PREPARED FOR
HAMPTON ROADS TRANSPORTATION PLANNING ORGANIZATION
HAMPTON ROADS HIGH-SPEED AND INTERCITY PASSENGER RAIL
PRELIMINARY VISION PLAN
PROGRESS REPORT A:
PRELIMINARY RIDERSHIP AND REVENUE FORECASTS
July 2010
Prepared by
Transportation Economics & Management Systems, Inc.
Hampton Roads High-Speed and Intercity Passenger Rail Preliminary Vision Plan Progress Report A: Preliminary Ridership and Revenue Forecasts
ACKNOWLEDGEMENT/DISCLAIMER
Prepared in cooperation with the U.S. Department of Transportation (USDOT), the Federal Highway Administration (FHWA), the Virginia Department of Transportation (VDOT) and the Virginia Department of Rail and Public Transportation (DRPT). The contents of this report reflect the views of the Hampton Roads Transportation Planning Organization (HRTPO). The HRTPO is responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the FHWA, VDOT, or DRPT. This report does not constitute a standard, specification, or regulation.
These opinions, findings and conclusion are preliminary in nature and do not represent final statements of fact or final projections of high-speed and intercity passenger rail service to Hampton Roads. It is anticipated upon completion of the next phase of the study, these initial study results will be refined to a level that supports a Hampton Roads Vision Plan for High-Speed and Intercity Passenger Rail services from Washington D.C. to the Hampton Roads metropolitan area.
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Hampton Roads High-Speed and Intercity Passenger Rail Preliminary Vision Plan Progress Report A: Preliminary Ridership and Revenue Forecasts
PROGRESS REPORT A: PRELIMINARY RIDERSHIP AND REVENUE FORECASTS
A.1 OVERVIEW
A key element in evaluating the high-speed and intercity passenger rail service is the assessment of the total travel market in the corridor under study, and how well a new passenger rail service might perform in that market. For the purpose of the preliminary analysis, this assessment was accomplished using the following process:
1. Gathered information on the total market and travel patterns in the corridor for auto, air, bus and passenger rail travel.
2. Identified and quantified factors that influence travel choices, including current and forecast socioeconomic characteristics and future gas price.
3. Built and calibrated a model to test different travel choice scenarios; in particular, identified the likely modal shares under each scenario.
4. Forecasted travel, including total demand and modal shares.
It should be noted that for the purposes of this analysis no new profile and stated preference data was gathered, which will be needed to finalize the forecasts at a feasibility level.
This chapter documents the analysis taken for the High-Speed and Enhanced Intercity Passenger Rail assessment. It also describes the methodology and major assumptions incorporated in the preliminary forecasting model. Finally, it presents the preliminary results of the forecasting results in terms of preliminary ridership and revenue forecasts for each level of passenger rail service, based on initial estimates of frequencies, travel times, fare levels and infrastructure investment.
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Hampton Roads High-Speed and Intercity Passenger Rail Preliminary Vision Plan Progress Report A: Preliminary Ridership and Revenue Forecasts
A.2 DATABASE DEVELOPMENT
A.2.1 ZONE DEFINITION
A key step in developing a study database (network, socioeconomic and origin-destination) is to construct the fundamental unit of analysis, the zone system. The zone system is predominantly county-based, in rural areas, and TAZ (traffic analysis zone) based in urban areas as shown in Exhibit A-1. County-based zones are compatible with the socioeconomic baseline and forecast data derived from the Bureau of Economic Analysis (BEA), which are also county-based. Zones are defined relative to the passenger rail network. As zones move outward from stations, their size transitions from small to larger.
The networks and zone systems developed for the Hampton Roads High-Speed and Intercity Passenger Rail Study were enhanced with finer zone detail in urban areas. Exhibit A-2 shows the finer zones in the Hampton Roads and Richmond-Petersburg regions. The zone system contains 274 zones within the study area boundaries.
EXHIBIT A-1: ZONES OF STUDY AREA
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EXHIBIT A-2: FINER ZONES IN HAMPTON ROADS AND RICHMOND-PETERSBURG REGIONS
A.2.2 NETWORK DATA
In transportation analysis, travel desirability is measured in terms of cost and travel time. These variables are incorporated into the basic network elements. Correct representation of the networks is vital for accurate forecasting. Basic network elements are called nodes and links. Each travel mode consists of a database comprised of zones, stations or nodes, and existing connections or links between them in the study area. Each node and link is assigned a set of attributes. The network data assembled for the study included the following attributes for all the zone pairs.
• For public travel modes (air, passenger rail and bus): − Access/egress times and costs (e.g., travel time to a station, time/cost of parking, time walking from a station, etc.) − Waiting at terminal and delay times − In-vehicle travel times
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− Number of interchanges and connection times − Fares − On-time performance − Frequency of service
• For private mode (auto): − Travel time, including rest time − Travel cost (vehicle operating cost) − Tolls
The station stops assumed in the model for each step are identified in Exhibit A-3. Note that not all trains stop at each station; some trips are designated and modeled as express trips with limited stops.
EXHIBIT A-3: ASSUMED HIGH-SPEED PASSENGER AND INTERCITY STATION STOPS WITHIN THE HAMPTON ROADS-RICHMOND-WASHINGTON CORRIDOR
Station Step 1 Step 2 Step 3 Step 4 Washington, Union X X X X Alexandria X X X X Franconia X X X X Woodbridge X X X X Quantico X X X X Fredericksburg X X X X Ashland X X X X Richmond, Staples Mill X X X X Richmond, Main Street X X X X Williamsburg X X X X Newport News (Airport) X X Newport News (Downtown) X X X X Petersburg X X X X Suffolk X X Bowers Hill X X X Norfolk X X X X
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Hampton Roads High-Speed and Intercity Passenger Rail Preliminary Vision Plan Progress Report A: Preliminary Ridership and Revenue Forecasts
The network data were obtained from a variety of sources, including FAA (Federal Aviation Administration) 2008 Primary and Non-primary Commercial Service Airports, BTS (Bureau of Transportation Statistics) 2009 National Transportation Atlas Database, schedule information from the airline websites and flight search engines, IRS 2010 Mileage Rate, MapPoint 2006, 2009 National Transportation Atlas Database, and Amtrak schedules. A key difference in this study with the Richmond/Hampton Roads Passenger Rail Project Draft Environmental Impact Statement (DEIS) is that as directed by the Hampton Roads Transportation Planning Organization (HRTPO), the Hampton Roads highway network is consistent with the region’s constrained Long-Range Transportation Plan (LRTP) and excludes the third crossing (2030 amended LRTP).
A.2.3 SOCIOECONOMIC BASELINE AND FORECASTS
Socioeconomic forecast growth rate percentages for each zone were derived from various sources, as follows:
• Hampton Roads Transportation Planning Organization and Hampton Roads Planning District Commission • Richmond Regional Planning District Commission • Crater Planning District Commission • Richmond/Hampton Roads Passenger Rail Project • Virginia Employment Commission • Metropolitan Washington Council of Governments • Baltimore Metropolitan Council • State Planning Organizations (multiple) • Bureau of Economic Analysis • U.S. Census Bureau • Applied Demographic Solutions
Using these sources, each zone was treated as an independent unit in the income, population and employment forecast. Exhibit A-4 shows the population and employment forecasts of the study area until 2040, and Exhibit A-5 shows the income projection until 2040 in the study area.
The exhibits show that there is higher growth of employment and income than population. Travel increases are strongly correlated to increases in per capita income, in addition to changes in population and employment. Therefore, travel in the corridor is expected to increase faster than the population growth rates, as changes in per capita income outpace population growth.
