welcomed 106.6 million overnight visitors in 2015, an 8 percent increase from the prior year. Figure 2-15 below shows historical visitation to the state from 2006 through 2015. In addition, travel related spending supported a 4 percent increase in travel related employment between 2014 and 2015. Figure 2-16 below shows travel related employment between 2009 and 2015.

 

Figure 2-14 HISTORICAL TRENDS IN FLORIDA VISITATION: 2006-2015
 

Source: Louis Berger, 2018 with data provided by Visit Florida, 2015

 

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Figure 2-15 TRAVEL RELATED EMPLOYMENT IN FLORIDA

Source: Louis Berger, 2018 with data provided by Visit Florida, 2015

 

Among these visitations, Walt Disney World Resort (Disney) attracts a large portion of visitors each year. The proposed Brightline considers a stop near Disney. Louis Berger team analyzed current travel market data for Disney. Disney consists of six theme parks including: Magic Kingdom, Disney’s Animal Kingdom, Epcot, Disney’s Hollywood Studios, Typhoon Lagoon, and Blizzard Beach. Among these theme parks, Magic Kingdom has the highest number of visitors. Louis Berger team collected historical and current data from the Themed Entertainment Association (TEA) for theme part visitation as shown in below. The Figure 2-17 depicts strong growth of visitation to Disney, despite some decrease due to impact of the recession in 2008.

 

Figure 2-16 ANNUAL VISITATION BY THEME PARK

 

Source: 2017 Theme Index & Museum Index, Themed Entertainment Association, 2017

 

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 2.7 Domestic Visitation

 

Of all the domestic visitors to the state of Florida in 2015, 89 percent traveled to the state for leisure activities, mostly during the spring and summer months. Domestic business travelers primarily visited central Florida (41%) followed by southeastern Florida (20%). Table 2-6 below details the main purpose of visitors’ trips by type of trip.

 

Table 2–5 Domestic Visitors Main Trip Purpose

 

Trip Purpose 2013 2014 2015 ‘15/’14 %
        Change
LEISURE 89% 90% 89% -1
General Vacation 38% 40% 37% -3
Visit Friends/Relatives 26% 25% 25% 0
Getaway Weekend 10% 11% 13% 2
Special Event 8% 8% 7% -1
Other Leisure/Personal 7% 6% 8% 2
BUSINESS 11% 10% 11% 1
Transient Business 5% 4% 4% 0
Convention 2% 2% 2% 0
Seminar/Training 2% 2% 2% 0
Other Group Meetings 2% 1% 2% 1

Source: Louis Berger, 2018 with data provided by Visit Florida, 2015

 

The states of Georgia, New York, and Texas provided the largest number of domestic visitors to Florida in 2015. The average age of domestic leisure visitors to Florida in 2015 was 47.9. The largest percentage (30%) of visitors to Florida were age 35 to 49 followed by those age 18 to 34 (26%) (Profile of Domestic Visitors, Visit Florida 2015).

 

2.8 International Visitation

 

International visitors to Florida in 2015 were primarily travelling to the state on vacation or holiday (74%), staying for an average of 11 nights, similar to length of stay in 2014 (10.7). These visitors primarily visited the southeastern (68%) portion of the state. Trip purpose for visitors is detailed in Table 2-7.

 

Table 2–6 International Visitors Main Trip Purpose

 

Main Trip Purpose 2014 2015
Vacation/Holiday 74% 74%
Visit Friends/Relatives 12% 12%
Business 6% 7%
Conference/Convention/Trade Show 5% 5%
Other 3% 3%

 Source: Louis Berger, 2018 with data provided by Visit Florida, 2015

 

The top market for international visitors is Canada with 3.8 million Canadians visiting Florida in 2015. These visitors stayed an average of 23 nights when visiting, up slightly from 22.7 nights in 2014. Most of these visitors traveled to southeastern (44%), central-central eastern (37%) or central west-southwestern (28%) Florida. Most of these visitors travelled to Florida via plane (52%) while over one third made the trip by car (36%). Few visitors traveled with children in 2015 (19%); many traveled alone (47%), and about 56% traveled with a spouse /partner or family /

 

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relatives. Most (76%) of these travelers stayed in a hotel or motel during their stay in Florida (International Visitors to Florida, Visit Florida 2015).

 

Despite the distances between city centers, the communities and economies of Southeast Florida, Central Florid, Lakeland, and Tampa Area are interconnected in many ways. Substantial numbers of people travel between these areas for business, journey to work, recreation, and other purposes. This section outlines the characteristics of the overall intercity travel market and an evaluation of prospects for growth. As shown in the Figure 2-1, the study area consists of Southeast Florida which incorporates Miami Urbanized Area, Central Florida which incorporates Orlando Urbanized Area, Lakeland which incorporates Lakeland Urbanized Area, and Tampa which incorporates Tampa Urbanized Area.

 

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3.0 Intercity Travel Market for Brightline

 

One of the key inputs to the mode choice model used to develop the Brightline ridership and revenue forecast is the size of the total addressable market for the proposed service. The total addressable market has the following characteristics:

 

● Considers all travels within the addressable geographical market that can be logically served by the proposed Brightline long- and short- distance service.

 

● Includes all existing modes of travel (i.e. auto, rail, bus, ride share, air)

 

● Includes all market segments (visitors, residents) and trip purposes (airport-access trips, non-airport-access trips)

 

This section provides a description of the existing modes of travel between the city pairs for long- and short-distance trips as well as an account of mode-specific historical, current, and future market sizes. The existing market size estimates for each mode form the basis of the trip tables for the base year, while future sizing is based on the estimated annual growth rates.

 

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3.1 Addressable Market Geography for Brightline

The earlier sections provided an overview of the regional socioeconomics in South and Central Florida and Tampa area. Following a review of the region, Louis Berger proceeded to identify the addressable market geography, or study area, for the Brightline service. This geography was determined based on the existing and proposed station locations as shown in Figure 1-1. The catchment area for long-distance intercity travel between Southeast Florida and Central Florida, between Southeast Florida and Lakeland and between Southeast Florida and Tampa was set to encompass an area within 30 to 40 minutes of driving time of a Brightline station. The catchment areas for shorter distance trips between Central Florida, Lakeland and Tampa area was set to encompass and areas within 20 to 30 minutes of drive time to the stations.

 

Figure 3–1 Travel Time to Nearest Brightline Station

Source: Louis Berger, 2018

 

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Based on the station access travel times presented in Figure 3-1, Louis Berger developed study areas for both long and short distance market segments in the Phase I and Phase II Studies (Figure 3-2). The shaded portion of the left side depicts 40 minute drive time to Brightline stations that defined the catchment areas of both Phase I and II long distance market areas. The cross hatched portion of the same figure depicts the 25 minute drive time to stations in the Tampa-Orlando corridor that was used to define the Phase II Study catchment area for short distance trips. The right portion of Figure 3-2 depicts the short distance travel market catchment areas for the Phase II Study that was drawn based on the regional travel demand models zonal structure to delineate 20 minute station access times.

 

Figure 3–2 Market Catchment Areas

 

3.2 Travel Market for Brightline Phase I Study

 

The Brighline Phase I provides service connecting between Central and Southeast Florida (long-distance trips) and connecting cities within Southeast Florida (short-distance trips), where travel currently takes place by bus, rail (Amtrak and Tri-tail), air and car.

 

3.2.1 Bus

 

Travel by bus is available for both short- and long-distance trips. Intercity buses take approximately 4-6 hours to travel between Central and Southeast Florida and make stops in all three Southeast Florida cities. Intercity buses also serve all city pairs in Southeast Florida, and a popular set of transit bus routes connects Miami with Fort Lauderdale via the I-95 Express Lanes.

 

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Historical and Current Market Size

 

The intercity bus operators serving the Central-Southeast Florida market include Florida Express, Greyhound, Florida Sunshine, Red Coach, Megabus, Jet Set Express, Smart Shuttle Line, SuperTours, and Javax. Louis Berger estimated an average of 35 daily departures per direction between Central and Southeast Florida in 2017 based on published bus schedules of all the operators listed above. Assuming an average of 20 passengers per vehicle and an O-D distribution pattern similar to that of the rail travel market, Louis Berger estimated the 2016 daily volumes presented in Table 3-1.

 

Table 3-1 Estimated Daily Intercity Long-Distance Bus Person Trips 

City Pair 2010 2016 2010-2016
CAGR
Orlando to Miami 212 297 +5.8%
Orlando to Fort Lauderdale 126 193 +7.4%
Orlando to West Palm Beach 145 209 +6.3%
Total 483 700 +6.4%

 

Source: Louis Berger, 2017

 

The number of intercity bus connections in Southeast Florida is limited to one public agency and a small number of privately operated services, which make local stops in the three Southeast Florida cities on the way to further destinations, primarily Orlando and Tampa. Broward County Transit (BCT) operates the 95Express and 595Express services. Each offer approximately 30 buses per day per direction and attract a total of approximate 1,650 riders per day (average annual daily basis from BCT reports). The full one-way fare is $2.65. This service is growing rapidly – as of 2014, the service carried only approximately 1,000 riders per day (average annual daily basis from BCT reports). Services on private operators cost in the $10-$25 range for a one-way trip between Southeast Florida cities. Greyhound offers about 13 daily trips from Miami to Fort Lauderdale and six from Miami through to West Palm Beach. Other intercity bus operators serving the Southeast Florida market include Florida Express, Florida Sunshine, Red Coach, and Megabus.

 

Bus ridership estimates in Southeast Florida were developed by assuming 30 riders per bus within the short-distance market (with 27 bus departures per day serving this corridor) and an O-D distribution pattern similar to the commuter rail market within the same corridor. Published ridership estimates obtained from Broward County Transit were also added to the initially estimated volume of bus trips within the Fort Lauderdale-Miami city pair. Table 3-2 shows the number of bus riders assumed for the base year forecast.

 

Table 3-2 Estimated Daily Short-Distance Bus Person Trips

City Pair 2016
W. Palm Beach – Fort Lauderdale 277
W. Palm Beach – Miami 184
Fort Lauderdale – Miami 2,006
Total 2,468
Source: Louis Berger, 2017

 

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Growth Forecast

 

Future-year bus ridership volumes were estimated by applying growth rates obtained from the Chaddick Institute for Metropolitan Development at DePaul University, which monitors intercity bus travel patterns in the country.3 The initial surge in intercity bus travel growth observed since 2007 has slowed in recent years – as shown in Figure 3-3. As such, Louis Berger applied a 1.7% CAGR to estimate future-year bus ridership, as shown in Table 3-3 and Table 3-4.

 

Figure 3-3 Historical Growth in Intercity Bus Travel in the United States

Source: Louis Berger analysis of data from the Chaddick Institute, 2016

 

Table 3-3 Projected Daily Intercity Long-Distance Bus Person Trips

City Pair 2016 2040 2016-2045
CAGR
Central Florida to Miami 297 478  
Central Florida to Fort Lauderdale 193 311  
Central Florida to West Palm Beach 209 337  
Total 700 1,125 +1.7%
Source: Louis Berger, 2017

 

Table 3-4 Projected Daily Short-Distance Bus Person Trips

City Pair 2016 2045

2016-2045

CAGR
W. Palm Beach to Fort Lauderdale 277 446  
W. Palm Beach to Miami 184 296  
Fort Lauderdale to Miami 2,006 3,225  
Total 2,468 3,967 +1.7%
Source: Louis Berger, 2017

 

 

3 “The Remaking of the Motor Coach: 2015 Year in Review of Intercity Bus Service in the United States”, Chaddick Institute for Metropolitan Development at DePaul University, January 2016, https://las.depaul.edu/centers-and-institutes/chaddick-institute-for-metropolitan-development/research-and-publications/Documents/2015%20Year%20in%20Review%20of%20Intercity%20Bus%20Service%20in%20the%20Unite d%20States.pdf

 

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3.2.2     Long-Distance Rail (Amtrak)

 

The rail travel market analyzed in this study is separated into long- and short-distance markets. Amtrak provides services for the long-distance market, from Miami to Orlando twice a day. The Amtrak service makes 11 stops between Miami and Orlando, including Fort Lauderdale and West Palm Beach. In Miami, the Amtrak station is in Hialeah, approximately a 20-minute drive northwest of downtown. In Fort Lauderdale, the Amtrak station is on the west side of I-95, approximately a 10-minute drive west of downtown. In West Palm Beach, the Amtrak station is a short walk from the proposed Brightline station. In Orlando, the Amtrak station is downtown, approximately a 25-minute drive northwest from the airport.

 

Historical and Current Market Size

 

Boarding and alighting data for all Amtrak stations in Florida is presented in Figure 3-4, showing ridership to, within, and from the state fluctuating between 700,000 and 1.25 million trips annually. The fluctuations around an overarching trend of growth between 2003 and 2016 reflect shifts in economic conditions as well as competitive dynamics overall in the intercity travel market (fuel prices, growth in intercity bus travel, etc.).

 

Figure 3-4 Amtrak Florida Station Boardings

 

Source: National Association of Rail Passengers Amtrak Ridership Statistics, 2017

 

However, Amtrak ridership between Central and Southeast Florida is limited to only a relatively small portion of the total boardings and alightings statistics shown in Figure 3-4. Louis Berger estimated the proportion of Central to Southeast Florida trips using 2008 Amtrak station-pair ridership data. The resulting count is approximately 36,000 trips annually, as shown in the city-pair breakdown in Table 3-5. The 2016 intercity trip estimate uses the 2008 passenger trip distribution with some adjustments to account for changes in Amtrak Florida station boardings and alightings. The relatively small long-distance rail travel market is most likely a function of Amtrak’s high travel times.

 

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Growth Forecast

Louis Berger estimated future-year estimates of Amtrak ridership between Central and Southeast Florida by applying the growth rates implied by the trend projection in Figure 3-4, resulting in CAGR of 0.4%, as shown in Table 3-5.

 

Table 3-5 Historical and Future Amtrak Passenger Volumes

City Pair 2008 2016 2045 2016-2045
CAGR		 2045 Daily
Orlando – W. Palm Beach 10,338 10,519 11,698 +0.4% 32
Orlando – Fort Lauderdale 9,946 9,711 10,800 +0.4% 30
Orlando – Miami 15,762 14,932 16,606 +0.4% 45
Total 36,046 35,162 39,105 +0.4% 107
Source: Amtrak, National Association of Rail Passengers, Louis Berger, 2017

 

3.2.3     Short-Distance Rail (Tri-Rail)

 

While the Amtrak service can be used for travel within Southeast Florida, the short-distance rail market is primarily served by Tri-Rail, a commuter rail line managed by the South Florida Regional Transportation Authority (SFRTA) connecting Miami and West Palm Beach, making a total of 18 station stops over 72 miles. The system has 5 stations in Miami-Dade County, 7 in Broward County, and 6 in Palm Beach County, as shown in Figure 3-5. This service runs 15 times a day per direction. In all three cities, shuttle connections are provided to the airports. In Miami, connection is available to Metrorail at the Metrorail Transfer station, whereas in Fort Lauderdale and West Palm Beach, Amtrak and Tri-Rail share a station. Tri-Rail’s southernmost station is near the Miami airport, approximately a 20 minute drive northwest of downtown. A planned extension of the Tri-Rail system will offer direct connectivity from MIA to Brightline’s Miami Central Station.

 

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Figure 3-5 Tri-Rail System Map

 

Source: Tri-Rail System Map

 

Figure 3-6 shows annual system ridership obtained from the Tri-Rail monthly operation statistics and the National Transit Database (NTD). System ridership has grown at an annualized rate of 2.9% over the last ten years, although that rate of growth appears to be slowing down (1.2% annualized rate of growth over the last five years).

 

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Figure 3-6 Tri-Rail System Ridership

 

 

Source: National Transit Database

 

Only the relevant stations that provide an alternative to Brightline based on origins and destinations were included in the Tri-Rail ridership estimates. Primary stations covered by the three Brightline service short-distance markets in West Palm Beach, Fort Lauderdale, and Miami, respectively, were:

 

● Mangonia Park Station, West Palm Beach, and Lake Worth;

 

● Fort Lauderdale downtown, Fort Lauderdale Airport, and Sheridan St.; and

 

● Opa-Locka, Metrorail Transfer Station, Hialeah Market, and Miami Airport.

 

Growth Forecast

 

Louis Berger examined the South Florida Regional Transportation Authority’s SFRTA Forward Plan: A Transit Development Plan for SFRTA FY 2014-2023. This report contained the results of a 2013 on-board and platform intercept origin and destination survey for Tri-Rail that can be used to determine the portion of Tri-Rail ridership traveling between the three cities to be served by Brightline. The survey indicated that approximately 24 percent of total ridership has both an origin and destination in the central city locations to be served by Brightline. Table 3-6 presents the estimate of applicable Tri-Rail ridership by city pair. The 2045 estimates reflect the growth implied by the trend presented in Figure 3-6.