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EXHIBIT A-4: STUDY AREA POPULATION AND EMPLOYMENT PROJECTIONS
70 Average Annual Growth Rates: 60 0.69% 50
40 0.79% 30
20 Population Employment people of Millions 10
0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045
EXHIBIT A-5: STUDY AREA PER CAPITA INCOME PROJECTIONS
80 Average Annual Growth Rate: 70 60 1.05% 50
40 30
20 Per Capita Income Thousands 2009$ of 10 0
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045
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Hampton Roads High-Speed and Intercity Passenger Rail Preliminary Vision Plan Progress Report A: Preliminary Ridership and Revenue Forecasts
To get a better understanding of the socioeconomic growth trend within the study area, the socioeconomic growths were computed separately for main areas in the High-Speed and Intercity Passenger Rail Corridors as shown in Exhibits A-6, A-7, and A-8.
EXHIBIT A-6: POPULATION PROJECTION BY MAIN URBAN AREAS
Population Forecast 2,000 1,800 CSXT/I-64 1,600
1,400 MillionsNS/Route 460 1,200 1,000 Richmond Area
800 Petersburg20202020 Area 600 00051015202530354045 400 Thousands of People of Thousands 200 0
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045
EXHIBIT A-7: EMPLOYMENT PROJECTION BY MAIN URBAN AREAS
Employment Forecast 1,200
1,000 CSXT/I-64
800 MillionsNS/Route 460
600 Richmond Area 400 Petersburg20202020 Area 00051015202530354045 200 Thousands of People Thousands 0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045
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Hampton Roads High-Speed and Intercity Passenger Rail Preliminary Vision Plan Progress Report A: Preliminary Ridership and Revenue Forecasts
EXHIBIT A-8: INCOME PROJECTION BY MAIN URBAN AREAS
Per Capita Income Forecast 80
70 CSXT/I-64
60 MillionsNS/Route 460
50 Richmond Area
40 Petersburg20202020 Area 00051015202530354045
Thousands of 2009$ 30
20
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045
It can be seen that the population and employment of the NS/Route 460 and Richmond areas grow faster than that of the CSXT/I-64 and Petersburg areas. Unlike population and employment, the income growths in these areas are similar to each other.
In applying these socioeconomic data to the model, separate forecasts were made for each of the 274 zones using BEA, MPO, and US Census disaggregate data.
A.2.4 ORIGIN DESTINATION INFORMATION
TEMS extracted, aggregated and validated data from a number of sources in order to estimate base travel between Origin-Destination Pairs. A key source was the DRPT DEIS total travel trip file. Unfortunately, this was not given by mode and as such, specific data for each mode had to be developed from the sources shown in Exhibit A-9. Preliminary estimates of travel were generated based on socioeconomic and trip attribute data, then validated with actual modal data counts.
Access/egress simulation refers to the need to identify origin and destination zones for trips via passenger rail, air and bus. Otherwise, all non-auto trips would appear to begin at the bus or passenger rail terminal or airport zones. Distribution of access and egress trips to zones was accomplished using socioeconomic data and access/egress travel time and cost data. The flowchart of origin-destination trip estimation is shown in Exhibit A-10.
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Hampton Roads High-Speed and Intercity Passenger Rail Preliminary Vision Plan Progress Report A: Preliminary Ridership and Revenue Forecasts
It should be noted that in Phase II, this data will be enhanced with additional data such as SEHSR information and disaggregate modal share data from DRPT, and specific surveys to bring it to a feasibility level quality.
EXHIBIT A-9: SOURCES OF TOTAL TRAVEL DATA BY MODE
Mode Data Source Description Data Enhancement Required Station-to-station Passenger Amtrak Ticketing Data; passenger volume; Access/Egress Simulation Amtrak Station Data Rail Station Passenger Volume Federal Aviation Administration (FAA) Airport-to-airport passenger Air 10% Ticket Sample; Access/Egress Simulation volume Bureau of Transportation Statistics Greyhound Bus Estimating bus load factors, Bus Access/Egress Simulation Schedules simulate passenger volume Statewide and Urban Statewide and MPO Trip Simulation for Auto Flow Auto Origin-Destination highway and urban traffic Movement and AADT Counts Studies studies
EXHIBIT A-10:
TRIP MATRIX GENERATION AND VALIDATION
Socioeconomic Trip Data Attributes
Trip Matrix Simulation
Control Using Station Ticket Counts
Control Using Inter- Station Volume
Trip Matrix
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Hampton Roads High-Speed and Intercity Passenger Rail Preliminary Vision Plan Progress Report A: Preliminary Ridership and Revenue Forecasts
The total estimated person trip within the study area in 2010 is 49.12 million. The travel market is estimated to grow at 0.8 percent annually on average. Exhibit A-11 shows the total travel demand from 2010 to 2040.
EXHIBIT A-11: GROWTH IN CORRIDOR TRAVEL MARKET
65 Average Annual Travel Market Growth Rate: 0.8% 60
55
Millions of Trips 50
45 2010 2015 2020 2025 2030 2035 2040
Exhibit A-10 shows that the travel market within the study area will have 63 million person trips in 2040, which is 28 percent more than that in 2010. The average annual growth rate of 0.8 percent is in line with the socioeconomic growth rate presented in last section.
A.2.5 FUEL PRICE FORECASTS
A crucial factor in the future attractiveness of High-Speed and Intercity Passenger Rail is the price of gas. Forecasts of oil prices from the Energy Information Agency suggest that oil price will return at least to $100 per barrel in the next five years and will remain at that level in real terms to 2030 and beyond. See Exhibit A-12. The implication of this is a central case gas price of 4 dollars per gallon with a high case price of $5 per gallon and a low case price of $3 per gallon. Since gas is currently $2.50+ a gallon in a weak economy environment, $4 per gallon once the economy starts to grow again seems very realistic. Exhibit A-13 shows the relationship of gas prices to oil acquisition cost from 1993 to 2010. It shows that gas prices rise directly with oil prices. As a result, gas prices are likely to rise as shown in Exhibit A-14. This gives high, low and central scenarios for gas price to use in the travel demand forecast.
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Hampton Roads High-Speed and Intercity Passenger Rail Preliminary Vision Plan Progress Report A: Preliminary Ridership and Revenue Forecasts
EXHIBIT A-12: U.S. CRUDE OIL COMPOSITE ACQUISITION (WHOLESALE) COST BY REFINERS – 1 HISTORIC DATA AND EIA FORECASTS
140
120
100
80 U.S. Imported Crude Oil Cost by Refiners: 60 Historic Data and Projections 2008$ per Barrel per 2008$ 40
20 Central Case
0 1993 1998 2003 2008 2013 2018 2023 2028 2033 Year
2 EXHIBIT A-13: U.S. RETAIL GASOLINE PRICES AS A FUNCTION OF CRUDE OIL PRICES (1993-2010)
4
3.5 y = 0.03x + 0.99 3 R2 = 0.98 2.5
2
1.5 Energy Prices: U.S. Retail 1 Gasoline Prices as a Function of Crude Oil Prices (1993-2008) 0.5
Retail Gasoline Prices ($2008 per per ($2008 Gallon) Prices Gasoline Retail 0 0 20406080100 Imported Crude Oil Cost by Refiners (2008$ per barrel)
1 Sources: EIA - http://www.eia.doe.gov/oiaf/aeo/aeoref_tab.html and http://www.eia.doe.gov/dnav/pet/pet_pri_rac2_dcu_nus_a.htm
2 Analysis developed by TEMS, Inc. for MARAD US DOT. Sources: http://tonto.eia.doe.gov/dnav/pet/hist/LeafHandler.ashx?n=pet&s=mg_tt_us&f=a and http://www.eia.doe.gov/dnav/pet/pet_pri_rac2_dcu_nus_a.htm
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EXHIBIT A-14: U.S. RETAIL GASOLINE PRICES - HISTORIC DATA AND THE FORECAST
7.00
6.00
5.00
4.00 High Case Central Case Low Case 3.00 Gas Price(2008$/Gal.) 2.00
1.00
0.00 2007 2008 2010 2015 2020 2025 2030 2035 2040 Year
A.2.6 HIGH-SPEED AND INTERCITY PASSENEGR RAIL SERVICE AND HIGHWAY TRAFFIC
CONGESTION
Specific High-Speed and Intercity Passenger Rail alternatives were developed to reflect proposed levels of service that would potentially be offered. The level of service of Auto mode incorporates the MPO congestion scenario to ensure that the automobile traveling impedances are adequately reflected. Exhibit A-15 illustrates a comparison of the High-Speed and Intercity Passenger Rail travel times with the Auto travel times in the corridor. As shown, in 2025, the auto travel time is 2.5 hours from Norfolk to Richmond, 5 hours from Norfolk to Washington DC, 2 hours from Newport News to Richmond, and 4.5 hours from Newport News to Washington DC. Compared to 2010, the auto travel times for the above city pairs will increase by 6 to 16 percent in 2025 due to increased travel demand and congestion.
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EXHIBIT A-15: CORRIDOR TRAVEL TIME COMPARISON
6
5.0 5 4.4 4.5 4.2 4.0 3.9 4 3.7 3.8 3.5
3 2.7 2.5 Hours 2.3 2.4 2.0 2.0 1.8 1.9 2 1.6 1.4 1.4 1.2 1.2 0.9 1 0.9
0 Norfolk-Richmond Norfolk-Washington Newport News- Newport News- Richmond Washington
Step 1 Step 2 Step 3 Step 4 Auto 2010 Auto 2025
In contrast to Auto travel times, the improved High-Speed and Intercity Passenger Rail service will provide shorter travel times, for example, the Passenger Rail travel time from Norfolk to Washington DC will decrease from 4 hours in Step 1 to 2 hours in Step 4. This will make High-Speed Rail service increasingly competitive as a result of improved level of service of passenger rail and longer travel time by Auto due to congestion.
A.2.7 FARE STRUCTURE
An all-day fare policy was assumed as the model is an all-day model and has been calibrated for this case. The policy assumes that the fare level is higher for better service as shown in Exhibit A-16. The fares shown are full price fares and do not reflect any discount, such as that for off-peak travel, corporate rates, group and family discounts, student fares, etc.
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EXHIBIT A-16: ONE WAY FULL FARE BY SERVICE LEVEL ($ 2010)
Passenger Rail Norfolk- Norfolk- Newport News- Newport News- Scenarios Richmond Washington Richmond Washington
Step 1 20 40 15 35
Step 2 20 40 15 35
Step 3 30 60 20 50
Step 4 35 70 22 65
A.3 RIDERSHIP AND REVENUE FORECAST RESULTS
A.3.1 BASIC STRUCTURE OF THE COMPASS™ MODEL
The COMPASS™ Multimodal Demand Forecasting Model is a flexible demand forecasting tool used to compare and evaluate alternative passenger rail network and service scenarios. It is particularly useful to assess the introduction or expansion of public transportation modes such as air, passenger rail or bus into new markets. Exhibit A-17 shows the structure and working process of COMPASS™ Model. As shown, the inputs to the COMPASS™ Model are base and proposed transportation networks, base and projected socioeconomic data, value of time and value of frequency from Stated Preference surveys, and base trip data.
The COMPASS™ Model structure incorporates two principal models: a Total Demand Model and a Hierarchical Modal Split Model. These two models are calibrated separately. In each case, the models are calibrated for origin-destination trip making in the study area. The Total Demand Model provides a mechanism for replicating and forecasting the total travel market. The total number of trips between any two zones for all modes of travel is a function of (1) the socioeconomic characteristics of the two zones and (2) the travel opportunities provided by the overall transportation system that exists (or will exist) between the two zones. Typical socioeconomic variables include income, employment, and population. The quality of the transportation system is measured in terms of total travel time, travel cost, and worth of travel by all modes.
The role of the COMPASS™ Modal Split Model is to estimate relative modal shares of travel given the estimation of the total market by the Total Demand Model. The relative modal shares are derived by comparing the relative levels of service offered by each of the travel modes. Three levels of binary choice
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Hampton Roads High-Speed and Intercity Passenger Rail Preliminary Vision Plan Progress Report A: Preliminary Ridership and Revenue Forecasts are typically calibrated (Exhibit A-18). The first level of the hierarchy separates private auto travel, with its perceived spontaneous frequency, low access/egress times, and highly personalized characteristics, from public modes (i.e., bus, passenger rail and air). The second structure level separates air, the fastest and most expensive public mode, from passenger rail and bus surface modes. The lowest level of the hierarchy separates passenger rail, a potentially faster, more reliable, and more comfortable mode, from the bus mode. The model forecasts changes in riders, revenue and market share based on changes travel time, frequency and cost for each mode.
EXHIBIT A-17: STRUCTURE OF THE COMPASS™ MODEL
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EXHIBIT A-18: HIERARCHICAL STRUCTURE OF THE MODAL SPLIT MODEL
Total Demand
Public Auto Modes Mode
Air Surface Mode Modes
Passenger Rail Bus Mode Mode
A.3.2 RIDERSHIP AND REVENUE FORECAST RESULTS
Exhibits A-19 and A-20 present the 2025 High-Speed and Intercity Passenger Rail ridership and revenue forecast for the Hampton Roads-Richmond-Washington D.C. Corridor, the forecasts were done for the CSXT/I-64 and NS/Route 460 corridor separately. Forecasts are given for the year 2025 in order to allow a direct comparison of each investment strategy. It is shown that the annual corridor riders (a rider is defined as a passenger making a one-way trip, a round trip generates two riders.) increase from 0.5 million in Step 1 and to 3.5 million in Step 4, and the corresponding annual fare-box revenues are 17 million and 228 million respectively.
If in the short term the frequency on the NS/Route 460 corridor is increased from one to three trains per day, the ridership will increase to approximately 400,000 riders in 2015.
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Hampton Roads High-Speed and Intercity Passenger Rail Preliminary Vision Plan Progress Report A: Preliminary Ridership and Revenue Forecasts
EXHIBIT A-19: 2025 HAMPTON ROADS-RICHMOND-WASHINGTON LOCAL CORRIDOR RIDERSHIP (MILLIONS)
4.5
4.0 4.0
3.5
3.0 2.8
2.5
n 2.5 CSXT/I-64 NS/Route 460 Millio 2.0 1.8 Total
1.5 1.4 1.5
1.1 1.0 1.0
0.5 0.4 0.5 0.3 0.2 0.0 Step 1 Step 2 Step 3 Step 4
EXHIBIT 20: 2025 HAMPTON ROADS-RICHMOND-WASHINGTON LOCAL CORRIDOR REVENUE (MILLIONS $2010)
250 228
200
148
0 150 128 CSXT/I-64 NS/Route 460 Total Million $201 100 85 80 68
47 50 42
17 20 10 7 0 Step 1 Step 2 Step 3 Step 4
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Hampton Roads High-Speed and Intercity Passenger Rail Preliminary Vision Plan Progress Report A: Preliminary Ridership and Revenue Forecasts
Ridership and revenue forecasts shown in Exhibits A-19 and A-20 do not include trips that connect to the Northeast corridor and Southeast corridor from the Hampton Roads-Richmond-Washington Corridor. Exhibits A-21 and A-22 show the ridership and revenue forecasts with the connecting trips
EXHIBIT A-21: 2025 HAMPTON ROADS-RICHMOND-WASHINGTON CORRIDOR RIDERSHIP WITH CONNECTING TRIPS (MILLIONS)
7.0 6.3
6.0
5.0 4.3 4.0 4.0 n CSXT/I-64 NS/Route 460 Millio 3.0 2.8 Total 2.4 2.3
2.0 1.6 1.5
1.0 0.8 0.7 0.4 0.4
0.0 Step 1Step 2Step 3Step 4
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EXHIBIT A-22: 2025 HAMPTON ROADS-RICHMOND-WASHINGTON CORRIDOR REVENUE WITH CONNECTING TRIPS (MILLIONS $2010)
600
511 500
400
0 332 CSXT/I-64 294 300 NS/Route 460 Total Million $201 198 200 179 161
111 96 100 49 50 31 18 0 Step 1 Step 2 Step 3 Step 4
The preliminary station volumes for the Hampton Roads-Richmond-Washington High-Speed and Intercity Passenger Rail corridor of 2025 for different scenarios are given in Appendix C of this report.