 

Table 3-6 Current and Projected Daily Short-Distance Rail Trips

City Pair 2016 2045 CAGR Daily (2045)
Miami – West Palm Beach 229,807 300,607 +0.9% 824
Miami – Fort Lauderdale 434,227 568,006 +0.9% 1,556
Fort Lauderdale – West Palm Beach 345,636 452,122 +0.9% 1,239
Total 1,009,671 1,320,735 +0.9% 3,618
 
Source: Louis Berger, 2017

 

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3.2.4     Air

 

Air travel is available at two airport pairs for travel from Southeast Florida to Central Florida. From Orlando International Airport (IATA airport code MCO), flights are available to Fort Lauderdale-Hollywood International Airport (IATA code FLL – via Spirit and Silver Airways) and Miami International Airport (IATA code MIA – via American and Delta). Currently, no flights from Central Florida are available to Palm Beach International Airport (IATA code PBI).

 

Historical and Current Market Size

Air travel volumes for the long-distance travel market were derived using data obtained from the FAA 10% sample of tickets, and corroborated against the Orlando International Airport air traffic reports published by Greater Orlando Aviation Authority (GOAA). Louis Berger compiled and reviewed historical data from the Federal Aviation Authority’s (FAA) 10 percent sample of tickets database. This data was used to trace historical volumes of air travel between each of the city pairs that offer air travel.

 

Historical air traffic data going back twenty years in Figure 3-7 shows a general decline air passenger volumes between Central and Southeast Florida, largely accelerated by the additional airport security requirements instituted after the events of September 11, 2001. Air traffic volumes post the 9-11 declines in short-distance air travel have remained relatively steady with fluctuations reflecting the effects of economic conditions.

 

Figure 3-7 Annual Air Traffic Volume between Central and Southeast Florida

 

 

Source: FAA 10 Percent Tickets Database, 2017

 

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Figure 3-7 shows that air passenger traffic between MCO and MIA has steadily grown post 9-11 while the volumes of trips between MCO and FLL have steadily declined, and trips between MCO and PBI have declined to zero. Table 3-7 shows the change in trip volumes between 2010 and 2016 by airport pair in both annual and daily terms.

 

Table 3-7 Annual Air Passenger Volumes, FAA 10% Ticket Sample

Airport Pair (Both Directions) 2010 2010 2016 2016 2010-2016
  Annual Daily Annual Daily CAGR
Orlando (MCO) – WPB (PBI) 40 <1 0 0  
Orlando(MCO) – Fort Lauderdale (FLL) 163,500 448 54,880 150 -16.7%
Orlando (MCO) – Miami (MIA) 88,900 244 168,050 460 +11.1%
Total 252,440 692 222,930 610 -2.1%
 
Source: Louis Berger, 2017

 

Growth Forecast

Given the trends depicted in Figure 3-7, Louis Berger estimated the future growth of the Central to Southeast Florida travel market based on the FAA’s Terminal Area Forecasts (TAF) for all three airports (MCO, FLL and MIA). TAF is the official forecast of aviation activity at U.S. Airports and are econometrically driven demand-side forecasts.

 

Figure 3-8 compares Central to Southeast Florida air passenger volumes against the volume of total enplanements at the three airports. The figure shows that the share air passenger traffic between Central and Southeast Florida has been declining relative to the total volume of air passenger enplanements at all three airports. The study team applied this trend of declining shares to the TAF projected growth in enplanements at all three airports.

 

Figure 3-8 Comparison of Air Traffic Volumes

 

 

Source: FAA Terminal Area Forecast, 2016

 

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The resulting volume of air passenger traffic between Central and Southeast Florida was further segmented to distinguish between travel to Fort Lauderdale and Miami based on the trends presented in Figure 3-5. Table 3-8 presents the resulting estimates of air passengers in the Brightline service travel market. The resulting air passenger volumes in the Central to Southeast Florida market grow at about a third of the rate projected for total enplanements of study area airports. This positive growth is attributed to growth observed within the Orlando-Miami city pair.

 

Table 3-8 Estimates of Air Passengers in the Brightline Service Travel Market

  2002
Annual		 2010
Annual		 2016
Annual		 2045
Annual		 2045 Daily 2016-2045
CAGR
             
Total Enplanements (MCO/FLL/MIA) 21,798,554 27,180,107 34,861,631 62,033,916 169,840 +2.01%
Orlando (MCO) – Fort Lauderdale (FLL) 205,240 163,500 54,880 40,304 110 -1.06%
Orlando (MCO) – Miami (MIA) 68,380 88,900 168,050 231,391 634 +1.11%
Central Florida – Southeast Florida 273,620 252,400 222,930 271,695 744 +0.68%
% of Enplanements 1.26% 0.93% 0.64% 0.44%    

 

Source: Louis Berger, 2017

 

3.2.5     Auto

 

Auto is the prevalent mode of travel within Southeast Florida and the dominant mode of travel between Southeast and Central Florida. Two primary auto routes collect a significant majority of traffic between Southeast Florida and Central Florida – the Florida Turnpike and I-95. While the Florida Turnpike is faster and more direct, it is a toll road, whereas I-95 is a free route. The two other highway routes between the two regions – US-27 and I-75 – add one and two hours respectively to the travel time between the two regions. Southeast Florida has a dense and often significantly congested highway network, but the Florida Turnpike and I-95 remain the major north-south arteries connecting the three Southeast Florida cities under discussion in this report. Other north-south arteries are either signaled (US-1, US-441) or further inland (SR-826, I-75, Florida Turnpike Homestead Extension).

 

Historical and Current Market Size

 

Louis Berger reviewed historical traffic counts on the Florida Turnpike and I-95 at points in between Southeast Florida and Central Florida, and found significant growth over the past decades. Traffic on the Florida Turnpike represents about 80% of the total traffic volume between Central and Southeast Florida. Louis Berger identified two counter stations on the Florida Turnpike between West Palm Beach and Orlando that are representative of the traffic traveling on the Turnpike between Central and Southeast Florida. Traffic on these stations has grown at a 3.2% CAGR from 2001 to 2016, with a slight decline during the years of the Great Recession. On I-95, which represents about 20% of the total traffic volume traveling between Central and Southeast Florida, the traffic counter in St. Lucie, FL – an intermediate point between the main urban areas of Orlando and West Palm Beach – indicates an average annual traffic growth of approximately 2.1% from 2001 to 2015. Annual traffic counts at four counters on the major roads connecting Southeast Florida and Central Florida, including the two counters discussed above, are shown in Figure 3-9 and Figure 3-10.

 

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Figure 3-9 Florida Turnpike Central-to-Southeast Florida Historical Traffic Counts

 

 

Source: Louis Berger analysis of data from FDOT, 2017

 

Figure 3-10 I-95 Central-to-Southeast Florida Historical Traffic Counts

 

 

Source: Louis Berger analysis of data from FDOT, 2017

 

Current traffic flows between Central and Southeast Florida on the Turnpike and I-95 are characterized as follows:

 

● Day of week patterns – Friday is the busiest day at all four counters, with average traffic on Friday being 21% above the average day. Tuesdays and Wednesdays are the least busy days at all four counters. The prominence of weekends is particularly pronounced at the Turnpike counters, where Friday-Saturday traffic counts are 17% above the average day. Furthermore, the Turnpike counters exhibit directionality on the weekends, with 38% more traffic seen on Sundays in the northbound direction than southbound,

 

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and with 21% less traffic seen on Sundays in the northbound direction than southbound. Similar, but more muted, effects are seen on Mondays, Thursdays, and Saturdays, as well as on I-95 on Fridays and Sundays.
 
● Time of day patterns – From investigating hourly traffic counter data from June 2016, the four counters appear to behave fairly idiosyncratic travel patterns by time of day. The St. Lucie County counter on the Florida Turnpike peaks at roughly midday every day of the week in both directions of travel. The St. Lucie County counter on I-95 displays traditional peaking (with weekday peaks in the morning and afternoon, and weekend peaks at midday), but traffic levels appear higher at the afternoon weekday peak in both directions than in the morning peak. The Osceola County counter on the Florida Turnpike displays traditional peaking, with weekday morning peak traffic heading northbound, and weekday afternoon peak traffic heading southbound. At all counters, the highest-traffic hours tend to each represent roughly 8.5% of daily traffic.

 

● Type of vehicle– The split of traffic by vehicle type are broadly similar across the counters, as well as by year through the 2001-2016 time period. Passenger vehicles represent roughly 85% of traffic, whereas trucks represent roughly 15% of traffic. The 15% of traffic represented by trucks is composed roughly of 4% single-unit trucks, 10% combination trailer trucks, and 1% multi-trailer trucks.

 

Accurately evaluating historical traffic growth within the three Southeast Florida cities is more complex, since there are a significant amount of potential origins and destinations in between the three main cities and therefore an important number of local trips that cannot be accurately accounted for. In order to gain an indication of the number of historical trips, Louis Berger investigated traffic counters between the three Southeast Florida cities on both I-95 and the Florida Turnpike, using FDOT’s Florida Traffic Online web portal. The AADTs at the four counters are shown in Figure 3-11 and Figure 3-12. From 2001 to 2016, traffic grew more quickly at both Florida Turnpike counters (2.8% CAGR at Atlantic Ave. and 3.7% at SR-824) than at the I-95 counters (0.6% at SR-858 and 0.3% at 48th St.), a pattern which can be explained by a combination of growing incomes in Southeast Florida and capacity issues on I-95. Traffic on both main routes, however, indicates an upward trajectory.

 

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Figure 3-11 Southeast Florida Historical Traffic Counts

 

 

Source: Louis Berger analysis of data from FDOT, 2017

 

Figure 3-12 Southeast Florida Historical Traffic Counts

 

 

Source: Louis Berger analysis of data from FDOT, 2017

 

Louis Berger relied on cell-phone data to establish the size of the current auto market for both long- and short- distance trips. Datasets purchased from AirSage, a company specialized in collecting and analyzing cell-phone location data, included observed travel behavior for March, June, September, and December of 2016. This dataset included all trips between origin and destination zones within the addressable geographic market for Brightline (Section 2.10).

 

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For trips seen in the cell-phone movement datasets, AirSage increments the trip table for the origin-destination pair based on a factor dependent on the population estimate and cell-phone penetration of that device’s home location (which is determined by an algorithm based on the device’s nighttime location). Population estimates are consistent with US Census data estimates. Based on this home location, AirSage also allocates the resulting trips to either a “resident” or a “visitor” category. Any device whose home location is outside of the zones included in the addressable geographical market for Brightline is classified as a “visitor”. The AirSage data also reflects all trips regardless of mode.

 

In order to ensure that the data is representative of the auto market within the addressable market geography for Brightline, Louis Berger had to perform the following adjustments to the AirSage data:

● Deduct trips from other modes as such that the data represents auto trips only, using the existing market size estimates for each mode described earlier in this section.
● For short-distance trips, reducing the number of trips by 12% to account for the percentage of travelers manifesting the need to use a car for intermediate stops during their trips, as indicated in the 2012 Stated Preference Survey (See Section 4.2.1). This percentage of travelers are considered to be captive to the auto mode choice, and are therefore not considered part of the in-scope market for Brightline.
● For long-distance trips, the visitor market was initially reduced by 46% in market size to calibrate the trip counts to vehicle counts observed on the Florida Turnpike; and then further revised down by 25% to account for through trips whose origin or destination is not within the addressable market geography.

 

Table 3-9 shows the resulting average daily travel counts for the long-distance market, while Table 3-10 shows the resulting average daily travel counts for the short-distance market.

 

Table 3-9 Estimated Daily Long-Distance Auto Person Trips 

City Pair Resident
SB		 Resident
NB		 Resident
Total		 Visitor
SB		 Visitor NB Visitor
Total		 Total
Orlando to West Palm Beach 12,473 12,236 24,710 2,652 2,658 5,310 30,019
Orlando to Fort Lauderdale 8,083 7,764 15,846 2,066 2,050 4,116 19,962
Orlando to Miami 8,400 8,139 16,538 2,088 1,943 4,031 20,569
Total 28,955 28,139 57,094 6,806 6,651 13,457 70,551

 

Source: Louis Berger analysis of AirSage data, 2017. SB = Southbound; NB = Northbound

 

Table 3-10 Estimated Daily Short-Distance Auto Person Trips

City Pair Resident
SB		 Resident
NB		 Resident
Total		 Visitor
SB		 Visitor NB Visitor
Total		 Total
West Palm Beach to Miami 11,641 11,699 23,339 22,909 20,743 43,651 66,991
W.P.B. to Fort Lauderdale 88,047 88,181 176,228 100,207 101,610 201,817 378,044
Fort Lauderdale to Miami 147,939 151,424 299,363 131,413 128,243 259,657 559,019
Total 247,626 251,304 498,930 254,529 250,596 505,125 1,004,055

 

Source: Louis Berger analysis of AirSage data, 2017

 

Table 3-11 shows the share of trips allocated to each city pair for residents and visitors in the long-distance travel market while Table 3-12 shows the same statistic, but for the short-distance travel market. As shown, the three city pairs have roughly equal shares in the long-distance market, with Orlando-West Palm Beach holding a slight plurality. In the short-distance market, the Fort Lauderdale-Miami city pair dominates.

 

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Table 3-11 Long-Distance City Pair O-D Shares

City Pair Resident Visitor Total
       
Orlando to West Palm Beach 43% 39% 43%
Orlando to Fort Lauderdale 28% 31% 28%
Orlando to Miami 29% 30% 29%
 
Source: Louis Berger, 2017

 

Table 3-12 Short-Distance City Pair O-D Shares

City Pair Resident Visitor Total
       
West Palm Beach to Miami 5% 9% 7%
W.P.B. to Fort Lauderdale 35% 40% 38%
Fort Lauderdale to Miami 60% 51% 56%
 
Source: Louis Berger, 2017

 

Growth Forecast

 

In order to estimate future growth for the long-distance auto travel market, Louis Berger developed an econometric model of traffic on the Florida Turnpike. The model used historical monthly traffic counts for the 2001-2016 period for a traffic counter station located at a point north of SR-70 on the Florida Turnpike. The economic variables considered included employment, number of households, and gas prices. Seasonal variation by month of year was also taken into account in the model development. This monthly count data also served as a validation of the long-distance origin-destination trip estimates provided by AirSage.

 

The traffic model specification was evaluated by comparing fitted versus actual traffic over the recent historical period going back to January of 2001 as shown in Figure 3-13. The resulting close agreement between fitted and actual model results provides confidence in the model’s ability to map key drivers to actual traffic levels. Figure 3-14 presents the historical observed and forecast traffic of Central – Southeast Florida Turnpike Traffic. Traffic at the counter grew at a 3.2% CAGR. The econometric model forecasts a 3.2% CAGR through 2047. Additional information on the traffic model, including sources and forecasts for the economic variables used, can be found in Appendix B.

 

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Figure 3-13 Florida Turnpike Traffic Model Performance (Actual Vs. Fitted – Monthly)

 

 

Source: Louis Berger analysis, 2017

 

Figure 3-14 Econometric Analysis and Projection of Florida Turnpike Traffic (Monthly)

 

 

Source: Louis Berger analysis, 2017

 

Growth estimates for the short-distance market are based on the growth rates modeled in the Southeast Florida Regional Planning Model (SERPM), maintained by the Florida Department of Transportation. This regional planning

 

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model accounts for a 1.2% annual growth rate in travel in the Southeast Florida region, broadly in line with regional employment growth forecasts discussed in Section 2.5.

 

3.3     Travel Market for Brightline Phase II Study

 

The Brighline Phase II provides service connecting between Tampa Area and Southeast Florida (long-distance trips), where travel currently takes place by bus, rail (Amtrak), air, and car, and connecting cities within Tampa Area, Lakeland and Central Florida (short-distance trips), where travel mainly takes place by bus and car. Because Brighline Phase II will include a terminal station at Orlando Airport, Louis Berger team also analyzed travel market for airport access trips in addition to non-airport trips (short- and long-distance trips)

 

3.3.1     Bus

 

Travel by bus is available for both short- and long-distance trips. Long-distance intercity buses take approximately 3-6 hours to travel within study area. Short-distance intercity buses also serve all city pairs in Central Florida, Tampa and Lakeland area.

 

Current Market Size

 

The intercity bus operators serving the study area market include Florida Express, Greyhound, Florida Sunshine, Red Coach, Megabus, Jet Set Express, Smart Shuttle Line, SuperTours, and Javax. Louis Berger estimated an average of 43 daily departures per direction between Central and Southeast Florida in 2017, when this study was conducted, based on published bus schedules of all the operators listed above. Assuming an average of 30 passengers per vehicle and an O-D distribution pattern similar to that of the rail travel market, Louis Berger estimated the 2018 daily volumes presented in Table 3-13; Louis Berger also compared the estimates with those provided by Developing Refined Estimates of Intercity Bus Ridership (Prepared by RSG).