The 2025 ridership mode split share for the corridor is illustrated in Exhibit A-23. The 2025 auto mode split share illustrates that auto mode continues to demonstrate its dominance. Passenger Rail riders represent 1.5 percent of all travel in the corridor in Step 1 rising to an 11 percent share of Passenger Rail travel in Step 4.
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Hampton Roads High-Speed and Intercity Passenger Rail Preliminary Vision Plan Progress Report A: Preliminary Ridership and Revenue Forecasts
EXHIBIT A-23: 2025 HAMPTON ROADS-RICHMOND-WASHINGTON CORRIDOR TRAVEL MARKET SHARES
Step 1 Step 2
Rail Air Bus Rail Air Bus 1.5% 3.2% 1.0% 4.2% 2.7% 0.8%
Air Bus Car Rail
Car Car 92.3% 94.4%
Step 3 Step 4 Rail Air Bus Air Bus 7.7% 2.3%0.6% Rail 11.1% 2.0%0.5%
Car Car 89.4% 86.3%
Exhibit A-24 illustrates the sources of High-Speed Passenger Rail trips of Step 4 in 2025. It can be seen that for both the CSXT/I-64 and NS/Route 460 corridors, natural growth accounts for a small proportion of the total Passenger Rail travel market, which is in line with the results of other studies. The induced growth accounts for 7.6 percent of the Passenger Rail travel for the CSXT/I-64 corridor, while it is 9.5 percent for the NS/Route 460 corridor. The induced growth is higher for the NS/Route 460 corridor because, there is no Passenger Rail service currently available in that corridor, the proposed level of service for Passenger Rail is higher in that corridor, and the highway system is limited. It can be also seen that the main part of Passenger Rail ridership is diverted from Auto mode, which are 90.5 percent for the CSXT/I-64 corridor and 88.9 percent for the NS/Route 460 corridor, which is typical for 200- to 500-mile long High-Speed and Intercity Passenger Rail corridors. Air diversion is low at 4 to 5 percent of the total air trips and only provides 2-3 percent of the Passenger Rail trips.
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Hampton Roads High-Speed and Intercity Passenger Rail Preliminary Vision Plan Progress Report A: Preliminary Ridership and Revenue Forecasts
EXHIBIT A-24: 2025 SOURCES OF HIGH-SPEED RAIL TRIPS
CSXT/I-64 Corridor NS/Route 460 Corridor
Natural Growth Induced Growth Natural Growth Induced Growth 1.6% 9.5% 1.9% 7.6%
Diverted Growth Diverted Growth 88.9% 90.5% Natural Growth Induced Growth Diverted Growth
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TECHNICAL APPENDIX
A SOCIOECONOMIC DATA
The study area is divided into 274 analysis zones:
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Hampton Roads High-Speed and Intercity Passenger Rail Preliminary Vision Plan Progress Report A: Preliminary Ridership and Revenue Forecasts Zone State County Centroid Name 2010 Population 2010 Employment 2010 Per Capita Income 1 VA Norfolk Co. Norfolk (Downtown) 9,713 32,919 36,497 2 VA Norfolk Co. Lamberts Point - Colonial Place 20,016 7,669 40,005 3 VA Norfolk Co. Fairmount Park - Lafayette Annex 19,040 2,911 32,290 4 VA Norfolk Co. Glenwood Park 23,506 73,098 21,169 5 VA Norfolk Co. Norfolk International Airport 41,671 11,925 31,646 6 VA Virginia Beach Co. Virginia Beach 42,642 30,165 65,344 7 VA Virginia Beach Co. Chinese Corner 77,044 61,517 43,087 8 VA Virginia Beach Co. Oceana Naval Air Station 36,175 27,283 33,116 9 VA Norfolk Co. Berkley - Campostella 7,427 7,557 18,225 10 VA Portsmouth Co. Portsmouth 22,124 32,902 27,105 11 VA Portsmouth Co. Victory Park 35,759 9,974 31,166 12 VA Portsmouth Co. Arostead Forest - Craney Island 27,357 5,489 38,947 13 VA Chesapeake Co. Bowers Hill 11,915 7,041 35,098 14 Va Chesapeake Co. Boone 23,095 13,320 44,901 15 VA Chesapeake Co. Loxley Gardens - Geneva Park 20,395 6,451 38,724 16 VA Chesapeake Co. Westover 43,622 25,583 32,543 17 VA Chesapeake Co. Chesapeake 51,160 15,554 45,957 18 VA Suffolk Co. Bennett Corner 31,006 9,339 41,092 19 VA Suffolk Co. Suffolk 27,955 19,216 28,659 20 VA Suffolk Co. Holland 6,421 1,139 35,948 21 VA Suffolk Co. Kings Fork 19,897 6,698 34,744 22 VA Isle of Wright Co. Smithfield 36,819 18,129 39,113 23 VA Newport News Newport News (Downtown South) 8,231 7,848 15,972 24 VA Newport News Newport News Amtrak Station 4,723 3,590 34,829 25 VA Newport News Newport News (Downtown North) 13,543 24,236 26,041 26 VA Newport News Newport News (Reed) 12,295 1,545 24,128 27 VA Newport News Glendale - Beaconsville 41,356 18,199 38,495 28 VA Newport News Denbigh 56,747 21,217 29,824 29 VA Hampton Hampton (West) 14,320 12,918 35,720 30 VA Hampton Hampton (Downtown) 12,771 6,957 29,659 31 VA Hampton Fox Corner 21,924 5,242 34,279 32 VA Hampton Chapel Village 10,458 12,327 30,893 33 VA Poquoson Poquoson 11,908 3,114 45,418 34 VA York Co. Yorktown (Rt. 134 & Rt. 600) 20,223 2,645 33,442 35 VA York Co. Yorktown (West) 10,054 8,908 44,841 36 VA York Co. Greensprings-Plantation Heights 923 3,717 43,601 37 VA York Co. Skimino 3,170 4,517 43,601 38 VA York Co. Charleston Heights - York Terrace 7,051 7,614 51,285 39 VA Williamsburg Williamsburg 7,101 19,314 20,803 40 VA Williamsburg Co. Williamsburg (Southeast - Forest Hill Park) 5,931 6,633 39,432 41 VA James City James Terrace - Grove 9,281 13,990 71,624 42 VA James City Jamestown - Hollybrook 13,077 3,951 61,090 43 VA James City Canterbury Hills - Jamestown Farms 27,687 13,585 51,836 44 VA James City Toano 14,253 4,655 42,927 45 VA Gloucester Gloucester 26,799 12,289 36,494 46 VA New Kent Co. New Kent 6,452 2,112 38,976 47 VA Charles City Sherwood Forest - Rustic 1,076 755 35,420 48 VA New Kent Co. Woodhaven Shores - New Kent Co. Airport 12,030 3,889 35,893 49 VA Charles City Charles City 6,153 1,992 35,965 50 VA Prince George Ethridge Estates 5,562 2,237 42,003
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Zone State County Centroid Name 2010 Population 2010 Employment 2010 Per Capita Income 51 VA Prince George Fort Lee 11,470 14,250 22,983 52 VA Prince George Rt. 106 & Rt. 156 8,995 2,991 43,074 53 VA Dinwiddie Co. Petersburg (Dinwiddie County Airport - PTB) 10,890 6,556 31,267 54 VA Petersburg Co. Petersburg (Blandford) 3,872 1,559 19,847 55 VA Petersburg Co. Berkley Manor 1,736 1,421 33,359 56 VA Petersburg Co. Petersburg (Downtown) 8,267 6,276 23,036 57 VA Petersburg Co. Petersburg (Kennelworth) 8,867 1,635 25,968 58 VA Petersburg Co. Camelot 1,051 245 45,222 59 VA Petersburg Co. Petersburg (South) 9,104 4,828 39,680 60 VA Colonial Heights Colonial Heights 13,125 4,805 39,375 61 VA Colonial Heights Colonial Heights (East) 4,913 5,382 53,853 62 VA Chesterfield Co. Ettrick (Amtrak Petersburg) 4,183 2,115 17,374 63 VA Hopewell City Hopewell 23,808 10,701 28,434 64 VA Chesterfield Co. Matoaca 9,942 743 35,717 65 VA Chesapeake Co. Grassfield - Chesapeake Regional Apt. 15,289 4,309 41,108 66 VA Chesterfield Co. Screamersville 5,986 4,817 44,455 67 VA Chesterfield Co. Pickadat Corner 10,205 1,664 39,135 68 VA Chesterfield Co. Meadowville - Cameron Hills 7,759 21,645 50,439 69 VA Chesterfield Co. Lake Chesdin Pkwy & Ivey Mill Rd. 2,527 98 41,413 70 VA Chesterfiled Co. Chester 36,070 20,818 41,414 71 VA Chesterfield Co. Swift Creek Resevoir 66,858 15,077 50,496 72 VA Chesterfield Co. Chesterfield County Airport 75,184 32,370 34,502 73 VA Norfolk Co. Gent-Park Place 10,861 22,553 66,237 74 VA Norfolk Co. Huntersville (Hunter's Village) 9,975 3,795 21,126 75 VA Norfolk Co. Ocean View - Willoughby Beach 33,883 2,865 36,983 76 VA Norfolk Co. Sussex - Wards Corner 18,829 7,294 42,457 77 VA Norfolk Co. Thomas Corner 38,660 51,219 34,706 78 VA Henrico Co. Sandston (Rt. 156 & Rt. 33) 7,821 2,394 47,287 79 VA Virginia Beach Co. London Bridge 68,097 41,823 52,517 80 VA Virginia Beach Co. Nimmo-Woodhouse Corner 55,168 16,737 41,687 81 VA Portsmouth Co. Westhaven Park 14,279 8,742 32,556 82 VA Chesapeake Co. Pinetta-Butts 49,735 49,924 43,212 83 VA Henrico Co. Richmond International Apt. (Sandston) 30,837 19,687 44,446 84 VA Henrico Co. East Highland Park 55,076 12,717 38,056 85 VA Virginia Beach Co. St. Brides 10,269 5,644 39,796 86 VA Newport News Deer Park - Harpersville 18,941 23,059 32,297 87 VA Henrico Co. Laurel 108,829 108,722 57,040 88 VA Newport News Newport News/Williamsburg International Airport 38,109 20,757 33,899 89 VA Hampton Hampton (East) 56,552 16,622 35,340 90 VA Hampton Greenwood Farms 8,360 1,867 32,304 91 VA Hampton Drummonds Corner 20,220 25,883 38,092 92 VA York Co. Yorktown - Grafton 20,554 8,689 56,279 93 VA Richmond City Richmond (Downtown-East) 3,843 39,904 59,157 94 VA Richmond City Church Hill 25,709 2,538 24,252 95 VA Richmond City Ginter Park - Hotchkiss Field 35,724 12,688 37,938 96 VA Richmond City Richmond (Downtown-West) 5,010 32,112 41,712 97 VA Richmond City Richmond (The Fan District) 33,554 40,601 50,469 98 VA Richmond City Richmond (West End) 16,856 9,506 92,686 99 MD Calvert Prince Frederick 91,749 36,130 44,944 100 VA Richmond City Richmond (Southside) 83,784 40,552 41,293
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Hampton Roads High-Speed and Intercity Passenger Rail Preliminary Vision Plan Progress Report A: Preliminary Ridership and Revenue Forecasts Zone State County Centroid Name 2010 Population 2010 Employment 2010 Per Capita Income 101 VA City of Alexandria Alexandria (Old Town) 61,923 56,789 87,185 102 VA Arlington Metro-Ballston Station 45,392 145,210 73,221 103 VA Powhatan Co. Powhatan (Rt. 60 & Dorset Rd.) 17,735 8,821 45,145 104 VA Powhatan Co. Powhatan (Rt. 522 & Three Bridges Rd.) 10,607 2,185 35,412 105 MD Carroll Westminster 175,900 89,350 44,608 106 VA Goochland Co. Sabot 11,568 13,023 89,867 107 VA Goochland Co. Goochland 10,057 7,248 41,021 108 MD Charles St. Charles 144,950 63,284 44,269 109 MD Cecil Elkton 103,850 43,967 39,085 110 MD St. Mary's Lexington Park 105,398 64,747 40,632 111 VA Hanover Co. Mechanicsville (Henry Clay Heights) 71,560 50,776 46,485 112 VA Hanover Co. Ashland 11,844 7,463 43,243 113 VA Hanover Co. Goodallr-Farrington 18,689 2,539 43,414 114 MD Montgomery Gaithersburg-Rockville-Bethesda 979,131 671,622 69,096 115 MD Prince George's College Park 862,800 450,271 40,223 116 MD Anne Arundel Annapolis 525,255 379,599 54,381 117 MD Harford Bel Air 249,306 126,089 45,247 118 MD Baltimore Towson 801,750 529,589 50,386 119 MD Frederick Frederick 233,600 139,492 45,673 120 MD Baltimore city Downtown 50,906 149,870 39,393 121 MD Baltimore city Johns Hospkins Hospital 282,246 130,580 36,647 122 MD Baltimore city Brooklyn Manor 12,657 9,409 28,196 123 MD Baltimore city South Baltimore - Locust Point 9,570 8,140 58,209 124 MD Baltimore city Druid Hill Park - Mondawmin Mall 289,472 105,597 32,730 125 RI Bristol Bristol 51,088 24,480 53,713 126 RI Kent Warwick 170,668 106,819 44,493 127 RI Providence Providence 635,005 361,606 37,734 128 RI Newport Newport 84,044 56,663 51,549 129 RI Washington Wakefield-Westerly 131,197 78,558 48,277 130 PA Bucks Levittown 636,431 388,761 53,264 131 PA Montgomery Norristown 800,051 633,400 64,654 132 NJ Philadelphia Philadelphia 1,544,870 769,255 33,896 133 PA Delaware Springfield-Media 555,410 297,587 50,430 134 PA Chester Downingtown-Exton 513,655 355,343 62,686 135 DC City of Washington The National Mall 13,288 95,608 65,084 136 DC City of Washington Capitol Hill - Union Station 80,054 132,820 52,682 137 DC City of Washington Washington Hospital Center 70,947 55,351 41,280 138 DC City of Washington Wesley Heights 78,804 61,295 125,206 139 DC City of Washington Brightwood 47,897 21,340 47,039 140 DC City of Washington Congress Heights 84,739 27,799 29,812 141 DC City of Washington Capital View 56,711 10,790 31,933 142 DC City of Washington Chevy Chase 18,971 2,907 114,914 143 DC City of Washington Downtown DC 27,407 375,628 97,275 144 DC City of Washington Logan Circle 127,349 47,269 61,599 145 CT Hartford Hartford-Glastonbury 873,869 652,486 52,446 146 CT New London Norwich-New London 265,775 179,978 47,474 147 CT New Haven New Haven 849,600 509,700 47,887 148 CT Middlesex Middletown 166,634 102,770 53,324 149 CT Fairfield Bridgeport 916,348 637,035 82,146 150 VA Dinwiddie Co. Dinwiddie 15,889 9,301 41,691