 

Table 3-13 Estimated Daily Intercity Long-Distance Bus Person Trips

City Pair Daily Weekly Weekly RSG
Benchmark
Tampa - Orlando 158 1,103 n/a
Lakeland - Orlando 11 77 n/a
Tampa - Lakeland 11 80 n/a
Tampa Area-Southeast Florida 240 1,680 500-1999
Tampa - West Palm Beach
 82 577
Tampa - Ft Lauderdale
 56 395
Tampa - Miami
 101 708
Lakeland Area-Southeast Florida 60 420 n/a
Lakeland - West Palm Beach
 18 127 n/a
Lakeland - Ft Lauderdale
 16 112 n/a
Lakeland - Miami
 26 181 n/a
Total 480 3,360 n/a
Source: Louis Berger, 2018 and Developing Refined Estimates of Intercity Bus Ridership, RSG, 2015   

 

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Figure 3–15 Ridership Benchmark FromRSG

 

Source: Louis Berger, 2018 and Developing Refined Estimates of Intercity Bus Ridership, RSG, 2015

 

Growth Forecast

 

Future-year bus ridership volumes were estimated by applying growth rates of population obtained from the Bureau of Economic and Business Research (BEBR) at University of Florida. The growth rates were based on BEBR Medium case and were considered in each study area separately.

 

Table 3–14 Projected Daily Intercity Bus Person Trips

City Pair 2017 2045 CAGR
Tampa - Orlando 158 223 1.2%
Lakeland - Orlando 11 15 1.1%
Tampa - Lakeland 11 17 1.6%
Tampa Area-Southeast Florida 239 310 0.9%
Tampa - West Palm Beach 82 107 1.0%
Tampa - Ft Lauderdale 56 71 0.9%
Tampa - Miami 101 132 1.0%
Lakeland Area-Southeast Florida 60 78 0.9%
Lakeland - West Palm Beach 18 24 1.0%
Lakeland - Ft Lauderdale 16 20 0.8%
Lakeland - Miami 26 34 1.0%
Total 480 643 1.1%
Source: Louis Berger, 2018      

 

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3.3.2     Rail (Amtrak)

 

Amtrak provides services from Tampa to Southeast Florida via Orlando. The Amtrak service makes 8 major stops within the study area, including Tampa, Lakeland, Winter Haven, Winter Park, Orlando, West Palm Beach, Fort Lauderdale and Miami.

 

Historical and Current Market Size

 

Boarding and alighting data for all Amtrak stations in Florida is presented in Figure 3-16, showing ridership to, within, and from the state fluctuating between 700,000 and 1.25 million trips annually. The fluctuations around an overarching trend of growth between 2003 and 2017 reflect shifts in economic conditions as well as competitive dynamics overall in the intercity travel market (fuel prices, growth in intercity bus travel, etc.).

 

Figure 3–16 Amtrak Florida Station Boardings (Thousands)

 

Source: National Association of Rail Passengers Amtrak Ridership Statistics, 2017

 

Growth Forecast

 

Louis Berger estimated future-year estimates of Amtrak ridership between Central and Southeast Florida by applying the growth rates implied by the trend projection in Figure 3-16, resulting in CAGR of 1.1%, as shown in Table 3-15.

 

Table 3–15 Historical and Future Amtrak Passenger Volumes

 

City Pair 2008 2017 2045 2017-2045
CAGR		 2045 Daily
Tampa - Orlando 11,832 11,762 16,235 1.2% 44
Lakeland - Orlando 879 818 1,062 0.9% 3
Tampa - Lakeland 828 857 1,231 1.3% 3
Tampa Area-Southeast Florida 36,580 38,191 55,435 1.3% 152
Tampa - West Palm Beach 11,825 13,116 19,295 1.4% 53
Tampa - Ft Lauderdale 8,570 8,972 13,258 1.4% 36
Tampa - Miami 16,185 16,103 22,882 1.3% 63
Lakeland Area-Southeast Florida 20,705 18,906 24,551 0.9% 67
Lakeland - West Palm Beach 5,700 5,719 7,605 1.0% 21
Lakeland - Ft Lauderdale 5,593 5,043 6,657 1.0% 18
Lakeland - Miami 9,412 8,144 10,289 0.8% 28
Total 70,824 70,534 98,514 1.2% 270

Source: Amtrak, National Association of Rail Passengers, Louis Berger, 2018

 

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3.3.3     Air

 

Air travel is available from Tampa to Southeast Florida with flights between Tampa International Airport (TPA) to both Fort Lauderdale and Miami airports.

 

Historical and Current Market Size

 

Similar to the discussion of the air travel market in Section 3.2, air travel volumes for the Phase II Study were derived using data obtained from the FAA 10% sample of tickets. Figure 3-17 shows the historical volume of origin-destination (O-D) air travel between Tampa (TPA) and both Fort Lauderdale (FLL) and Miami (MIA). Similar to the patterns observed in the Orlando to Southeast Florida market, this figure shows a declining trend of air passenger volumes to and from Fort Lauderdale, and conversely, an increasing trend of air passenger volumes to and from Miami.

 

Figure 3–17 Annual Air Traffic Volume between Tampa and Southeast Florida

 

 

Source: FAA 10 Percent Tickets Database, 2018

 

Table 3-16 shows the change in trip volumes between 2010 and 2017 by airport pair in both annual and daily terms.

 

Table 3–16 Annual Air Passenger Volumes, FAA 10% Ticket Sample

Airport Pair (Both Directions) 2010
Annual		 2010
Daily		 2017
Annual		 2017
Daily		 2010-2017
CAGR
Tampa Area-Southeast Florida 347,300 952 384,650 1,054 1.47%
Tampa (TPA) – Fort Lauderdale (FLL)
 265,980 729 220,320 604 -2.65%
Tampa (TPA) – Miami (MIA)
 81,320 223 164,330 450 10.57%
Source: Louis Berger, 2018
 
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Growth Forecast

 

Given the trends depicted in Figure 3-17, Louis Berger estimated the future growth of the Tampa-Southeast Florida travel market by determining the share of this travel market in relation to the overall air travel activity at all three airports. Figure 3-18 depicts both the historical and projected levels of enplanements at both airports obtained from the FAA’s Terminal Area Forecasts (TAF) for all three airports (TPA, FLL and MIA). All of these three airports experienced significant growth in enplanements over the past 30 years and are expected to continuing grow along similar trajectories over the next 30 years. Figure 3-19 depicts the historical and projected trends of TPA-FLL and TPA-MIA volumes. Based on this assessment, Louis Berger predicted the growth in air travel that is depicted in Figure 3-20.

 

Figure 3–18 FAA’s Terminal Area Forecasts For Airport Enplanements In Study Area

 

 

Source: FAA Terminal Area Forecast, 2018

 

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Figure 3–19 Passenger Volume Share Of Enplanement In Origin And Destination Airport

 

 

Source: Louis Berger, 2018

 

Figure 3–20 Prediction Of Future Passenger Volume Between City Pairs

 

 

Source: Louis Berger, 2018

 

3.3.4     Auto

 

Auto is the prevalent mode of travel within Southeast Florida and the dominant mode of travel with study area. Two primary auto routes collect a significant majority of traffic between Southeast Florida and Central Florida – the Florida Turnpike and I-95. While the Florida Turnpike is faster and more direct, it is a toll road, whereas I-95 is a free route.

 

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The two other highway routes, I-4 and I-75, connect Tampa and Southeast Florida and Tampa and Central Florida separately.

 

Historical and Current Market Size

 

Louis Berger reviewed historical traffic counts on the Florida Turnpike, I-9, I-4 and I-75 at points within study area, and found significant growth over the past decades. Traffic on the Florida Turnpike represents about 80% of the total traffic volume between Central and Southeast Florida. Louis Berger identified four counter stations on these four routes that are representative of the traffic traveling from Central Florida and Tampa to Southeast Florida and from Tampa to Central Florida. Traffic on these stations has grown at a 3.1% CAGR from 2002 to 2017, with a slight decline during the years of the Great Recession. Annual traffic counts at four counters on the major roads connecting Southeast Florida and Central Florida, including the two counters discussed above, are shown in Figure 3-21 and Figure 3-22.

 

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Figure 3–21 Counter stations identified for historical traffic volume

 

Source: Louis Berger, 2018

 

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Figure 3–22 Historical AADT Trend At Counter Stations

 

Source: Louis Berger, 2018

 

Table 3–17 Summary Of Historical Aadt And Cagr

 

Counter Stations
 2002 2007 2012 2017 2002-2017 CAGR
Turnpike
 
 22,900 28,900 26,000 33,900 2.6%
I-95
 
 42,500 52,289 46,500 61,831 2.5%
I-75
 
 18,351 21,141 19,444 24,968 2.9%
I-4
 
 55,000 74,000 76,500 94,000 3.6%
Total
 
 136,000 145,100 154,294 165,585 3.1%

Source: Louis Berger, 2018 

 

Louis Berger relied on cell-phone data to establish the size of the current auto market of the study area. Datasets purchased from AirSage, a company specialized in collecting and analyzing cell-phone location data, included observed travel behavior for March, June, September, and December of 2017. This dataset included all trips between origin and destination zones within the travel market for Brightline.

 

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For trips seen in the cell-phone movement datasets, AirSage increments the trip table for the origin-destination pair based on a factor dependent on the population estimate and cell-phone penetration of that device’s home location (which is determined by an algorithm based on the device’s nighttime location). Population estimates are consistent with US Census data estimates. Based on this home location, AirSage also allocates the resulting trips to either a “resident” or a “visitor” category. Any device whose home location is outside of the zones included in the addressable geographical market for Brightline is classified as a “visitor”. The AirSage data also reflects all trips regardless of mode.

 

In order to ensure that the data is representative of the auto market within the addressable market geography for Brightline, Louis Berger had to perform the following adjustments to the AirSage data:

● Deduct trips from other modes as such that the data represents auto trips only, using the existing market size estimates for each mode described earlier in this section.

● For short-distance trips, reducing the number of trips by 12% to account for the percentage of travelers manifesting the need to use a car for intermediate stops during their trips, as indicated in the 2012 Stated Preference Survey (See Section 4.2.1). This percentage of travelers are considered to be captive to the auto mode choice, and are therefore not considered part of the in-scope market for Brightline.

● For long-distance trips, the visitor market was initially reduced by 46% in market size to calibrate the trip counts to vehicle counts observed on the Florida Turnpike; and then further revised down by 25% to account for through trips whose origin or destination is not within the addressable market geography.

 

Table 3–18 Estimated Daily Auto Person Trips By Time Of Day

     
 Resident
          Visitor
      Total
City Pair AM MD
PM
 NT Total AM MD PM NT Total
Tampa - Orlando 2,514 16,119 8,371 11,547 38,551 832 8,080 3,836 5,738 18,486 57,037
Lakeland - Orlando 1,044 3,975 2,248 2,962 10,229 359 2,282 1,209 1,651 5,501 15,730
Tampa - Lakeland 1,025 4,756 2,454 3,480 11,715 226 1,773 924 1,271 4,194 15,909
Tampa Area-Southeast Florida 423 4742 2622 3019 10,806 155 1653 934 1058 3,800 14,606
Tampa - West Palm Beach 55 959 559 525 2,098 18 299 164 166 647 2,745
Tampa - Ft Lauderdale 169 1,596 848 960 3,573 71 602 314 367 1,354 4,927
Tampa - Miami 199 2,187 1,215 1,534 5,135 66 752 456 525 1,799 6,934
Lakeland Area-Southeast Florida 298 2210 1116 1388 5,012 101 743 364 475 1,683 6,695
Lakeland - West Palm Beach 112 762 397 422 1,693 34 248 107 141 530 2,223
Lakeland - Ft Lauderdale 81 653 327 394 1,455 30 213 102 128 473 1,928
Lakeland - Miami 105 795 392 572 1,864 37 282 155 206 680 2,544
Total 5,304 31,802 16,811 22,396 76,313 1,673 14,531 7,267 10,193 33,664 109,977
% of Total Trips 4.82% 28.92% 15.29% 20.36% 69.39% 1.52% 13.21% 6.61% 9.27% 30.61% 100.00%

Source: Louis Berger, 2018

 

Growth Forecast

 

Louis Berger predicts future auto market based on AirSage data and applies average annual growth historical rate of growth at the relevant counter stations to make projections of future growth as shown in Table 3-19. The future auto market between key origins and destinations in the Phase 2 Study market will increase with CAGR of 2.0%.

 

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Table 3–19 Annual Auto Person Trips Projection

 

City Pair
 2017 2045 CAGR
Tampa - Orlando
 
 20,749,188 38,593,355 2.2%
Lakeland - Orlando
 
 5,735,777 10,668,509 2.2%
Tampa - Lakeland
 
 5,801,778 10,791,269 2.2%
Tampa Area-Southeast Florida
 
 4,821,022 9,109,907 2.3%
Tampa - West Palm Beach
 
 958,363 1,810,944 2.3%
Tampa - Ft Lauderdale
 
 1,548,900 2,926,834 2.3%
Tampa - Miami
 
 2,313,759 4,372,129 2.3%
Lakeland Area-Southeast Florida
 
 2,402,741 3,792,195 1.6%
Lakeland - West Palm Beach
 
 799,007 1,261,056 1.6%
Lakeland - Ft Lauderdale
 
 693,228 1,094,107 1.6%
Lakeland - Miami
 
 910,506 1,437,032 1.6%
Total
 
 39,510,506 72,955,235 2.2%
Source: Louis Berger, 2018
      

 

3.3.5     Airport Access Trips

 

Sections above all discuss non-airport-access trips (intercity trips), which consist a majority of trips that can potentially be affected by introduction of Brightline Service. The Phase II Study also analyzed airport access trips to and from Orlando Airport given its proximity to Disney as well as to the other short distance market stations such as Tampa. The airport access trips include two categories--choice trips and captive trips-defined as below:

● Captive trips- prepaid through travel packages etc

● Choice trips – travelers could choose from the array of options available including Brightline service.

 

Louis Berger developed 2017 airport-access estimates trips based on data sourced from a 2002 Investment Grade Ridership Study prepared by AECOM and Wilbur Smith Associates for Florida High Speed Rail Authority. Further, Louis Berger team forecasts the future airport-access trips by applying growing trend of Disney visitation and MCO enplanements mentioned in the former sections. The current and future projections of airport access trips are choice trips presented in the table below:

 

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Table 3–20 Current And Future Airport Access Choice Trips By Mode

 

      2017     2045 2017-
2045
CAGR
OD Pairs Auto Rental Shuttle Taxi Total Total
Disney- MCO 393,121 1,718,085 800,802 713,442 3,625,451 5,552,800 1.5%
Southwest Orlando (Excluded Disney)-MCO 1,587,896 89,059 89,059 460,459 2,226,472 4,230,357 2.3%
Tampa-MCO 635,269 35,630 35,630 184,216 890,745 1,692,439 2.3%
Lakeland-MCO 193,718 10,865 10,865 56,175 271,623 516,090 2.3%
Total 2,810,004 1,853,639 936,356 1,414,292 7,014,291 11,991,686 1.9%

Source: Louis Berger, 2018

 

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4.0          Brightline Travel Demand Model
 
4.1          Overview of Methods
 
The demand for Brightline service was estimated through a process that involved three distinct phases:
 
●
Primary market research. Data describing the travel patterns, behavior and attitudes of potential users of the service was collected through surveys, thereby providing the underlying basis for analyzing future ridership.
●
Mode choice model estimation. Survey data was used to develop mathematical models of mode choice based on various modal service attributes and individual characteristics.
●
Travel demand model development. Travel demand models were constructed for both long and short-distance travel. Service attributes of the various mode alternatives serving both markets were compiled and interacted with mode choice models to generate market share estimates under both build and no build conditions, thereby providing estimates of diversions to Brightline service.
 
4.2          Survey Design
 
The survey instrument included the following types of questions:
 
●
Screening Questions – Screening questions determine whether a person is qualified to participate in the survey.
●
Reference trip – Data were collected to characterize the respondent’s most recent trip within the study area. Reference trip information included trip purpose; travel party composition; trip origin and destination; mode of travel.
●
Choice Exercise – Respondents were presented with a series of hypothetical choice scenarios that include different travel options including Brightline.  The reference trip described in previous questions is used to frame the hypothetical choice experiment.
●
Induced Travel - It is expected that a portion of Brightline ridership will be trips that would not be made without Brightline.  Respondents were asked about their interest in the additional intercity travel between Southeast and Central Florida and within Central Florida.
●
Socioeconomic/demographic characteristics – Socioeconomic characteristics include age, gender, household size, household income, education and employment status.
 
4.2.1      Screening
 
To be qualified to participate in the survey, potential respondents were required to meet the following criteria:
 
●
Age 18 or older
●
Within the past 6 months, the respondent must have traveled at least once between an origin and a destination pair that would be served by the proposed Brightline service. Locations with stops included in the survey include Miami, Fort Lauderdale, West Palm, Orlando International Airport, Disney World, Lakeland, and Tampa.
 

 

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4.2.2      Reference Trip
 
Respondents who meet the screening criteria were asked to describe their most recent qualified trip.  The reference trip provides a realistic context for the stated choice exercise.
 
Questions regarding the most recent trip:
 
●
Origin - Location and Type of Location (i.e., home, work, school, airport, hotel/motel, other).
●
Destination – Location
●
Trip Purpose – Leisure/Pleasure, Visiting Family/Friends, Personal Business, Company Business, Convention, Other
●
Travel Party - Number of persons in travel party, travel party composition, special needs.
●
Mode of Travel (customized based on OD pair) – Personal Car, Rental Car, Bus/Shared Shuttle, Amtrak Train, Privat
●
e Shuttle, Taxi, Rideshare, Air, Disney Magical Express
●
Number of nights spent at destination
●
Trip payment responsibility - Business trips are typically reimbursable by the employer or business; Company travel policy effect on mode choice for reimbursed travelers.
●
Travel package – Travelers who purchased transportation as part of travel package that includes both lodging and transportation may not know which portion of the cost is for transportation.
●
Reasons for choosing auto (for auto users only) – To identify en-route and destination captives who need a car to make stops along the way or at their destination.
 
4.2.3      Choice Exercise
 
The purpose of the choice exercise was to explore the survey respondent’s interest in various travel mode options, including Brightline, based on in-vehicle travel time, access time, frequencies, cost and other mode specific attributes. The choice exercise is the principal section of the survey and the resulting data forms the basis of the ridership forecast.
  
The choice exercise section started with a general introduction of the Brightline service between Southeast Florida, Orlando and Tampa. The overview included a listing station locations and levels of service for Smart and Select Service. The overview did not present the Brightline travel time, headways and fare assumptions used in the study because the choice exercise that follows provides varying levels of these operating characteristics in each choice set. The introduction is followed by instructions about how to complete the choice exercise. As part of the exercise, each respondent was presented with 10 hypothetical choice sets. Each choice set included four of the following five main mode options:
 
●
Brightline
●
Auto (Personal Car for respondents who made the reference trip by car or who indicated that a car was available for their trip; Rental Car will be used for respondents who indicated that no car was available)
●
Express Bus/Shared Shuttle Bus
●
Air (only for trips between Southeast Florida and Central Florida)
●
AMTRAK Train (only for trips between Southeast Florida and Central Florida)
●
Private Shuttle/Taxi/Rideshare (only for trips between Tampa/Lakeland/Disney World and Orlando International Airport)
 

 

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Each mode option was described using all or some of the following characteristics or attributes:
 
●
Access Time (for public modes only) – Time to Travel to the main mode
●
In Vehicle Travel Time – Travel Time in main mode
●
Frequency (for public modes only) – Number of trains, flights, buses per hour
●
Cost – Fare(s) for public modes and private car/shuttle services; gas and toll for personal car; gas, toll and rental fee for rental car; parking for trips ending at Disney World or Orlando International Airport.
 
FIGURE 4–1 EXAMPLE STATED CHOICE EXERCISE
 
 
Note:  Travel times and fares shown above are examples of a single possible choice experiment given to respondents. Travel times and fares varied randomly within applicable ranges in each of the eight choice experiment shown to survey respondents.
 
In each choice set, respondents were asked to choose between the four options. They were instructed to make the choice considering a trip with the same origin and destination pair as the reference trip and the same characteristics as that trip in terms of purpose and travel party.  Responses provide insight into how travelers value the travel time savings, service frequency and amenities that Brightline offers while taking into account the characteristics of other available modes of travel.
 
To make the exercise more realistic, travel times and other service characteristics for existing modes were customized based on each respondent’s origin and destination pair.  Louis Berger conducted extensive research on existing travel options between Southeast Florida, Orlando and Tampa.

 

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4.2.4      Induced Travel
 
Intercity rail often has the potential to generate induced travel, which are trips that would not take place if the service were not available.  To explore this potential, respondents who stated that they would take Brightline for their reference trip were asked if they think they would  travel more  often  to  any  of  the  destinations  served  by  the Brightline if  the  service  were available.
 
4.2.5      Socioeconomic Characteristics
 
Respondents were asked to report socioeconomic and demographic characteristics including the following:
 
●
Age
●
Gender
●
Household size
●
Household income
●
Education
●
Number of working adults in household
●
Number of motor vehicles in household
 
4.3          Survey Implementation and Summary
 
4.3.1     Survey Implementation and Summary for Brightline Phase I Study
 
4.3.1.1      Survey Sample and Administration
 
The survey instrument was targeted at intercity travelers residing within one of the three following segments:
 
●
Greater Orlando residents
●
Southeast Florida residents
●
Non-residents
 
The data was collected with a web survey and an intercept survey using tablet computers.  The web survey was sent solely to residents of Greater Orlando and Southeast Florida.  All non-resident data was collected with the intercept survey.
 
The total sample size target was 1,400, with specific targets by geographic segment and mode designed to obtain statistically significant samples for cross-tabulations.  Quotas were incorporated in the web survey instrument in an attempt to obtain sufficient records from non-auto modes. The overall target was exceeded (Table 4.3-5).   While geographic and mode specific targets (non-residents and bus users) were not met, we still collected a sufficient number of records for these segments.
 

 

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Table 4–1 Sample Size By Place Of Residence and Mode of Transportation
 
City Pair
Target
Actual
Geographic Segment
    
Southeast Florida
800
1,251
Greater Orlando
200
338
Non-residents
400
271
Mode
    
Auto
700
1,256
Air
200
196
Train
250
224
Bus
250
157
Total
1,400
1,860
 
The web survey was conducted from April 14, 2012 to April 21, 2012.  An invitation to the web survey was sent to 65,301 e-panel members, 5,100 of which responded, which is a response rate of 8 percent.  One quarter of respondents (1,261) were qualified and completed the survey.
 
In addition, an intercept survey was conducted from April 10, 2012 to April 15, 2012 at the following locations:
 
●
Miami Airport
●
Miami Airport Car rental facility & people mover
●
Tri-Rail Miami Airport
●
Fort Lauderdale Airport
●
Tri-Rail Fort Lauderdale
●
Tri-Rail West Palm Beach
●
Orlando Airport
●
Canoe Creek Service Plaza
 
A total of 1,255 persons were intercepted and 599 persons qualified and completed the survey.
 
4.3.1.2 Summary Tabulation & Frequencies
 
The following section presents an overview of the data collected with the internet and intercept survey instruments.  A total of 1,860 completed surveys were collected, including 1,261 from the internet survey and 599 from the intercept survey.
 
Reference Trip
 
To provide context for the SP experiment, respondents were asked to identify and describe an intercity reference trip within the study area.  The reference trip is defined as the respondent’s most recent or typical trip between the O-D pair that the respondent traveled most often within past six months or one year.  Data about the reference trip include trip purpose, mode of transportation, party size and composition, bags, and person or entity responsible for payment of the trip.
 
More than half of the respondents (54.4 percent) reported a reference trip between Southeast Florida and Central Florida, which is called a long distance trip in this study (Table 4.3-6).  The remaining respondents reported

 

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reference trip within Southeast Florida or a short distance trip. The characteristics of long distance and short distance reference trips are discussed separately below.
 
Table 4–2  Number of Respondents by Origin and Destination Pairs
 
OD Pairs
Total
Percent of Total
Short Distance Trips
848
45.6%
Miami - Fort Lauderdale
397
21.3%
Miami - West Palm Beach
150
8.1%
Fort Lauderdale-West Palm Beach
301
16.2%
Long Distance Trips
1,012
54.4%
Miami- Orlando
384
20.6%
Fort Lauderdale – Orlando
359
19.3%
West Palm Beach – Orlando
269
14.5%
Grand Total
1,860
100%
 
About 60 percent of respondents chose a leisure trip as their reference trip (Table 4-3). (Company) business trips and combination of both business and Leisure accounted for 15 and 9 percent of the reference trips, respectively.
 
Table 4–3 Number of Respondents by Reference Trip Purpose
 
OD Pairs
Short Distance Trips
Long Distance Trips
Total
 
Number
Percent
Number
Percent
Number
Percent
Leisure/Pleasure
481
56.7%
701
69.3%
1182
63.6%
Business
122
14.4%
151
14.9%
273
14.7%
Combination of Business and Leisure
66
7.8%
108
10.7%
174
9.4%
Commute
44
5.2%
8
0.8%
52
2.8%
Other
135
15.9%
44
4.3%
178
9.6%
Total
848
100%
1,012
100%
1860
100%
 
Trips made by personal car or rental car combined accounted for more than 60 percent of the total reference trips (Table 4-4).
 
Table 4–4 Number of Respondents by Reference Trip Mode
 
Mode
Short Distance Trips
Long Distance Trips
Total
 
Number
Percent
Number
Percent
Number
Percent
Private car (not a rental)
574
67.7%
487
48.1%
1061
57.0%
Rental car
35
4.1%
115
11.4%
150
8.1%
Air
35
4.1%
189
18.7%
224
12.0%
Train
16
1.9%
77
7.6%
93
5.0%
Bus
148
17.4%
85
8.4%
233
12.5%
Shared passenger van
41
4.8%
40
4.0%
81
4.4%
Other
574
67.7%
19
1.9%
593
31.9%
Total
848
100%
1,012
100%
1860
100%
 
Interest in Brightline
 
After the eight stated-choice experiments, respondents were asked directly if they would take the service if it would have been available for travel to the destination of their reference trip.  Respondents were asked to assume the base

 

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travel time and a one-hour headway, and the higher range fare. Respondents who declined to take the service at the higher range fare were asked whether they would take the service at the base fare.
 
Most business (78.3 percent) and non-business (69.7 percent) travelers expressed an interest in the All Aboard Florida service for travel between Southeast and Central Florida at the proposed base fare level or higher (Table 4.3-17).  Business travelers were more likely to respond positively than non-business travelers to the highest fare level.  Also for trips within Southeast Florida, most business (57.6 percent) and non-business (55.1 percent) travelers expressed interest in the All Aboard Florida service at the proposed base fare level or higher.  However, as many 22.6 percent of non-business travelers and 18.1 percent of business travelers state that they would not take the service at any fare.
 
The most common reason for not taking the All Aboard Florida service for travel between Southeast and Central Florida is the need for a car at the destination (Figure 4.3-5).  Half of travelers strongly agreed that car dependency was one of the reasons that they would not choose to take the All Aboard Florida service at the base fare.  For trips within Southeast Florida, the most common reason for not taking the new service was access time.  More than half (52.8%) of travelers strongly agree that based on the station location that they were provided, it would take too long to get to the All Aboard Florida service.  Car dependency was the second most common selected reason for not taking the new service for travel within Southeast Florida (i.e., 46.9 percent strongly agree).  Almost half of business travelers traveling within Southeast Florida indicated (i.e., agree or strongly agree) that the train’s presented frequency – one train per hour – was (one of) the reason(s) for not taking the new service.
 
4.3.2     Survey Implementation and Summary for Brightline Phase II Study
 
4.3.2.1 Survey Sample and Administration
 
The data collection was conducted from June 18, 2018 to July 2, 2018. The survey was conducted using Computer Assisted Self Interviewing (CASI) techniques as part of which respondents accessed the online survey and completed the survey on their computer or phone without an interviewer.  Internet surveys have been a growing trend in travel survey research due to the lower costs and faster data collection.  Respondents to the internet survey were recruited using an ePanel provider. The ePanel, which is managed by a market research firm, is composed of persons who have expressed interest in participating in survey research and receive payment for their participation on a survey by survey basis.  The survey was distributed to residents of the market area including Southeast Florida, Greater Orlando and the Tampa Bay Area. Because visitors are a key part of the leisure travel market, the resident sample was supplemented with a visitor sample.  To capture visitors to Florida, the survey was distributed to the residents of regions from which Florida draws the largest number of visitors, which are the Atlanta, Boston, Chicago and New York metro areas. Randomly selected members of the ePanel residing in any of these market areas received an invitation to participate in the survey directly from the ePanel provider.
 
A total of 1,983 completed surveys were collected, including 1,565 resident surveys and 418 visitor surveys. Residents of Southeast Florida completed 640 surveys while Greater Orlando and Tampa Bay area residents completed 412 and 438 surveys, respectively (Table 4-5).  Sufficient sample sizes were also obtained for each trip

 

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purpose with a total of 365 respondents with business trips and 1,618 with non-business/leisure trips.  The visitor sample is composed of leisure trips (Table 4-6).
 
Table 4–5 Resident Sample Size By Place Of Residence
 
Place of Residence
Number of Respondents
Percent of Respondents
Southeast Florida
640
32.3%
Greater Orlando
412
20.8%
Tampa Bay
438
22.1%
Polk County
75
3.8%
Total
1,565
100%
 
Table 4–6 Resident Sample Size By Trip Purpose
 
Purpose
Number of Respondents
Percent of Respondents
Business
365
18.4%
Leisure/Non-Business
1,618
81.6%
Total
1,983
100%
Source: Louis Berger, 2018
 
4.3.2.2 Summary Tabulation & Frequencies
 
Reference Trip
 
At the beginning of the survey, respondents were asked a series of questions about past travel between origin and destination pairs that would be served by Brightline.  Later in the survey, this reference trip provided context for the stated choice exercise as respondents were instructed to complete the stated choice exercise keeping in mind this actual trip, including the trip’s purpose, its origin and destination and travel party.
Of the 1,983 completed surveys, about half of the respondents reported a reference trip between Southeast Florida and Central Florida while the remaining half reported a reference trip within Central Florida (Table 4-7).  Trips to or from Orlando International Airport accounted for 23 percent of the reference trips.  About one fifth of the reference trips were between Orlando International Airport and Disney World.
 
Table 4–7  Number of Respondents by Origin and Destination Pairs
 
OD Pairs
Total
Percent of Total
Between Southeast Florida - Central Florida
981
49.5%
Southeast Florida - Greater Orlando
628
31.7%
Southeast Florida - Polk County
42
2.1%
Southeast Florida - Tampa Bay
311
15.7%
Within Central Florida
1,002
50.5%
Greater Orlando - Polk County
60
3.0%
Greater Orlando - Tampa Bay
563
28.4%
Orlando International Airport - Disney World
379
19.1%
Grand Total
1,983
100%
 
About 60 percent of respondents chose a leisure trip as their reference trip (Table 4-8). (Company) business trips and trips to visit family or friends accounted for 18 and 15 percent of the reference trips, respectively.

 

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Table 4–8 Number of Respondents by Reference Trip Purpose

 

Purpose Southeast Florida to Central Florida Within Central Florida Total
  Number Percent Number Percent Number Percent
Leisure/Pleasure 458 46.7% 744 74.3% 1,202 60.6%
Visit Family/Friends 186 19.0% 110 11.0% 296 14.9%
Personal Business (e.g., medical appointment, funeral) 26 2.7% 20 2.0% 46 2.3%
Company Business 265 27.0% 90 9.0% 355 17.9%
Convention 13 1.3% 9 0.9% 22 1.1%
Other 33 3.4% 29 2.9% 62 3.1%
Total 981 100% 1,002 100% 1,983 100%

 

Trips made by personal car or rental car combined accounted for 57 percent of the total reference trips (Table 4-9).

 

Table 4–9 Number of Respondents by Reference Trip Mode

 

Mode Southeast Florida to Central Florida Within Central Florida Total
  Number Percent Number Percent Number Percent
Personal Car 456 46.5% 453 46.7% 909 45.8%
Rental Car 74 7.5% 149 15.4% 223 11.2%
Air 111 11.3% - - 111 5.6%
Bus/Shared Shuttle 102 10.4% 54 5.6% 156 7.9%
Private Shuttle/Taxi/Rideshare 138 14.1% 159 16.4% 297 15.0%
Amtrak Train 31 3.2% - - 31 1.6%
Disney Magical Express 0 0.0% 86 8.9% 115 5.8%
Other 69 7.0% 68 7.0% 141 7.1%
Total 981 100% 969 100% 1,983 100%

 

Interest in Brightline

 

While the choice exercise data formed the basis for ridership forecasts, the survey instrument also directly asked respondents if they would choose the Brightline service for their reference trip. While this direct question does not include any information about alternative travel options, it offers additional insight in understanding travelers’ interest in the Brightline service and their willingness to pay for the travel time savings and service amenities.

 

About 70 percent of respondents with reference trips between Southeast Florida and Central Florida and 76 percent of those with reference trips within Central Florida indicated that they would choose Brightline service if it were available. About 42 percent of respondents indicated that they would pay a premium fare for Select Service, which was defined as extra wide 21-inch seats; choice of single seats, side by side seats and four seats facing a table; complimentary beverages and snacks and access to the premium select lounge in the station.

 

Induced Travel

 

About 41 percent of the resident sample indicated that they would, or probably would, travel more often if the Brightline service were available. The majority of Greater Orlando and Tampa Bay Area residents indicate that they would make additional trips to Southeast Florida (72 and 65 percent, respectively).

 

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4.4 Mode Choice Model Estimation

 

Data from the hypothetical choice experiments was evaluated using discrete choice analysis techniques to determine the factors driving mode choice decisions. The anticipated differences in travel behavior distinguished by travel distance (long and short-distance travel) and by trip purpose (business/non-business travel) required the iterative development and testing of four separate mode choice models.