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Zone State County Centroid Name 2010 Population 2010 Employment 2010 Per Capita Income 151 VA Prince George's Co. Templeton 12,016 1,908 37,040 152 VA Gloucester Dutton 13,675 3,470 29,257 153 VA Loudoun Leesburg 304,609 189,340 54,160 154 VA Fairfax City Fairfax 1,072,727 889,442 72,445 155 VA Fauquier Warrenton 72,685 38,641 55,454 156 VA Prince William Manassas 455,238 332,131 44,135 157 VA Arlington Pentagon 173,619 69,291 61,966 158 VA Virginia Beach Co. Pecan Gardens 69,310 55,069 35,833 159 VA Virginia Beach Co. Acredale 89,172 23,688 40,584 160 VA City of Alexandria Landmark - Van Dorn 88,836 69,178 63,169 161 VA Chesterfield Co. Robious & Hylton Park 93,739 79,305 53,466 162 VA Henrico Co. Tuckahoe 98,379 80,057 67,937 163 VA Culpeper Culpeper 48,074 21,801 36,845 164 VA Stafford Stafford 133,180 49,529 40,068 165 VA King George King George 24,204 17,457 38,511 166 VA Spotsylvania Fredericksburg 156,402 76,935 39,446 167 MD Howard Columbia 285,116 201,477 63,407 168 VA Westmoreland Hague 17,744 5,940 33,546 169 VA Caroline Bowling Green 29,201 9,907 34,190 170 VA Essex Tappahannock 11,257 5,736 32,566 171 VA Richmond Warsaw 9,333 3,917 25,550 172 VA Northumberland Heathsville 13,420 4,728 37,407 173 VA King and Queen Mattaponi 6,891 2,442 32,854 174 VA King William King William 16,469 5,524 38,853 175 VA Lancaster Irvington 11,485 7,446 48,368 176 VA Middlesex Topping-Deltaville 11,012 5,358 39,397 177 VA Mathews Foster 9,097 3,953 48,504 178 VA Surry Co. Surry 7,210 2,715 32,409 179 VA Lunenburg Lunenburg 13,172 4,200 24,797 180 VA Sussex Co. Waverly 11,543 4,542 29,350 181 VA Brunswick Lawrenceville 18,263 6,739 24,468 182 VA South Hampton Co. Franklin 26,316 12,151 33,470 183 VA Emporia Emporia 17,072 10,567 24,110 184 VA Mecklenburg South Mill 32,369 18,141 29,809 185 DE New Castle Wilmington 535,572 370,116 47,449 186 MA Plymouth Co. Plymouth-Kingston 496,053 270,469 48,934 187 MA Bristol Co. Taunton 546,922 282,745 40,142 188 NY Nassau Hempstead 1,349,616 849,911 66,260 189 NY Kings Brooklyn 2,564,778 783,942 33,343 190 NY Westchester Yonkers-New Rochelle 964,914 603,151 75,752 191 NY Bronx Bronx 1,411,792 354,742 27,157 192 NY New York New York City 1,638,525 2,845,765 117,333 193 NY Richmond Staten Island 506,490 147,722 46,135 194 NY Queens Queens 2,350,032 776,425 36,744 195 NY Putnam Carmel 103,186 44,340 54,345 196 NY Rockland Spring Valley 302,616 160,799 56,384 197 NC Harnett Dunn 118,448 46,173 30,734 198 NC Cumberland Fayetteville 318,699 215,669 36,899 199 NC Pitt Greenville 162,605 96,868 34,796 200 NC Gates Gatesville 11,922 2,727 27,866 Transportation Economics & Management Systems, Inc. A-5
Hampton Roads High-Speed and Intercity Passenger Rail Preliminary Vision Plan Progress Report A: Preliminary Ridership and Revenue Forecasts
Zone State County Centroid Name 2010 Population 2010 Employment 2010 Per Capita Income 201 NC Camden Camden 9,883 4,290 34,886 202 NC Currituck Currituck 24,296 10,528 36,709 203 NC Stokes King 46,577 13,720 31,630 204 NC Northampton Jackson 20,281 8,845 30,935 205 NC Hertford Ahoskie 23,359 12,308 27,201 206 NC Warren Warrenton 19,489 5,161 24,822 207 NC Vance Henderson 43,170 18,562 29,632 208 NC Granville Oxford 58,099 27,081 30,162 209 NC Halifax Rosemary 54,591 23,861 28,378 210 NC Pasquotank Elizabeth City 41,717 25,817 29,312 211 NC Perquimas Hertford 13,085 4,095 30,200 212 NC Chowarr Edenton 14,815 8,149 33,785 213 NC Yadkin Yadkinville 38,220 14,765 32,186 214 NC Franklin Franklinton 60,960 23,941 32,910 215 NC Forsyth Winston-Salem 363,526 243,123 42,488 216 NC Guildford Greensboro 487,749 361,594 42,536 217 NC Alamance Burlington 153,366 83,866 34,883 218 NC Orange Chapel Hill 131,165 84,987 47,508 219 NC Durham Durham 275,926 243,915 42,308 220 NC Nash Rocky Mount 96,021 56,598 35,940 221 NC Edgecombe Tarboro 51,767 26,003 29,886 222 NC Wake Raleigh 930,005 612,011 48,189 223 NC Davie Mocksville 42,128 17,633 40,726 224 NC Davidson Lexington 161,419 76,570 34,935 225 NC Dare Manteo 33,968 32,003 39,900 226 NC Randolph Asheboro 144,050 67,092 31,671 227 NC Chatham Siler City 65,902 41,170 47,243 228 NC Wilson Wilson 79,679 48,124 34,333 229 NC Rowan Salisbury 143,025 59,608 32,866 230 NC Selma Smithfield 174,106 79,118 35,670 231 NC Lincoln-Boger City Lincolnton 78,010 29,721 33,586 232 NC Mecklenburg Charlotte 926,736 749,354 51,411 233 NC Cabarrus Concord 178,852 99,800 40,070 234 NC Gaston Gastonia 213,279 101,198 36,367 235 NC Union Monroe 207,372 85,923 36,611 236 NC Catawba Hickory 160,796 102,396 36,521 237 NC Moore Southern Pines 88,609 46,831 44,576 238 NC Hoke Raeford-Silver City 46,305 14,578 25,398 239 NC Lee Sanford 61,338 36,283 35,067 240 NJ Sussex Sussex 153,558 70,076 50,384 241 NJ Passaic Paterson 493,247 244,390 41,354 242 NJ Bergen Paramus 898,077 625,274 70,286 243 NJ Warren Phillipsburg 111,420 52,518 44,268 244 NJ Morris Parsippany Troy Hills 493,564 400,771 76,174 245 NJ Essex Newark 778,671 482,121 51,253 246 NJ Hudson Jersey City-Hoboken 599,998 315,145 40,774 247 NJ Hunterdon Flemington 132,125 82,942 73,186 248 NJ Somerset Bridgewater-Somerville 329,899 227,680 77,220 249 NJ Union Elizabeth 526,013 304,602 52,891 250 NJ Middlesex New Brunswick 796,749 520,049 49,704
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Hampton Roads High-Speed and Intercity Passenger Rail Preliminary Vision Plan Progress Report A: Preliminary Ridership and Revenue Forecasts
Zone State County Centroid Name 2010 Population 2010 Employment 2010 Per Capita Income 251 NJ Mercer Trenton 369,745 282,323 55,228 252 NJ Burlington Willingboro 454,117 288,974 48,311 253 NJ Camden Camden 519,409 284,125 41,921 254 NJ Gloucester Woodbury 291,210 140,739 39,500 255 NJ Salem Penns Grove-Carneys Point 66,646 31,616 38,545 256 MA Essex Lawrence 744,424 409,218 51,764 257 MA Middlesex Cambridge - Burlington 1,486,918 1,076,833 62,453 258 MA Worcester Worcester 787,825 431,778 43,906 259 MA Suffolk Co. Boston 738,379 695,642 53,374 260 MA Norfolk Co. Quincy 663,989 435,296 64,674
* Zones 261~274 are reserved dummy airport zones
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Hampton Roads High-Speed and Intercity Passenger Rail Preliminary Vision Plan Progress Report A: Preliminary Ridership and Revenue Forecasts
B COMPASS™ MODEL
The COMPASS™ Model System is a flexible multimodal demand-forecasting tool that provides comparative evaluations of alternative socioeconomic and network scenarios. It also allows input variables to be modified to test the sensitivity of demand to various parameters such as elasticities, values of time, and values of frequency. This section describes in detail the model methodology and process used in the study.