 

4.4.1 Conceptual Overview

 

The basic concept driving discrete choice analysis is the idea of utility maximization. Utility in economics is described as the satisfaction an individual gains from the consumption of goods or services. Each alternative in a decision maker’s choice set provides a level of utility that is both a function of the attributes specific to that alternative, as well as the decision maker’s own characteristics.

 

The utility function derived for each alternative in a choice set is typically characterized by a linear combination of explanatory variables as shown below and will also generally comprise a constant term, often termed the alternative specific constant (ASC) or mode constant. The mode constant reflects the relative preference towards a given alternative among the set of choices available, after accounting for and holding the effects of the other variables in the utility function fixed. (The example below is for 3 competing modes—all modes relevant to each market were considered in our study).

 

UAAF = ASCAAF+ (β1× IVTTAAF) + (β2× OVTTAAF) + (β3× CostAAF) + ... (1)
UAUTO = ASCAUTO+ (β1× IVTTAUTO) + (β2× OVTTAUTO) + (β3× CostAUTO) + ... (2)
UAIR = ASCAIR+ (β1× IVTTAIR) + (β2× OVTTAIR) + (β3× CostAIR) + ... (3)
URAIL = ASCRAIL+ (β1× IVTTRAIL) + (β2× OVTTRAIL) + (β3× CostRAIL) + ...  (4)
UBUS = ASCBUS+ (β1× IVTTBUS) + (β2× OVTTBUS) + (β3× CostBUS) + ... (5)

Where:

 

IVTT = In-Vehicle Travel Time

OVTT = Out-of-Vehicle Travel Time

 

The magnitudes of coefficients (β1-β3) which represent the relative importance of each modal attribute such as time and cost, are obtained by statistically evaluating the tradeoffs respondents from the SP survey made in their hypothetical choice experiments. The estimated coefficients are interacted with actual values of modal attributes to calculate probabilities of each mode choice using the nested logit formulation shown below:

 

(6)
   
(7)

 

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Where:

U = Utility equation for a given mode of travel (See equations 1-5)

θP = Nesting coefficient (0 < θP < 1)

ΓP = Public nest logsum

 

(8)

 

And the conditional probability of choosing AAF (Brightline), Rail, Air or Bus given the selection of a public mode of travel is estimated using the general expression below:

 

(9)

 

4.4.1.1 Value-of-Time

 

Value-of-time (VoT) is the estimated price an individual is willing to pay to save time on a given journey. This measure compares the estimated coefficients of travel time variables against the cost coefficient, and provides a useful summary metric to evaluate the conceptual consistency of an estimated model. The $/hr. VoTs represent the rate at which individuals are willing to substitute time and cost. This measure is typically calculated as the ratio of the travel time coefficient (converted from minutes to hours) to the cost coefficient as shown in equation 10.

 

(10)

 

The United States Department of Transportation (U.S. DOT) has provided guidelines for recommended values of time based on estimated hourly wages, trip length and trip purpose. The Louis Berger Team used these guidelines to estimate the corresponding set of anticipated VoT ranges specific to the income composition of the survey data collected (Table 4-10). These guidelines were used to evaluate the conceptual consistency of estimated models.

 

Table 4–10 U. S. DOT Guidelines

 

Category Plausible VOT Range
Low Middle High
Local Travel Non-Business $6.97 $9.95 $11.94
Business $19.10 $23.87 $28.65

Intercity Travel

 Non-Business $12.06 $14.07 $18.09
Business $18.31 $22.89 $27.46

 

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4.4.2    Summary of Model Estimation Process (Phase I Study)

 

The US DOT guidelines above point to distinct travel behaviors based on travel distance. Therefore, the Louis Berger Team estimated two separate sets of models for both the long and short-distance markets. Respondents who traveled from Central Florida (the region around Orlando) to Southeast Florida (West Palm Beach, Fort Lauderdale, or Miami) were categorized as long-distance travelers while respondents traveling within the Southeast Florida area were categorized as short-distance travelers.

 

4.4.2.1 Long Distance Model

 

Due to the relatively small sample size of business travelers, the Louis Berger Team estimated mode choice models using a pooled sample of both business and non-business traveler data while using interactions with a business travel categorical variables as the mechanism for segmentation by trip purpose. This approach allowed a direct comparison of the practical and statistical differences between business and non-business travelers, and thereby helped identified explanatory variables that should not be segmented by trip purpose, and that should remain common among both groups of travelers. The effect of household income on price sensitivity was accounted for by applying the same pooled data segmentation approach described above to statistically distinguish the response to cost across three broad income segment groups: low income (<$50,000), medium income ($50,000 - $100,000), and high income (>$100,000). Figure 4-2 presents the nesting structure applied in model application while Table 4-11 presents the corresponding model coefficients distinguished by trip purpose.

 

Table 4-12 provides a breakdown of household income segment groupings from the survey. These household income distributions were evaluated against and corroborated by an independent origin-destination (O-D) survey that was also conducted to support this ridership demand study.

 

Figure 4–2 LONG DISTANCE MODE CHOICE MODEL NESTED LOGIT STRUCTURE

 

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Table 4–11 Long Distance Mode Choice Models (PHASE I Study) 

 

Variable Non-Business Business
Alternative Specific Constants (ASC)    
AAF (Brightline) 0.00000 0.00000
Air -0.43860 -0.22748
Rail -0.46610 -0.17348
Bus -0.88768 -0.99021
Private Auto -0.21814 -0.12251
Rental Car -1.88400 -1.41145
     
LOS Variables    
Access & Egress Time -0.00396 -0.00559
Headway -0.00098 -0.00152
In-Vehicle Travel Time (IVTT) -0.00394 -0.00394
Cost - Low Inc (<$50,000) (2012 $ / 2016 $) -0.01424 / -0.0136 -0.01424 / -0.0136
Cost - Medium Inc ($50,000-$100,000) (2012 $ / 2016 $) -0.01263 / -0.0121 -0.00824 / -0.0079
Cost - High Inc (>$100,000) (2012 $ / 2016 $) -0.01175 / -0.0112 -0.00542 / -0.0052
     
Nesting Coefficient θ 0.53179 0.53179
     
Implied IVTT VOT (2012 $/Hr / 2016 $/Hr)    
Low Inc (<$50,000) $16.62 / $17.37 $16.62 / $17.37
Med Inc ($50,000-$100,000) $18.74 / $19.58 $28.71 / $30.01
High Inc (>$100,000) $20.13 / $21.05 $43.69 / $45.20

 

Table 4–12 Household Income Distribution
 
Income Group Trip Purpose
Non-Business Business
Low Inc <$25K 6.5% 27.1% 4.3% 11.3%
$25-50K 20.6% 7.0%
Med Inc $50-75K 22.0% 43.9% 21.7% 48.7%
$75-100K 21.9% 27.0%
High Inc $100-150K 18.6% 29.0% 24.3% 40.0%
$150-200K 5.8% 9.6%
>$200K 4.5% 6.1%

 

The statistical and practical similarities in IVTT coefficients across both travel segments comports with structural rationale that both sets of travelers have similar disutility of additional travel time but vary in their means or willingness to pay for a faster mode of travel. The results in Table 4-3 however, show that long-distance business travelers have a higher disutility of access time relative to the non-business market segment.

 

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With the exception of the lowest income segment, business travelers display a lower disutility of travel costs and this results in both higher and wider arrays of VoT when compared to the long-distance non-business travel market. The cost coefficients of these model specifications were modified to account for increases in the value of time since the 2012 survey date, based on CPI increases observed over that time period.

 

4.4.2.2    Short Distance Model (Phase I Study)

 

The pooled data approach used to estimate the long-distance models did not generate similar analytical advantages for the short-distance travel market and the Louis Berger Team therefore elected to estimate separate nested logit models for the business and non-business markets. Figure 4-3 shows the nesting structure used to estimate the short-distance models while Table 4-13 presents the corresponding model specifications.

 

Figure 4–3 SHORT DISTANCE MODE CHOICE MODEL NESTED LOGIT STRUCTURE

 

Table 4–13 Long Distance Mode Choice Models
 

Variable Non-Business Business
Alternative Specific Constants (ASC)    
AAF (Brightline) 0.00000 0.00000
Rail -0.22625 -0.21261
Bus -0.61782 -0.84988
Private Auto 0.44180 0.58282
LOS Variables    
Access & Egress Time -0.00932 -0.01352
Headway -0.00207 -0.00403
In-Vehicle Travel Time (IVTT) -0.00774 -0.01115
Cost (2012 $ / 2016 $) -0.04022 / - 0.0400 -0.04184 / - 0.0385
Nesting Coefficient θ 0.33191 0.51709
     
Implied IVTT VOT (2012 $/Hr / 2016 $/Hr) $11.54 / $12.07 $15.99 / $16.72
Implied Access/Egress VOT (2012 $/Hr / 2016 $/Hr) $13.90 / $14.53 $19.39 / $20.27

 

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As was the case in the long-distance models, business travelers display a relatively higher value-of-time for both in-vehicle and out-of-vehicle travel time. It should also be noted that following testing of several model specifications, the final agreed upon model did not segment the income effect on cost sensitivity as the resulting coefficient estimates were both insignificant at both practical and typical statistical terms. The cost coefficients of these model specifications were also modified to account for increases in the value of time since the 2012 survey date, based on CPI increases observed over that time period.

 

4.4.3    Summary of Model Estimation Process (Phase II Study)

 

Louis Berger developed a similar set of mode choice models for the Phase II Study using the survey data collected in 2018 specifically to understand travel behavior of potential riders making trips between Tampa-Southeast Florida (Long distance) and within the Tampa-Orlando corridor (Short distance). In addition to representing business and non-business trips as discussed in Phase I Study section, an additional market segment separately recognizing airport access trips was also included in this analysis. Table 4-14 summarizes the corresponding collection mode choice models estimated for all the Phase II Study market segments.

 

Table 4–14 Phase ii Study Mode Choice Models

 

  Long Distance Short Distance Airport Access
  Business Non-Business Business Non-Business Business Non-Business
auto 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
air -0.0804 -0.7790        
Brighline 0.1652 -0.0064 0.0414 0.1196 0.6610 0.6610
rail -0.4748 -0.6002 -0.4242 -0.5128    
bus -0.5487 -0.8568 -0.4817 -0.9725    
rentalcar -0.5808 -0.6826 0.8827 -0.2806 -0.1141 -0.1141
private shuttle/taxi     -0.4238 -1.9499 -0.3449 -0.3449
Shared Van     -0.7121 -0.6708 0.0273 0.0273
Access/Egress Time -0.0053 -0.0055 -0.0181 -0.0188 -0.0100 -0.0220
Headway -0.0009 -0.0009 -0.0023 -0.0024 -0.0027 -0.0037
In-Vehicle Travel Time (IVTT) -0.0035 -0.0037 -0.0091 -0.0094 -0.0146 -0.0146
Cost -0.0049 -0.0073 -0.0202 -0.0349 -0.0176 -0.0293
Rental Cost -0.0059 -0.0087 -0.0038 -0.0066 -0.0013 -0.0021
             
nest 0.7500 0.7500 0.4964 0.5483 0.6016 0.6016
             
IVTT VOT $43.29 $30.18 $26.90 $16.15 $49.93 $29.97

 

4.5 Travel Demand Model Development

 

The Louis Berger Team constructed a travel demand model to represent travel patterns of the Central and Southeast Florida regions as described in preceding sections. To operationalize the mode choice model the Louis Berger Team assembled a database of level of service information for each mode of travel. In-vehicle travel times, operating costs, fare costs, and station access times were developed for each origin and destination pair for each mode of travel. Using this level of service data, the nested mode choice model representing the travel behavior of each market segment was applied to the corresponding trip table to derive travel utilities and implied mode shares for all O-D pairs in both the long and short-distance travel markets. Adjustments to the mode constants were made to match predicted shares

 

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against the targets implied by trip table mode splits. Once calibrated, the adjusted model specifications were applied to a build scenario that included the Brightline service as an additional travel alternative. The difference between the build and no-build scenarios was used to estimate the diversions from existing modes and arrive at an initial estimate of Brightline ridership. The final ridership forecast also included an estimate of the potential induced ridership accruing to the introduction of Brightline service in the corridor.

 

4.5.1    Level of Service Assumptions

 

4.5.1.1 Level of Service Assumptions for Brightline Phase I Study

 

The Louis Berger team developed level of service (LOS) profiles for each of the intercity travel modes considered in this study. As outlined above, these LOS variables would be applied to the mode choice model equations described earlier to estimate travel utilities for each available mode. Given the structure of Louis Berger’s mode choice models, the LOS variables of interest include the following:

 

● Private auto

 

– In-Vehicle Travel Time

 

– Out-of-pocket travel cost – includes cost of gas and tolls and is divided by number of vehicle occupants

 

● Public modes

 

– Service headways (minutes)

 

– Out-of-Vehicle Travel Time (OVTT) – includes access and egress travel time from stations and terminal time

 

– In-Vehicle Travel Time (IVTT)

 

– Fares

 

– Access/egress costs

 

–

 

Auto Level of Service Assumptions

 

Auto is the predominant mode of travel in the corridor and the level of service variables describe the trip lengths and costs that travelers typically encounter. Knowledge of typical travel times and costs is a factor in the traveler’s choice of modes. Because of the relatively short-distances involved in the Southeast Florida market and the dominant position of this mode in the candidate travel market, Louis Berger took care to develop conservative assumptions for the level of service parameters so as not to overestimate the willingness of current auto travelers to switch to Brightline service.

 

Travel Time

 

For the short-distance Southeast Florida market, Louis Berger utilized travel time data extracted from the SERPM 6.7 model for the 2010 base year. The following steps were employed in the development of the travel time estimates.

 

● Zone-to-zone travel times for each of the 4,106 TAZs in the SERPM 6.7 were assembled for all trip purposes for peak and off-peak periods.

 

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● The TAZ data base was clipped to exclude zones outside the catchment areas and limit the evaluation to only those trips and travel times between the catchment areas.

● SERPM TAZs were aggregated to the 398 analysis zones described in Section 2.10.

● Average (trip-weighted) zone-to-zone times were calculated for peak and off-peak periods for intercity journeys among the 398 zones.

 

Table 4-15 shows an example of the estimated average travel times for each city pair. The table shows that the off-peak uncongested times derived from SERPM are consistent with off-peak travel times from the Google Maps service; and that the composite times fall between the map service estimates for peak and off-peak travel times. Louis Berger performed other spot checks of travel times and distances and found the dataset to be generally consistent with published map service estimates.

 

Table 4-15 also includes travel times for the long-distance travel market, these travel times were obtained by detailed inquiry using Google API. Louis Berger extracted zone to zone travel times from Google Maps using Google Maps API. These travel times were then evaluated against uncongested and congested times extracted from the Central Florida Regional Planning Model (CERPM) and the SERPM for validation.

 

Table 4–15 Highway Travel Times
 
City Pair Distance Off-Peak (Uncongested) Travel Time AM Travel Time PM Travel Time
West Palm Beach - Miami 73 mi 91 93 99
West Palm Beach - Fort Lauderdale 46 mi 60 61 65
Fort Lauderdale - Miami 32 mi 44 45 48
Orlando – Miami 241 mi 216 221 222
Orlando – Fort Lauderdale 216 mi 194 196 197
Orlando – West Palm Beach 179 mi 170 172 173

Source: Louis Berger analysis of data from Google Maps API, 2017

 

Travel Costs

 

Given the large size of the intercity auto market, an important aspect in the development of the forecast is the identification of a sound assumptions for out-of-pocket auto operating costs, which are based in large part on fuel prices. Louis Berger reviewed the latest EIA Annual Energy Outlook (2017). This outlook provides the latest U.S. average gasoline pump price projection. Figure 4-4 shows the 2017 projection for long term gas prices most appropriate for use in the model. The 2040 projections are $3.90 for the reference case, $2.61 for the low case, and $5.04 for the high case.

 

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Figure 4-4 Eia Gas Prices (2016 $ per Gallon)

 

 

The Louis Berger Team also conducted a detailed review of potential toll costs incurred by auto users in the Brightline service travel corridor. Based on the various O-D pairs, the Louis Berger Team compiled an estimate of toll costs based on published rates obtained from the Florida Turnpike. Table 4-16 provides the resulting estimate of toll costs represented in per mile terms for each of the major movements in the corridor. These results were incorporated into the travel demand model calculations for auto travel utility.

 

Table 4-16 PerMile Toll Costs, Brightline Study Area (2016 $) 

  Distance (miles) Avg Cost $/Mile
Orlando - W. Palm Beach 171 0.063
Orlando - Ft. Lauderdale 213 0.068
Orlando - Miami 236 0.084
W. Palm Beach - Ft. Lauderdale 53 0.069
W. Palm Beach - Miami 72 0.138

 

Bus Level of Service Assumptions

 

Louis Berger reviewed bus schedules of key players in the Florida intercity bus market (Greyhound, Megabus, and Red Coach). Tables 4-16 and 4-17 represent the travel time, service frequency and travel cost assumptions applied to both business and non-business travel in the ridership model. Red Coach bus schedules and costs were used to represent the intercity bus mode choice parameters for long-distance business travelers, while both Greyhound and Megabus schedules were used to populate the parameters for non-business travel.