B.1 DESCRIPTION OF THE COMPASS™ MODEL SYSTEM
The COMPASS™ Model is structured on two principal models: Total Demand Model and Hierarchical Modal Split Model. For this study, these two models were calibrated separately for three trip purposes, i.e., Business, Commuter, and Social/Recreational. For each market segment, the models were calibrated on origin-destination trip data, network characteristics and base year socioeconomic data.
The models were calibrated on the base year data. In applying the models for forecasting, an incremental approach known as the “pivot point” method is used. By applying model growth rates to the base data observations, the “pivot point” method is able to preserve the unique travel flows present in the base data that are not captured by the model variables. Details on how this method is implemented are described below.
B.2 TOTAL DEMAND MODEL
The Total Demand Model, shown in Equation 1, provides a mechanism for assessing overall growth in the travel market.
Equation 1:
β0p β1p β2p Uijp Tijp = e (SEijp) e
Where,
Tijp = Number of trips between zones i and j for trip purpose p
SEijp = Socioeconomic variables for zones i and j for trip purpose p
Uijp = Total utility of the transportation system for zones i to j for trip purpose p β β β = Coefficients for trip purpose p 0 p, 1 p, 2 p
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As shown in Equation 1, the total number of trips between any two zones for all modes of travel, segmented by trip purpose, is a function of the socioeconomic characteristics of the zones and the total utility of the transportation system that exists between the two zones. For this study, trip purposes include Business, Commuter, and Social/Recreational. The socioeconomic characteristics consist of population, employment, and income. The utility function provides a measure of the quality of the transportation system in terms of the times, costs, reliability and level of service provided by all modes for a given trip purpose. The Total Demand Model equation may be interpreted as meaning that travel between zones will increase as socioeconomic factors such as population and income rise or as the utility (or quality) of the transportation system is improved by providing new facilities and services that reduce travel times and/or costs. The Total Demand Model can therefore be used to evaluate the effect of changes in both socioeconomic and travel characteristics on the total demand for travel.
B.2.1 SOCIOECONOMIC VARIABLES
The socioeconomic variables in the Total Demand Model show the impact of economic growth on travel demand. The COMPASS™ Model System, in line with most intercity modeling systems, uses three variables (population, employment, and income) to represent the socioeconomic characteristics of a zone. Different combinations were tested in the calibration process and it was found, as is typically found elsewhere, that the most reasonable and statistically stable relationships consists of the following formulations:
Trip Purpose Socioeconomic Variable
Business Ei Ej ( Ii + Ij ) / 2
Commuter (PiEj+PjEi) / 2 (Ii+Ij) / 2
Social/Recreational Pi Pj ( Ii + Ij ) / 2
The Business formulation consists of a product of employment in the origin zone, employment in the destination zone, and income of the two zones. Since business trips are usually made between places of work, the presence of employment in the formulation is reasonable. While the income factor is correlated to the type of employment, higher income levels generate more Business trips. The Commuter formulation consists of all socioeconomic factors, this is because commuter trips are between homes and places of work, which are closely related to population and employment, and income factor is related to the wealth of the origin zone and the type of employment in the destination zone. The formulation for Social and Recreational trip purposes consists of a product of population in the origin zone, population in the destination zone and income of the two zones. Such trip purposes encompass many types of trips, but the majority is home-based and thus, greater volumes of trips are expected from zones with higher population and income.
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Hampton Roads High-Speed and Intercity Passenger Rail Preliminary Vision Plan Progress Report A: Preliminary Ridership and Revenue Forecasts
B.2.2 TRAVEL UTILITY
Estimates of travel utility for a transportation network are generated as a function of generalized cost (GC), as shown in Equation 2:
Equation 2:
Uijp = f(GCijp) where,
GCijp = Generalized Cost of travel between zones i and j for trip purpose p
Because the generalized cost variable is used to estimate the impact of improvements in the transportation system on the overall level of trip making, it needs to incorporate all the key attributes that affect an individual’s decision to make trips. For the public modes (i.e., passenger rail and bus), the generalized cost of travel includes all aspects of travel time (access, egress, in-vehicle times), travel cost (fares), and schedule convenience (frequency of service, convenience of arrival/departure times). For auto travel, full average cost of operating a car is used for Business, while only the marginal cost is used for Commuter and Social/Recreational trips. In addition, tolls and parking charges are used where appropriate.
The generalized cost of travel is typically defined in travel time (i.e., minutes) rather than dollars. Costs are converted to time by applying appropriate conversion factors, as shown in Equation 3. The generalized cost (GC) of travel between zones i and j for mode m and trip purpose p is calculated as follows:
Equation 3:
* OH * exp(α * F ) + TC ijmp VOF mp GC ijmp = TT ijm + 2 VOT mp VOT mp *α * F ijm
Where,
TTijm = Travel Time between zones i and j for mode m (in-vehicle time + station wait time + connection wait time + access/egress time + interchange penalty), with waiting, connect and access/egress time multiplied by a factor (greater than 1) to account for the additional disutility felt by travelers for these activities
TCijmp = Travel Cost between zones i and j for mode m and trip purpose p (fare + access/egress cost for public modes, operating costs for auto)
VOTmp = Value of Time for mode m and trip purpose p
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VOFmp = Value of Frequency for mode m and trip purpose p
Fijm = Frequency in departures per week between zones i and j for mode m a = Frequency damping factor OH = Operating hours per week
Station wait time is the time spent at the station before departure and after arrival. On trips with connections, there would be additional wait times incurred at the connecting station. Wait times are weighted higher than in-vehicle time in the generalized cost formula to reflect their higher disutility as found from previous studies. Wait times are weighted 70 percent higher than in-vehicle time. Similarly, access/egress time has a higher disutility than in-vehicle time. Access time tends to be more stressful for the traveler than in-vehicle time because of the uncertainty created by trying to catch the flight or train. Based on previous work, access time is weighted 80 percent higher for passenger rail and bus travel. The third term in the generalized cost function converts the frequency attribute into time units. Operating hours divided by frequency is a measure of the headway or time between departures. Tradeoffs are made for the value of frequencies on this measure. Although there may appear to some double counting because the station wait time in the first term of the generalized cost function is included in this headway measure, it is not the headway time itself that is being added to the generalized cost. The third term represents the impact of perceived frequency valuations on generalized cost. TEMS has found it effective to measure this impact as a function of the headway.
B.2.3 CALIBRATION OF THE TOTAL DEMAND MODEL
In order to calibrate the Total Demand Model, the coefficients are estimated using linear regression techniques. Equation 1, the equation for the Total Demand Model, is transformed by taking the natural logarithm of both sides, as shown in Equation 4:
Equation 4:
= β + β + β log(Tijp ) 0 p 1p log(SEijp ) 2 p (Uijp )
Equation 4 provides the linear specification of the model necessary for regression analysis.
The segmentation of the database by trip resulted in three sets of models, one for each trip purpose. The results of the calibration for the Total Demand Models are displayed in Exhibit B-1.
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(1) EXHIBIT B-1: TOTAL DEMAND MODEL COEFFICIENTS
2 Business log(Tij) = -11.38 + 0.76 log(SEij) + 0.93 Uij R=0.67 (17) (262)
where Uij = log[exp(-4.69+ 0.96UPublic ) + exp(-0.015 GCCar)]
2 Commuter log(Tij) = - 7.88 + 0.66 log(SEij) + 1.17 Uij R=0.73 (21) (38)
where Uij = log[exp(-5.53+ 0.72UPublic ) + exp(-0.016 GCCar)]
2 Social/Recreational log(Tij) = - 6.99 + 0.64 log(SEij) + 1.23 Uij R=0.65 (24) (48)
where Uij = log[exp(-5.73+ 0.66UPublic ) + exp(-0.015 GCCar)]
(1) t-statistics are given in parentheses.