 

TABLE 4-17 Long-Distance BusAssumptions (Business)

Level of Service Parameter Orlando–Miami Orlando – Fort Lauderdale Orlando – West Palm Beach
In Vehicle Travel Time (minutes) 233 190 148
Fare Cost (2016$) $40.33 $40.33 $40.33
Headway (minutes) 190 190 190

 

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Table 4-18 Long-Distance Bus Assumptions (Non-Business)

Level of Service Parameter Orlando–Miami Orlando – Fort Lauderdale Orlando – West Palm Beach
In Vehicle Travel Time (minutes) 370 320 305
Fare Cost (2016$) $17.50 $17.50 $24.40
Headway (minutes) 95 95 228

 

In Southeast Florida, several private services provide connections between Miami and West Palm Beach with prices ranging from $18 to $25 for a one-way trip. Broward County Transit provides express bus service from Fort Lauderdale and key locations in suburban Broward to Miami. This service is relatively inexpensive at $5.00 for a full fare one-way ticket and is comparable to the Tri-Rail fare. To represent the current bus market in the mode choice model, Louis Berger assumed the service parameters for each city pair presented in Table 4-18.

 

Table 4-19 ShortDistance Bus Assumptions

Level of Service Parameter WPB – Miami WPB – Fort Lauderdale Fort Lauderdale – Miami
In Vehicle Travel Time (minutes) 100 65 60
Fare Cost (2016$) $23.00 $20.00 $5.00
Headway (minutes) 120 210 60

 

Existing Rail Level of Service Assumptions

 

Louis Berger reviewed Amtrak schedules to obtain assumptions regarding long-distance rail travel and these are presented in Table 4-19 while short-distance rail assumptions were obtained from TriRail schedules and are presented in Table 4-20.

 

Table 4-20 LongDistance Rail Assumptions

Level of Service Parameter Orlando–Miami Orlando – Fort Lauderdale Orlando – West Palm Beach
In Vehicle Travel Time (minutes) 397 348 290
Headway (minutes) 570 570 570
Fare Cost Non-Business(2016$) $46.00 $42.00 $33.00
Fare Cost Business (2016$) $169.00 $165.00 $156.00

 

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Table 4-21 ShortDistance Rail Assumptions

Level of Service Parameter WPB – Miami WPB – Fort Lauderdale Fort Lauderdale – Miami
In Vehicle Travel Time (minutes) 98 60 43
Fare Cost (2016$) $6.90 $6.25 $5.00
Headway (minutes) 45 45 45

 

Air Level of Service Assumptions

 

Air travel between Orlando and Fort Lauderdale and Miami is a small but active component of the travel market. To determine level of service parameters, Louis Berger evaluated published schedules and obtained one-way fare data by analyzing reported costs from the FAA 10 percent ticket sample. The resulting assumptions are presented in Table 4-21.

 

Table 4-22 AirTravel Assumptions

Level of Service Parameter Orlando–Miami Orlando – Fort Lauderdale Orlando – West Palm Beach
In Vehicle Travel Time (minutes) 70 74 N/A
Fare Cost (2016$) $175 $60 -
Headway (minutes) 142 228 -

 

Brightline Service Assumptions

 

In general, the Brightline service is planned to run hourly in each direction, operating from the early morning to the late evening. The current plan is to operate on average 32 trains per day, with 16 operating southbound and 16 northbound. Over the course of each year this translates to approximately 11,700 total train operations.

 

In Phase 1, the weekday schedule will begin early in the morning to serve the business and commuter travelers with a 6:00am southbound departing from West Palm Beach and 6:20am northbound departing from Miami with service extending to approximately 10:00pm or 11:00pm each evening. On weekends, the schedule will likely shift to slightly later start each morning with a corresponding change each evening to accommodate the greater percentage of leisure and event travelers. As demand patterns emerge during actual service, adjustments to this schedule can be accommodated and Brightline has appropriately invested in sufficient rolling stock capacity to allow for these adjustments.

 

As Phase 2 comes online the weekday and weekend schedules with hourly service throughout each day will be maintained, again with earlier starts on weekdays. Minor adjustments in departure times may be required to integrate with additional stop in Orlando and there will likely be southbound originations from both West Palm Beach and Orlando each day.

  

Based on this proposed running schedule, the Louis Berger Team developed the following assumptions for Brightline service characteristics.

 

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Table 4-23 BrightlineService Assumptions

Level of Service Parameter Orlando–
Miami		 Orlando – Ft Lauderdale Orlando – WPB WPB – Miami WPB – Ft
Lauderdale		 Ft
Lauderdale
– Miami
In Vehicle Travel Time (minutes) 195 163 127 65 34 24
Fare Cost – Business (2016$) $124.14 $112.04 $99.94 $57.34 $36.73 $36.73
Fare Cost – Non-Business (2016$) $88.67 $80.03 $71.39 $40.78 $26.25 $26.25
Headway (minutes) 60 60 60 60 60 60

 

Station Access and Egress

 

Through analysis of relevant data from the SP survey, Louis Berger confirmed that travelers place a higher value on the time it takes to access a public mode of travel (out of vehicle travel time -OVTT) than an equivalent amount of time spent traveling on board that mode (in-vehicle travel time - IVTT). Because OVTT is an important parameter in mode choice, Louis Berger took care in developing assumptions for access and egress from Brightline stations and other public modes of transport in the region. The steps in determining OVTT estimates for Brightline, Amtrak, Air travel, commuter rail, and bus are outlined below along with key assumptions.

 

● Consistent with the process for developing zone-to-zone times for highway auto travel (see Section 4.4.1.1.1) in the Short-Distance Market, Louis Berger assembled travel times from each of the 4,106 TAZs in the SERPM 6.7 to each TAZ containing a proposed Brightline Station or existing Tri-Rail Station. Bus locations were assumed to be the same as Brightline station zones and Tri-Rail station zones. This data extraction was done for peak and off-peak time periods.
● As with the highway dataset, the TAZ data base was clipped to exclude zones outside the catchment areas and then SERPM TAZs were aggregated to the Brightline model analysis zones.
● A terminal time of 15 minutes for Brightline and other modes with the exception of 75 minutes applied to air travel to account for additional security checks.
● Egress times, representing the journey from the arrival station to the ultimate destination zone were also estimated and a single parameter representing access time, terminal time, and egress time was calculated for each zone pair.

 

The cost of station access was also calculated for all public modes of travel and Table 4-23 provides the calculation of access and egress costs based on the average access and egress distance, and that also takes into account the various modes of station access. The resulting average cost of $11.15 was normalized by average access/egress distance and divided by travel party size to obtain per mile cost for each estimated travel party.

 

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Table 4-24 Access/Egress CostAssumptions

Station Access Mode % Utilization Cost (per travel party) Weighted Cost
Private Auto (Operating Cost) 35% $1.89 $0.66
Private Auto (Parking Cost) 35% $11.00 $3.85
Private Auto (Total Cost) 35% $12.89 $4.51
Drop-off by Private Auto 15% - $0.00
Taxi/TNC 10% $64.85 $6.48
Transit 20% $4.25 $0.85
Walk / Shuttle / Other 20% - $0.00
Total 100%   $11.85

 

4.5.1.2 Level of Service Assumptions for Brightline Phase II Study

 

The Louis Berger team developed level of service (LOS) profiles for each of the intercity travel modes considered in this study. As outlined above, these LOS variables would be applied to the mode choice model equations described earlier to estimate travel utilities for each available mode.

 

Auto Level of Service Assumptions

 

Auto is the predominant mode of travel in the corridor and the level of service variables describe the trip lengths and costs that travelers typically encounter. Knowledge of typical travel times and costs is a factor in the traveler’s choice of modes. Because of the relatively short-distances involved in the Southeast Florida market and the dominant position of this mode in the candidate travel market, Louis Berger took care to develop conservative assumptions for the level of service parameters so as not to overestimate the willingness of current auto travelers to switch to Brightline service.

 

Travel Time

 

Louis Berger utilized travel time data extracted from Google API for the 2010 base year. Table 4-24 shows an example of the estimated average travel times for each city or location pair. The table includes travel times for both the long-distance and short-distance travel market.

 

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Table 4-25 HighwayTravel Times

City Pair Distance
(Miles)		 AM Travel
Time (Min)		 Mid-Day Travel
Time (Min)		 PM Travel Time
(Min)		 Night Travel
Time (Min)
Short Distance Trips          
Disney - Orlando Airport 20 27 29 37 28
Tampa - Orlando 87 106 94 106 88
Lakeland - Orlando 63 81 72 83 68
Tampa - Lakeland 32 51 46 50 43
Long Distance Trips          
Tampa - West Palm Beach 261 205 206 206 200
Tampa - Ft Lauderdale 276 228 225 225 224
Tampa - Miami 173 244 240 246 239
Lakeland - West Palm Beach 216 177 180 176 174
Lakeland - Ft Lauderdale 239 217 217 219 210
Lakeland - Miami 171 236 235 241 228

Source: Louis Berger analysis of data from Google Maps API, 2017

 

Travel Costs

 

Given the large size of the intercity auto market, an important aspect in the development of the forecast is the identification of a sound assumptions for out-of-pocket auto operating costs, which are based in large part on fuel prices. Louis Berger reviewed the latest EIA Annual Energy Outlook (2018). This outlook provides the latest U.S. average gasoline pump price projection and differs slightly from the version utilized to conduct the Phase I Study (2017 outlook) as depicted by the comparison in Figure 4-5.

 

Figure 4-5 EIA GasPrices (2016 $ Per Gallon)

 

 

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The Louis Berger Team also conducted a detailed review of potential toll costs incurred by auto users in the Brightline service travel corridor. Based on the various O-D pairs, the Louis Berger Team compiled an estimate of toll costs based on published rates obtained from the toll calculation website: TollGuru. Table 4-26 provides the resulting estimate of toll costs represented in per mile terms for each of the major movements in the corridor. These results were incorporated into the travel demand model calculations for auto travel utility.

 

Table 4–26 Per Mile Toll Costs, Brightline Study Area (2016 $) 

    

  Distance (miles) Avg Cost $/Mile
Short Distance Trips    
Disney - Orlando Airport – –
Tampa - Orlando – –
Lakeland - Orlando – –
Tampa - Lakeland – –
Long Distance Trips    
Tampa - West Palm Beach 205 $6.9
Tampa - Ft Lauderdale 264 $4.7
Tampa - Miami 280 $5.8
Lakeland - West Palm Beach 171 $5.7
Lakeland - Ft Lauderdale 213 $5.7
Lakeland - Miami 236 $11.0

 

Bus Level of Service Assumptions

 

Louis Berger reviewed bus schedules of key players in the Florida intercity bus market (Greyhound, Megabus, and Red Coach). Table 4-27 represent the travel time, service frequency and travel cost assumptions applied in the ridership model.

 

Table 4–27 BusAssumptions

         
Level of Service Parameter  In Vehicle Travel
Time (minutes)		 Fare Cost (2016$) Headway (minutes)
AM MD PM NT AM MD PM NT AM MD PM NT
Short Distance Trips                        
Disney - Orlando Airport - - - - - - - - - - - -
Tampa - Orlando 114 113 130 100 20 20 23 18 380 210 180 180
Lakeland - Orlando 73 85 60 73 15 15 15 15 480 420 180 570
Tampa - Lakeland - - - - - - - - - - - -
Long Distance Trips                        
Tampa - West Palm Beach 415 450 465 545 29 33 33 39 180 210 380 180
Tampa - Ft Lauderdale 490 428 422 375 28 29 28 33 180 210 90 180
Tampa - Miami 535 478 470 0 29 29 28 25 180 210 90 180
Lakeland - West PalmBeach 390 390 390 390 36 36 36 36 480 210 480 570
Lakeland - Ft Lauderdale 320 350 335 335 50 32 41 41 180 420 480 570
Lakeland - Miami 278 418 0 278 27 27 28 27 380 210 180 440

 

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In Central Florida and Tampa Area, several private services provide shuttle service to the airport with prices ranging from $42 to $121 for a one-way trip. To represent the current bus market in the mode choice model, Louis Berger assumed the service parameters for each city pair presented in Table 4-28.

 

Table 4-28 ShuttleAssumptions

  

  Travel Time (Min) Avg Cost
  AM MD PM NT $/Mile
Short Distance Trips            
Disney - Orlando Airport 42 44 52 43 $ 20.00
Tampa - Orlando 121 109 121 103 $ 50.00
Lakeland - Orlando 96 87 98 83 $ 45.00
Tampa - Lakeland 66 61 65 58   -

 

Existing Rail Level of Service Assumptions

 

Louis Berger reviewed Amtrak schedules to obtain assumptions regarding rail travel and these are presented in Table 4-29

 

Table 4-29 Rail Assumptions
               
Level of Service Parameter  In Vehicle Travel
Time (minutes)		 Fare Cost (2016$) Headway (minutes)
AM MD PM NT AM MD PM NT AM MD PM NT
Short Distance Trips                        
Disney - Orlando Airport - - - - - - - - - - - -
Tampa - Orlando 128 128 128 128 10 10 10 10 780 420 780 960
Lakeland - Orlando 76 76 76 76 10 10 10 10 780 780 180 960
Tampa - Lakeland - - - - - - - - - - - -
Long Distance Trips                        
Tampa - West Palm Beach 227 227 227 227 26 26 26 26 780 420 780 960
Tampa - Ft Lauderdale 287 287 287 287 34 34 34 34 780 420 780 960
Tampa - Miami 328 328 328 328 36 36 36 36 780 420 780 960
Lakeland - West Palm Beach 182 182 182 182 25 25 25 25 780 420 780 960
Lakeland - Ft Lauderdale 242 242 242 242 33 33 33 33 780 420 780 960
Lakeland - Miami 283 283 283 283 35 35 35 35 780 420 780 960

 

Air Level of Service Assumptions

 

Air travel between Tampa and South Florida is a small but active component of the travel market. To determine level of service parameters, Louis Berger evaluated published schedules and obtained one-way fare data by analyzing reported costs from the FAA 10 percent ticket sample. The resulting assumptions are presented in Table 4-30.

 

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Table 4-30 AirTravel Assumptions

               
Level of Service Parameter   In Vehicle Travel
Time (minutes)		 Fare Cost (2016$) Headway (minutes)
AM MD PM NT AM MD PM NT AM MD PM NT
Long Distance Trips                        
Tampa - Ft Lauderdale 106 62 65 60 139 158 136 106 180 210 180 180
Tampa - Miami 69 71 71 69 158 136 136 136 90 210 180 180

 

Brightline Service Assumptions

 

In general, the Brightline service is planned to run hourly in each direction, operating from the early morning to the late evening. Based on this proposed running schedule, the Louis Berger Team developed the following assumptions for Brightline service characteristics.

 

Table 4-31 BrightlineService Assumptions

         
Level of Service Parameter  In Vehicle Travel Time
(minutes)		 Headway
(minutes)
AM MD PM NT AM MD PM NT
Short Distance Trips                
Disney - Orlando Airport 18 18 18 18 60 60 60 60
Tampa - Orlando 65 65 65 65 60 60 60 60
Lakeland - Orlando 65 65 65 65 60 60 60 60
Tampa - Lakeland - - - - - - - -
Long Distance Trips                
Tampa - West Palm Beach 187 187 187 187 60 60 60 60
Tampa - Ft Lauderdale 229 229 229 229 60 60 60 60
Tampa - Miami 266 266 266 266 60 60 60 60
Lakeland - West Palm Beach 187 187 187 187 60 60 60 60
Lakeland - Ft Lauderdale 229 229 229 229 60 60 60 60
Lakeland - Miami 266 266 266 266 60 60 60 60

 

Station Access and Egress

 

Station access and egress was handled through using an station access egress mode choice model based on the model parameters discussed in Section 4.4.3.

 

4.5.2     Model Calibration

 

The coefficients from the model estimation phase represent the statistical estimates of mode choice behavior recorded from the SP survey, but still require calibration to observed mode-choice behavior before they can be used to predict expected Brightline ridership. The trip attribute data for each individual mode described in the previous section was applied to the mode choice model’s level-of-service variable coefficients (e.g. in-vehicle travel time, access/egress travel time, total trip costs etc.) for the 2016 base year and the resulting mode splits were compared against expected mode share estimates based on current observations of mode splits.

 

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In keeping with best practice in mode choice forecasting, adjustments were made to the Alternative Specific Constants (referred to commonly as ASC or mode constants) of existing modes of travel (Auto, Rail, Bus, and Air) in order to align the predicted and observed mode shares—calibrating the model. Once the predicted and observed mode shares were achieved for existing modes of travel, the study then made the appropriate adjustments to the ASC for Brightline in the models. These adjustments were made to ensure a reasonable ordinal ranking of mode constant preferences across all modes available (i.e., maintain the preference for AAF expressed in the survey relative to the existing public modes); and also to ensure reasonable rates of AAF market capture based on examination of other similar intercity travel systems and markets in the United States. A review of the literature on calibration reveals varying treatments for the adjustment of mode constants for new services, including benchmarking to comparable modes.

 

Forecasts for new rail service in the U.S. and abroad often seek to benchmark the rail service against air travel given the amenities, similarities in access/egress attributes, and overall travel time (factoring in the security and terminal wait time of modern air travel). When making adjustments to the Brightline mode constant during the calibration process, the study team used the ASC for Air as a benchmark to ensure that the preference for Brightline relative to air travel that was observed in the responses to the SP survey, was preserved through the calibration process. During calibration, the magnitude of the Brightline mode constant adjustment in the long-distance non-business mode choice model was made in direct proportion to the calibration adjustment for the Air ASC. The mode constant adjustment for business travel was set at approximately 25 percent the adjustment applied to calibrate business market air travel volumes, while the mode constant adjustment for non-business travel was set at approximately 50 percent the non-business air travel market adjustment.

 

The calibration of ASCs for the short distance travel market were indexed relative to the adjustments made to both commuter rail and intercity bus travel in this region. Adjustments were also made to ensure logical relationships in market share capture across the three short distance intercity travel markets.

 

Overall, the calibration process resulted in a small increase in the preference for auto travel relative to the other modes, and decreases in the preference existing public modes: Air, Bus, and existing Rail. By benchmarking the adjustment in Brightline mode constant to that for existing air travel, a comparable premium travel mode, the overall preference for auto versus public modes in the mode choice model is preserved. This maintains consistency with the findings of the survey, which indicates that although Brightline will be an attractive choice for many travelers, and a complementary addition to the air travel option, automobiles will continue to be the predominant mode for long-distance intercity travel into the future.

 

4.5.3 Induced Ridership

 

Introduction of a new mode of travel, particularly premium rail service which is more convenient and improves travel time, can often encourage travelers to make trips they may not have made in the absence of the new service. Previous studies have found that the introduction of intercity rail service can result in levels of induced travel ranging from 5 percent to 30 percent. The highest levels of induced travel have been observed on high speed rail services serving multiple markets over distances of 200 to 500 miles.

 

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With the full implementation of Brightline service from Miami to Orlando, Louis Berger expects substantial opportunity for induced travel. The full service will result in a measurable reduction in the overall generalized cost of travel and Louis Berger used the general cost of travel principle to estimate the change in travel impedances that result from the introduction of the Brightline service between Miami and Orlando. Variants of the generalized cost approach are often used for induced travel estimates including a recent study of proposed high-speed rail conducted for the State of California.

 

Assuming the total number of trips (T) generated between a given O-D pair is a function of both socioeconomic/demographic factors (SED), as well as a measure of travel impedance – characterized by the generalized cost or utility of travel (U), as shown in the equation 12:

 

T = SED * Ucomp (11)

 

Where:

 

SED      = the socioeconomic/demographic factors characterizing both the origin and destination

 

Ucomp = generalized utility of travel between the origin and destination

 

And:

 

Ucomp = LN(expUauto + expUair + expUrail + expUbus + …)      (12)

 

Induced Trips = Total Trips with Brightline (TA) – Total Trips before Brightline (TB)         (13)

 

This induced trip methodology generates an incremental change in trip volumes that applies to all modes available. Based on equation 1, the total travel before and after Brightline introduction are estimated as follows:

 

TB = SED * UcompB

  TA = SED * UcompA

 

Holding the SED factors constant, the percentage increase induced demand in travel can therefore be expresses entirely in terms of changes in the generalized cost as shown in equation 14.

 

Induced Demand % = (UcompA – UcompB)/UcompB                (14)

 

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5.0 Brightline Ridership and Revenue Forecast

 

The significant time savings, frequent service, and reliability that Brightline provides has substantial potential to generate ridership and fare revenue. To determine the overall level of this potential, Louis Berger prepared annual Base Case forecasts for future operations with a focus on four time periods: 2018, the first year of revenue service in Southeast Florida; 2021 the assumed opening year of service to Orlando and Tampa, 2023, the first stabilized year after ramp-up for all phases of the Brightline facility; and 2040, the forecast horizon year. This section presents the results of these forecasts with reporting on market share, source of Brightline ridership, segment loading, and other performance metrics. Due to differences in modeling efforts, th results presented here distinguish where possible and applicable between ridership and revenue accruing to Phase I Study area (Miami to Orlando), and the incremental ridership and revenue accruing due to the extension of service to Tamp (Phase II Study). Forecast results are benchmarked to previous study efforts where possible.

 

5.1 Overall Level of Ridership and Revenue

 

The mode choice modeling tool and network information described in Section 4 allow Louis Berger to compare the travel time, access, and cost attributes of competing modes of travel against the origin and destination patterns of travelers. Responses to the stated preference survey indicate travelers’ willingness to pay for travel time savings and their overall preference for mode of travel. This information formed the basis for a mode choice model which was calibrated to existing patterns of travel behavior without Brightline. Further analysis of the survey data allowed us to account for Brightline as a new mode of travel and recognize the premium level of service it will provide relative to the existing modes. Given the known attributes of the existing modes serving the corridor, the size of the overall travel market, and the attributes of service to be offered by Brightline, Louis Berger used the mode choice model to estimate the proportion of travelers that will choose Brightline for trips between Southeast Florida and Orlando, as well as for trips within Southeast Florida. The forecast also includes an estimate of the extent to which Brightline will generate new travel demand based on the new level of connectivity it provides and the marketing efforts to be conducted by the operators. Figure 5-1 displays the forecast ridership results, in aggregate annual values, for Brightline service. Riders represent passengers making a one-way trip on Brightline, with a round trip generating two riders.

 

Table 5-1 2023 Ridership & Revenue

Phase I Study (Miami-Orlando) Phase II Study (Extension to Tampa)

Grand Total

Short- 

Distance 

Long-  

Distance 

Subtotal 

Short- 

Distance 

Long- 

Distance 

Subtotal
Ridership 3,079,472 3,534,197 6,613,669 1,967,353 935,992 2,903,345 9,517,014
Fare Revenue $100,763,367 $298,773,216 $399,536,583 $34,053,816 $145,891,900 $179,945,716 $579,482,299








 

Table In 2023, the number of riders on Brightline is expected to total approximately 9.5 million. This volume of riders includes riders who now travel by other modes, but would find Brightline more desirable than auto, rail, and bus services now connecting the cities. As travel demand in the corridor grows, Louis Berger projects that ridership will grow to over 13.64 million riders in 2040. Due to the various components of the ridership forecast, the overall growth in the number of riders on Brightline is expected to average 2.2 percent per year once demand reaches stabilized state, ahead of the growth in population and employment within Southeast Florida and Central Florida, but slower than the anticipated growth in airline and turnpike trips between the two regions.

 

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Figure 5-1 Brightline Annual Ridership Forecast – Base Case
 
 

Source: Louis Berger, 2017, 2018

 

Fares applied in the ridership revenue calculation are distinguished by station origin and destination pair and market segment (business and non-business). Brightline operations can be expected to generate total farebox revenues just over $579 million (2016 dollars) in 2023, the first stabilized year after ramp-up as indicated in Figure 5-2.

 

Figure 5-2 Brightline Annual Revenue Forecast – Base Case
 
 

Source: Louis Berger, 2017,2018

 

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5.1.1 Ramp-Up

 

As shown in the forecast charts presented above, we expect ridership and revenue for the initial years of Brightline to start at relatively low levels and grow to a stabilized volume after two years of operation for each segment that is placed into operation. This reflects Louis Berger’s “ramp-up” assumption, a period of time during which ridership is building up to long-term forecast levels as travelers become acquainted with the new rail service and adjust their trip-making habits. During 2017, management has made substantial investment in marketing, pre-launch ticket sales, and corporate block sales prior to the anticipated commencement of full-scale revenue service for Miami-West Palm Beach in May 2018. Management also intends to implement reduced price fares during an introductory period following the beginning of revenue service for each segment (see discussion in Section 5.2), it should also be noted that South Segment service will have been in operation for three years upon commencement of service between Southeast Florida and Orlando. Given these plans, for the short-distance trips, Louis Berger assumed a two calendar year ramp-up period: ridership volumes for 2018 are 40 percent of forecasted volumes, and 80 percent forecasted volumes in 2019. For the long-distance trips, Louis Berger assumed a two calendar year ramp-up period: ridership volumes for 2021 are 40 percent of forecasted volumes and 80 percent of forecasted volumes in 2022. The forecasts include the assumption that management will implement the stepped fare structure noted in Section 5.2, below, and that short-distance rail service will be fully operational until the first quarter of 2018, and long-distance revenue service will begin in first quarter of 2021. The full service will reach stabilized volumes by 2023.

 

There are no set standards for ramp-up assumptions in passenger rail forecasting and few direct comparables in the U.S. to the Brightline service. However, the Acela and the Euro Star, both comparable systems to Brightline, experienced substantial adoption levels in their initial years of operation, as shown in Table 5-2 below. . In terms of prior studies for rail in Florida, FOX Florida High Speed Rail Ridership study (1998) assumed a three-year ramp-up at 40 percent, 60 percent, and 90 percent. The Florida High Speed Rail Authority Orlando-Miami Planning Study (2002) assumed a two year ramp-up at 50 percent and 75 percent of forecasted volumes.

 

Table 5-2 Ramp-up Comparisons
Ramp-Up Period Eurostar Amtrak Acela Brightline
Year 1 32% 52% 40%
Year 2 53% 72% 80%
Year 3 88% 92% 100%

Source: Global Mass Transit Research, Eurostar: Restructuring, expansion and rolling stock procurement, December 1, 2014; Amtrak Annual Reports and Consolidated Financial Statements, FY 2000-2012.

 

5.1.2 Methodological Overview

 

As indicated in the introduction to this section, the ridership and revenue forecasts are based on state-of-the-practice mode choice modeling techniques used to evaluate how the introduction of Brightline service will influence travel choices in the market. The mode choice tool and network information allow Louis Berger to develop a head-to-head comparison of Brightline with existing modes of travel based on the travel time and cost of each mode, and the origin and destination patterns of travelers. Given the location and preferences of travelers, the forecast estimates Brightline’s capture of the overall travel market, which is a key element of overall ridership and revenue potential.

 

The ridership and revenue results presented were developed using fare plans from All Aboard Florida Operations, LLC, which were developed based on Brightline market pricing research. These fares were inputs into an evaluation of the short- and long-distance travel demand markets using the network model, which in turn generated the ridership

 

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and revenue estimates. The following subsections will address each of these distinct elements as well as other relevant details regarding the forecasts presented.

 

5.2 Fare Revenue Estimation

 

Brightline fares assumed in the modeling process were provided by All Aboard Florida Operations, LLC. All fares for Brightline and all costs for competing modes were fixed in real terms. The fare structure and levels were developed by All Aboard Florida Operations, LLC based on the findings in the 2012 Stated Preference Survey as well as the Pricing Research Survey conducted by Integrated Insights. Fares differ by time of day and day of week. Brightline offers economy fares (referred to as “Smart”) and premium fares (referred to as “Select”). Table 5-3 shows the selected average fares for the Smart class, while Table 5-4 shows the selected average fares for the Select class. Both tables are presented in 2016 dollars. For short-distance trips, fares are kept flat in real terms after 2019, while for long-distance trips, fares are kept flat in real terms after 2022.

 

Table 5-3 Average Fares (Smart Class), 2016 $
City Pair 2018 2019 2020 2021 2022 2023

 PHASE I STUDY
WPB-FTL $21.34 $26.25 $26.25 $26.25 $26.25 $26.25
WPB-MIA $33.94 $40.78 $40.78 $40.78 $40.78 $40.78
FTL-MIA $21.34 $26.25 $26.25 $26.25 $26.25 $26.25
ORL-WPB - - - $68.42 $71.39 $71.39
ORL-FTL - - - $77.13 $80.03 $80.03
ORL-MIA - - - $85.84 $88.67 $88.67
PHASE II STUDY
DIS-ORL - - - $7.56 $7.56 $7.56
TPA-ORL - - - $29.74 $29.74 $29.74
TPA-WPB - - - $120.52 $120.52 $120.52
TPA-FTL - - - $139.84 $139.84 $139.84
TPA-MIA - - - $151.34 $151.34 $151.34

 

Source: All Aboard Florida Operations, LLC, 2017, 2018

 

Table 5-4 Average Fares (Select Class), 2016 $
City Pair 2018 2019 2020 2021 2022 2023
PHASE I STUDY
WPB-FTL $31.32 $36.73 $36.73 $36.73 $36.73 $36.73
WPB-MIA $49.90 $57.34 $57.34 $57.34 $57.34 $57.34
FTL-MIA $31.32 $36.73 $36.73 $36.73 $36.73 $36.73
ORL-WPB - - - $95.78 $99.94 $99.94
ORL-FTL - - - $107.98 $112.04 $112.04
ORL-MIA - - - $120.17 $124.14 $124.14
PHASE II STUDY
DIS-ORL - - - $11.70 $11.70 $11.70
TPA-ORL - - - $46.08 $46.08 $46.08
TPA-WPB - - - $165.06 $165.06 $165.06
TPA-FTL - - - $191.52 $191.52 $191.52
TPA-MIA - - - $207.27 $207.27 $207.27

 

Source: All Aboard Florida Operations, LLC, 2017, 2018

 

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In comparison to fares from Amtrak’s Northeast Corridor, the premium service Brightline per mile rates for the long-distance market are noticeably lower than comparable Amtrak fares for similar travel distances. Figure 5-3 plots both the Brightline Smart and Select per-mile fares and includes the Acela and Regional Amtrak fares. The data indicates that that the Brightline select fares for lie close to the Amtrak Regional Service fares over comparable distances, and are far lower than the fares observed on Amtrak Acela service that offers amenities that more closely correspond to the premium service features experienced with Brightline service.

 

Figure 5-3 Comparison of Brightline Fares to Amtrak Northeast Corridor Fare Rates

 

 

Source: Louis Berger, 2017, 2018

 

5.3 Network Model Ridership & Revenue Forecasts

 

Louis Berger conducted a detailed analysis of potential ridership using the network model. Details of this analysis are presented in this section.

 

5.3.1 Market Capture and Compatibility with Existing Modes of Travel

 

The central station locations offered by Brightline will allow the railroad to provide an alternative source of transportation for travelers with origins or destinations near the urban cores of the three major cities in Southeast Florida and near major activity centers in Central Florida and Tampa. The network model forecast shows that the addition of the Brightline service will complement the existing modes of travel between these core locations.

Using the fares discussed in Section 5.2, the estimated Brightline mode shares and market capture rates in 2023 following the introduction and ramp-up of the Brightline service are presented the figures and tables below. The

 

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higher capture rate observed for the Tampa-Southeast Florida leg is likely driven by a higher existing air travel market share for this movement.

 

Figure 5-4 Long-Distance Travel Network Model Market Shares, 2023
 
Source: LB, 2017, 2018
 
Table 5-5 Long-Distance Travel Network Model Market Shares by City Pair, 2023

  Brightline Air Other Rail Bus Auto
Orlando - Palm Beach 10.8% 0.0% 0.0% 0.1% 89.0%
Orlando - Broward 9.7% 0.7% 0.0% 0.2% 89.4%
Orlando - Miami 8.9% 0.1% 0.0% 0.2% 90.7%
Orlando - SE Florida (Phase I Study) 9.9% 0.2% 0.0% 0.2% 89.6%
           
Tampa - Palm Beach 19.3% 0.0% 0.9% 2.0% 77.8%
Tampa - Broward 15.2% 10.4% 0.3% 0.8% 73.3%
Tampa - Miami 12.4% 5.9% 0.4% 1.0% 80.2%
Tampa-SE Florida (Phase II Study) 14.5% 6.5% 0.5% 1.1% 77.5%

 

Source: Louis Berger, 2017, 2018

 

Brightline is expected to attract between 72 percent and 83 percent of users currently traveling by air, rail, or bus (see Figure 5-6) from travelers to Orlando. Capture rates of users to Tampa are notably lower (25 to 40 percent) and this difference is likely due to the longer distance and greater level of competition by air travel that dominates the public modes of travel in the long distance corridors.

 

As shown in Figure 5-5, Brightline service linking Southeast Florida and Central Florida/Tampa will draw most of its ridership from travelers that would have otherwise used a private auto or existing public modes of travel for their trip. New trips, prompted by the convenience and travel time savings that Brightline will introduce to the market will make up 6 percent of total Brightline ridership after ramp-up.

 

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Figure 5-5 Share of Brightline Ridership by Source (Long-Distance)
 
Source: Louis Berger, 2017, 2018
 
Figure 5-6 Brightline Ridership Market Draw by Source (Long-Distance)
 

 

Source: Louis Berger, 2017, 2018

 

Short Distance Market

 

Figure 5-7 indicates Brightline will again contribute to the public modes of travel (bus and rail service currently provided by Tri-Rail). After the initial ramp up period, Brightline will serve approximately 0.74 percent of the travel market within Southeast Florida – bringing the total market share served by public transit to 0.95 percent. Within the Tampa-Orlando corridor however, Brightline is anticipated to capture a much higher percentage of the market – partly due to the longer intercity travel distance between Tampa and Orlando, but also due to the unique airport

 

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access travel market where Brightline is expected to be competitive against high cost alternatives such as Taxi/TNC and airport shuttles.
 
Figures 5-8 and Figures 5-9 shows that the largest proportion of Brightline riders in the Short Distance travel market will once again be drawn from auto travelers. Louis Berger anticipates that in 2023, approximately 58 percent of short distance Brightline riders would have otherwise made their journey by car. Although auto is substantial source of Brightline ridership, less than 1 percent of overall auto volume traveling between the three key cities in Southeast Florida will divert to Brightline, however Louis Berger expects a higher auto capture rate of almost 8 percent in the Tampa-Orlando corridor.
 
Figure 5-7 Share of Brightline Ridership by Source (Short-Distance), 2023
 
Source: LB, 2017, 2018
 
Table 5-6 Short-Distance Travel Network Model Market Shares by City Pair, 2023
 
Brightline
Rail
Bus
Auto
Taxi/TNC
Shuttle
Palm Beach-Miami
2.1%
0.3%
0.1%
97.5%
NA
NA
Palm Beach-Ft. Lauderdale
0.7%
0.1%
0.0%
99.2%
NA
NA
Ft. Lauderdale-Miami
0.6%
0.1%
0.2%
99.1%
NA
NA
Palm Beach-Miami (Phase I Study)
0.7%
0.1%
0.1%
99.0%
NA
NA
             
Disney - Orlando Airport
21.4%
0.0%
0.0%
49.0%
17.6%
11.9%
Tampa - Orlando
6.7%
0.1%
0.1%
91.9%
1.1%
0.1%
Tampa-Orlando (Phase II Study)
11.6%
0.0%
0.1%
77.5%
6.7%
4.1%
Source: Louis Berger, 2017, 2018

 

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Figure 5-8 Share of Brightline Ridership by Source (Short-Distance)
 
Source: LB, 2017, 2018
 
Figure 5-9 Brightline Ridership Market Draw by Source (Short-Distance)
 
Source: Louis Berger, 2017, 2018
 
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5.4          Overall Forecast Summary
 
In addition to the metrics presented above, two key components of the forecast are discussed below: a comparison of the Brightline forecast growth compared to other travel modes; a summary of boardings and alightings for the system.
 
5.4.1      Forecast Growth Comparison
 
Brightline Ridership performance into the future was estimated by determining the outlook for growth in the intercity travel market between Central and Southeast Florida.
 
The average annual forecast growth rate for Brightline ridership from the first stabilized year (2023) through 2040 is 2.2 percent.  This level of growth is higher than growth in population and employment observed in from 2000 to 2010 (at 1.7 percent and 1.5 percent per year respectively), but is also lower than the corresponding estimates of growth in both Turnpike auto trips and air passenger traffic. Figure 5-10 shows that both the Florida Turnpike and travel by air which saw growth above 3 percent per year in the last ten years.
 
 
Figure 5-10 Comparison of Brightline Forecast Growth Rates
 
Source: Louis Berger, 2017
 
5.5          Segment Loading and Boardings & Alightings
 
The overall ridership and revenue forecast totals summarized in Section 5.1 are based on forecast estimates of travel between station pairs between Southeast Florida and Central Florida.  Table 5-7 summarizes the annual segment volumes and revenues for 2023 and 2030.
 
The annual city pair segment volumes presented above allow for estimation of daily boardings and alightings at the four station locations.  These estimates are presented in Table 5-8 and Table 5-9. As expected, Orlando generates the highest count of forecasted boardings and alightings given its central location in the system and the volume of both long and short distance trips attracted to and from Central Florida.
 
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Table 5-7 Forecast Brightline – Annual Segment Volumes and Revenues (2016 $)
 Station Pairs
Dist.
Miles
2023
2030
Ridership
Revenue
Avg. Fare
Ridership
Revenue
Avg. Fare
               
PHASE I STUDY
Short Distance Market
              
Miami / Ft. Lauderdale
25
1,386,400
$40,972,028
$29.55
1,510,995
$44,657,226
$29.55
Ft. Lauderdale / West Palm
41
1,100,223
$32,894,966
$29.90
1,201,466
$35,923,160
$29.90
Miami / West Palm
66
592,849
$26,896,373
$45.37
648,781
$29,439,202
$45.38
Subtotal
  
3,079,472
$100,763,367
$32.72
3,361,242
$110,019,588
$32.73
               
Long Distance Market
              
Orlando / West Palm
174
1,632,610
$125,703,886
$77.00
2,033,787
$156,584,682
$76.99
Orlando / Ft. Lauderdale
215
984,603
$85,139,230
$86.47
1,226,011
$106,009,933
$86.47
Orlando / Miami
240
916,983
$87,930,100
$95.89
1,140,619
$109,355,118
$95.87
Subtotal
  
3,534,197
$298,773,216
$84.54
4,400,417
$371,949,733
$84.53
               
Phase I Study Subtotal
  
6,613,669
$399,536,583
$60.41
7,761,658
$481,969,321
$62.10
               
PHASE I STUDY
Short Distance Market
              
Disney / Orlando Airport
18
1,219,354
$9,552,996
$7.83
1,364,794
$10,692,682
$7.83
Tampa / Orlando
84
748,000
$24,500,820
$32.76
880,093
$28,846,983
$32.78
Subtotal
  
1,967,353
$34,053,816
$17.31
2,244,888
$39,539,666
$17.61
               
Long Distance Market
              
Tampa / West Palm
253
206,483
$27,383,214
$132.62
242,416
$32,148,670
$132.62
Tampa / Ft. Lauderdale
294
336,761
$52,566,181
$156.09
387,562
$60,493,162
$156.09
Tampa / Miami
319
392,747
$65,942,505
$167.90
463,143
$77,762,161
$167.90
Subtotal
  
935,992
$145,891,900
$155.87
1,093,120
$170,403,993
$155.89
               
Phase II Study Subtotal
  
2,903,345
$179,945,716
$61.98
3,338,008
$209,943,659
$62.89
               
GRAND TOTAL (PHASES I & II)
  
9,517,014
$579,482,299
$60.89
11,099,666
$691,912,980
$62.34
 
Source: Louis Berger, 2017, 2018
 
Table 5-8 Brightline Daily Boardings and Alightings, 2023
 
Station
BOARDINGS
ALIGHTINGS
PHASE I
 STUDY
PHASE II
STUDY
TOTAL
PHASE I
STUDY
PHASE II
STUDY
TOTAL
Miami
3,960
536
4,496
3,975
540
4,515
Fort Lauderdale
4,728
462
5,190
4,782
460
5,242
West Palm Beach
4,531
271
4,802
4,581
294
4,875
Orlando (Airport)
4,901
2,053
6,954
4,782
2,039
6,821
Orlando (Disney)
  
2,336
2,336
  
2,302
2,302
Tampa
  
2,295
2,295
  
2,318
2,318
TOTAL
18,120
7,954
26,074
18,120
7,954
26,074
 
Source: Louis Berger, 2017, 2018

 

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Table 5-9 Brightline Daily Boardings and Alightings, 2030
 
BOARDINGS
ALIGHTINGS
Station
PHASE I
STUDY
PHASE II
STUDY
TOTAL
PHASE I
STUDY
PHASE II
STUDY
TOTAL
Miami
4,509
633
5,141
4,533
636
5,170
Fort Lauderdale
5,362
532
5,894
5,428
530
5,958
West Palm Beach
5,291
318
5,609
5,350
346
5,696
Orlando (Airport)
6,103
2,324
8,427
5,953
2,303
8,256
Orlando (Disney)
  
2,649
2,649
  
2,613
2,613
Tampa
  
2,689
2,689
  
2,717
2,717
TOTAL
21,265
9,145
30,410
21,265
9,145
30,410
 
Source: Louis Berger, 2017, 2018
 
5.6          Phase II Study Lakeland Station Impacts
 
As indicated in the introduction, Louis Berger evaluated the potential impact of introducing additional stations in the Lakeland region. Two station locations at the Kathleen Road and USF Polytechnic location were considered for this analysis and the corresponding ridership and revenue impacts are presented in Table 5-9. Based on these results, the introduction of a station in the Lakeland region is expected to increase ridership by approximately 10 percent.
 
Table 5-10 Lakeland Station Ridership and Revenue Impacts
 
2023 Base Case
Lakeland (Kathleen Road)
Lakeland (USF Polytechnic)
 
Ridership
Revenue
Ridership
Revenue
Ridership
Revenue
SHORT DISTANCE
            
Disney - Orlando Airport
1,219,354
$9,552,996
1,219,354
$9,552,996
1,219,354
$9,552,996
Tampa - Orlando
748,000
$24,500,820
730,614
$23,922,190
729,783
$23,898,285
Lakeland - Orlando
0
$0
160,843
$3,457,313
123,642
$2,080,434
Tampa - Lakeland
0
$0
23,661
$341,387
39,001
$740,269
Subtotal
1,967,353
$34,053,816
2,134,472
$37,273,886
2,111,780
$36,271,984
             
LONG DISTANCE
            
Tampa - West Palm Beach
206,483
$27,383,214
175,942
$23,457,711
180,722
$24,096,027
Tampa - Ft Lauderdale
336,761
$52,566,181
311,235
$48,744,867
315,969
$49,487,798
Tampa - Miami
392,747
$65,942,505
360,098
$60,665,112
365,414
$61,562,738
Lakeland - West Palm Beach
0
$0
68,401
$7,564,165
79,279
$8,350,969
Lakeland - Ft Lauderdale
0
$0
63,283
$8,295,740
73,655
$9,245,348
Lakeland - Miami
0
$0
74,074
$10,609,840
84,939
$11,702,021
Subtotal
935,992
$145,891,900
1,053,032
$159,337,435
1,099,978
$164,444,901
             
Grand Total
2,903,345
$179,945,716
3,187,504
$196,611,321
3,211,758
$200,716,885
 

 

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6.0          Forecast Sensitivity
 
Louis Berger conducted several additional simulations to determine the sensitivity of the network/mode choice model forecast outputs to changes in key input parameters.  The findings of these tests are summarized in Table 6-1, with implications for validation of the forecast and for risk of forecast error summarized below.
 
 
Table 6-1 Sensitivity Test Results, Ridership and Revenue % Change, 2023
Sensitivity Test
Assumption
Modified
Change in Assumption
Phase I Study
Phase II Study
Short-Distance
Long-Distance
Short-Distance
Long-Distance
Ridership
Effect
Revenue
Effect
Ridership
Effect
Revenue
Effect
Ridership
Effect
Revenue
Effect
Ridership
Effect
Revenue
Effect
Brightline Travel
Time
10% decrease
3.60%
4.00%
6.30%
6.30%
2.93%
3.75%
6.96%
7.06%
10% increase
-3.50%
-3.80%
-5.90%
-6.00%
-3.47%
-4.40%
-7.97%
-8.08%
Brightline
Frequency
20% decrease
-3.70%
-3.90%
-1.50%
-1.60%
-4.26%
-3.41%
-1.82%
-1.83%
20% increase
3.80%
4.00%
1.50%
1.60%
3.89%
3.36%
1.11%
1.11%
Intercity Travel
Time by Auto
20% decrease
-10.40%
-10.90%
-11.20%
-11.40%
-10.95%
-13.01%
-11.95%
-11.86%
20% increase
11.90%
12.60%
12.60%
12.70%
12.19%
14.99%
13.28%
13.16%
Auto Fuel
Prices*
Low: (-35% /-31%)
-3.30%
-3.50%
-3.10%
-3.10%
-1.72%
-2.90%
-2.29%
-2.30%
High: (+79% /+48%)
7.90%
8.40%
7.40%
7.50%
2.81%
4.75%
3.67%
3.68%
Air Fares
20% decrease
N/A
N/A
-0.30%
-0.30%
N/A
N/A
-1.94%
-2.12%
20% increase
N/A
N/A
0.10%
0.10%
N/A
N/A
1.67%
1.85%
* Note: Different EIA Fuel price forecast assumptions used in the separate Phase I and II Study models – see Section 4.5.2
 
Source: Louis Berger, 2017, 2018
 
6.1          Brightline Travel Time
 
Among other things, travelers will choose the Brightline service for the savings in travel time that the service offers.  The travel time on the train duration of the trip on board the train, referred to as in-vehicle travel time (IVTT), is a key input to the mode choice model.  The discrete choice analysis of the SP survey provides an indication of how business and non-business travelers value IVTT.  In tests where the duration of the Brightline trip between destinations was varied, we found that travelers are somewhat less sensitive to IVTT than they are to fare price, as follows.
 
Overall, a decrease in Brightline running time of 10 percent (i.e., a reduction of 20 minutes in the running time from Miami to Orlando) could be expected to result in an increase of 6.3 percent in ridership.  Should the running time need to be increased from the levels assumed in this study, a similar magnitude of decrease in ridership could be expected.  In the SEF market a similar decrease of 10 percent in run time (7 minutes) would result in a 3.6 percent increase in ridership. Similar patterns of magnitude are observed in the Phase II Study.
 
6.2          Brightline Frequency
 
Travelers also place a value on the frequency of service, meaning how often trains with the same origin and destination operate.  With intercity rail service, as opposed to intracity transit, travelers tend to time their arrivals to the station closely with scheduled departures and frequency of service is less important than running time or cost.
 
A 20 percent increase in the frequency of service over the one departure per hour base assumption results in a 3.8 percent increase in ridership in the Miami to West Palm Beach short-distance market, and a 1.5 percent increase for
 
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the longer distance city pairs.  An equivalent decrease in frequency would result in a roughly equivalent decrease in ridership. Similar magnitudes of service frequency response are recorded in the Phase II Study.
 
6.3          Intercity Travel Time by Auto
 
An increase in auto travel time between the city pairs as a result of greater roadway congestion in the region would make the Brightline service more competitive (a cross-elasticity response).
 
In the case where only changes in intercity travel time by auto are considered (attributable to greater intercity and roadway congestion without impacting station access times), a 20 percent increase would increase long-distance and short-distance travel ridership by 12.6 and 11.9 percent, respectively.   A decrease in travel time by auto will result in similar reductions for both market, within 1% percentage point. Slightly higher magnitudes of service frequency response are recorded in the Phase II Study.
 
6.4          Auto Fuel Prices
 
An increase in gas prices would also be expected to make the Brightline service more competitive.  This effect is offset, however, by a corresponding change in cost of accessing the stations (e.g. by private auto or taxi/bus transit where fuel costs are passed on in fare prices).  It should be noted that this evaluation does not include a change in the cost of Brightline fuel prices that may be passed on in higher fares.
 
Because the two studies were conducted using different fuel forecast assumptions, the reactions to EIA high or low price scenarios vary due to the difference in magnitude of those changes. A decrease in fuel prices results in Brightline ridership losses, while increases conversely result in boosts to ridership.
 
6.5          Air Fares
 
As with costs in the auto mode of travel, an increase in airline fares could also be expected to make the Brightline service relatively more competitive for travel between Southeast Florida and Central Florida.  This effect is expected to be small, however, given that air travel is a small part of the intercity travel market, especially when compared to auto travel. This sensitivity test was only conducted for the long-distance market, since there is currently no air travel service within the short distance city pairs.
 
An increase in air fares of 20 percent would be expected to result in a 0.1 percent increase in Brightline ridership for the long-distance market to and from Central Florida but an almost 2 percent increase to and from Tampa.  Should air fares decrease by the same magnitude, Brightline ridership would drop by 0.3 percent to and from Central Florida, and decrease by about 1.7 percent to and from Tampa.

 

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7.0          Conclusion
 
With frequent service between city centers in the corridor, Brightline offers the prospect of substantial time savings to current users of auto, bus, traditional rail, and even air.  To determine how these time savings would alter travel behavior and generate ridership and revenue for Brightline, Louis Berger undertook a detailed examination of current travel behavior, and conducted surveys that determined traveler preferences and willingness to pay.  Best practices in discrete choice analysis and travel network modeling were employed and findings were tested and referenced to previous studies.     The analysis revealed that introduction of Brightline service would complement existing modes of travel and draw substantial number of business and non-business travelers. The analysis also identified several areas of focus already under consideration in Brightline business planning with respect to operating schedules, service offerings, and fare setting.

 

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Appendix A Population Density

Figure A A Population Density In The West Palm Beach
 
 
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Figure A 2 Population Density In The Broward

 
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Figure A 3 Population Density In The Miami-Dade

 
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Figure A 4 Population Density In The Central Florida

 
 
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Figure A 5 Population Density In The Tampa Area

 
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Appendix B Employment Density

Figure B– 1 Employment Density In The West Palm Beach

 
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Figure B– 2 Employment Density In The Broward

 
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Figure B– 3 Employment Density In The Miami-Dade

 
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Figure B– 4 Employment Density In The Central Florida
 
 
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Figure B– 5 Employment Density In The Tampa Area

 
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