In evaluating the validity of a statistical calibration, there are two key statistical measures: t-statistics and R2. The t-statistics are a measure of the significance of the model’s coefficients; values of 1.95 and above are considered “good” and imply that the variable has significant explanatory power in estimating the level of trips. The R2 is a statistical measure of the “goodness of fit” of the model to the data; any data point that deviates from the model will reduce this measure. It has a range from 0 to a perfect 1, with 0.3 and above considered “good” for large data sets. Based on these two measures, the total demand calibrations are good. The t-statistics are high, aided by the large size of the data set. The R2 values imply good fits of the equations to the data.
As shown in Exhibit B-1, the socioeconomic elasticity values for the Total Demand Model are in the range of 0.64 to 0.76, meaning that each one percent growth in the socioeconomic term generates approximately a 0.64 to 0.76 percent growth in trips.
The coefficient on the utility term is not strictly elasticity, but it can be considered an approximation. The utility term is related to the scale of the generalized costs, for example, utility elasticity can be high if the
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Hampton Roads High-Speed and Intercity Passenger Rail Preliminary Vision Plan Progress Report A: Preliminary Ridership and Revenue Forecasts absolute value of transportation utility improvement is significant. This is not untypical when new transportation systems are built. In these cases, a 20 percent reduction in utility is not unusual and may impact more heavily on longer origin-destination pairs than shorter origin-destination pairs.
B.2.4 INCREMENTAL FORM OF THE TOTAL DEMAND MODEL
The calibrated Total Demand Models could be used to estimate the total travel market for any zone pair using the population, employment, income, and the total utility of all the modes. However, there would be significant differences between estimated and observed levels of trip making for many zone pairs despite the good fit of the models to the data. To preserve the unique travel patterns contained in the base data, the incremental approach or “pivot point” method is used for forecasting. In the incremental approach, the base travel data assembled in the database are used as pivot points, and forecasts are made by applying trends to the base data. The total demand equation as described in Equation 1 can be rewritten into the following incremental form that can be used for forecasting (Equation 5):
β Equation 5: f f 1 p T ⎛ SE ⎞ ijp = ⎜ ijp ⎟ exp( β ( U f − U b )) b ⎜ b ⎟ 2 p ijp ijp Tijp ⎝ SEijp ⎠
Where,
f Tijp = Number of Trips between zones i and j for trip purpose p in forecast year f f T ijp = Number of Trips between zones i and j for trip purpose p in base year b f SE ijp = Socioeconomic variables for zones i and j for trip purpose p in forecast year f b SE ijp = Socioeconomic variables for zones i and j for trip purpose p in base year b f Uijp = Total utility of the transportation system for zones i to j for trip purpose p in forecast year f b U ijp = Total utility of the transportation system for zones i to j for trip purpose p in base year b
In the incremental form, the constant term disappears and only the elasticities are important.
B.3 HIERARCHICAL MODAL SPLIT MODEL
The role of the Hierarchical Modal Split Model is to estimate relative modal shares, given the Total Demand Model estimate of the total market that consists of different travel modes available to travelers. The relative modal shares are derived by comparing the relative levels of service offered by each of the travel
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EXHIBIT B-2: HIERARCHICAL STRUCTURE OF THE MODAL SPLIT MODEL
Total
Demand
Public Auto
Modes Mode
Air Surface
Mode Modes
Rail Bus Mode Mode
The main feature of the Hierarchical Modal Split Model structure is the increasing commonality of travel characteristics as the structure descends. The upper level of the hierarchy separates private auto travel – with its spontaneous frequency, low access/egress times, low costs and highly personalized characteristics – from the public modes. The second level separates air – the fastest and most expensive public mode – from the passenger rail and bus surface modes. The lower separates passenger rail – a faster and more comfortable public mode from bus, which provides slower conventional services within the corridor.
B.3.1 FORM OF THE HIERARCHICAL MODAL SPLIT MODEL
The modal split models used by TEMS derived from the standard nested logit model. Exhibit B-3 shows a typical two-level standard nested model. In the nested model shown in Exhibit B-3, there are four travel modes that are grouped into two composite modes, namely, Composite Mode 1 and Composite Mode 2.
EXHIBIT B-3: A TYPICAL STANDARD NESTED LOGIT MODEL
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Total Demand
Composite Composite Mode 1 Mode 2
Mode 1-1 Mode 1-2 Mode 2-1 Mode 2-2
Each travel mode in the above model has a utility function of Uj, j = 1, 2, 3, 4. To assess modal split behavior, the logsum utility function, which is derived from travel utility theory, has been adopted for the composite modes in the model. As the modal split hierarchy ascends, the logsum utility values are derived by combining the utility of lower-level modes. The composite utility is calculated by
UU=+αβlog exp( ρ ) (1) NNNkkk∑ i ∈ iNk where
Nk is composite mode k in the modal split model, i is the travel mode in each nest,
Ui is the utility of each travel mode in the nest, ρ is the nesting coefficient.
The probability that composite mode k is chosen by a traveler is given by
ρ exp(U N / ) PN()= k (2) k exp(U /ρ ) ∑ Ni ∈ NNi
The probability of mode i in composite mode k being chosen is
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exp(ρU ) Pi()= i (3) Nk ρ ∑ exp(U j ) ∈ jNk
A key feature of these models is a use of utility. Typically in transportation modeling, the utility of travel between zones i and j by mode m for purpose p is a function of all the components of travel time, travel cost, terminal wait time and cost, parking cost, etc. This is measured by generalized cost developed for each origin-destination zone pair on a mode and purpose basis. In the model application, the utility for each mode is estimated by calibrating a utility function against the revealed base year mode choice and generalized cost.
Using logsum functions, the generalized cost is then transformed into a composite utility for the composite mode (e.g. Public modes in Exhibit B-2). This is then used at the next level of the hierarchy to compare the next most similar mode choice (e.g. in ExhibitB-2, Public mode is compared with Auto mode).
B.3.2 DEGENERATE MODAL SPLIT MODEL
For the purpose of the Hampton Roads High-Speed and Intercity Passenger Rail Study (and other high- speed intercity passenger rail projects) TEMS has adopted a special case of the standard logit model, the degenerate nested logit model [Louviere, et.al., 2000]. This is because in modeling travel choice, TEMS has followed a hierarchy in which like modes are compared first, and then with gradually more disparate modes as progress is made up the hierarchy, this method provides the most robust and statistically valid structure. This means however, that there are singles modes being introduced at each level of the hierarchy and that at each level the composite utility of two modes combined at the lower level (e.g. the utility of Surface mode combined from Passenger Rail and Bus modes) is compared with the generalized cost of a single mode (e.g. Air mode). It is the fact that the utilities of the two modes being compared are measured by different scales that creates the term degenerate model. The result of this process is that the nesting coefficient is subsumed into the hierarchy and effectively cancels out in the calculation. That is ρ why TEMS set to 1 when using this form of the model in COMPASS™.
Take the three-level hierarchy shown in Exhibit B-2 for example, the utilities for the modes of Passenger Rail and Bus in the composite Surface mode are:
=+αβ UGCRail Rail Rail Rail (4) = β UGCBus Bus Bus (5) The utility for the composite Surface mode is
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= α + β ρ + ρ U Surface Surface Surface log[exp( U Rail ) exp( U Bus )] (6)
The utility for the Air mode is ==βρρβ UGCGCAir Airlog[exp( Air )] Air Air (7) Then the mode choice model between Surface and Air modes are exp(U /ρ ) P() Surface = Surface (8) ρρ+ exp(UUSurface / ) exp( Air / )
UGC= ρβ exp(U /ρ ) It can be seen in equation (7) that Air Air Air , the term of Air in equation (8) reduces exp(β GC ) ρ to Air Air , thus that the nesting coefficient is canceled out in the single mode nest of the ρ hierarchy. As a result, loses its statistical meaning in the nested logit hierarchy, and leads to the degenerate form of the nested logit model.
B.3.3 CALIBRATION OF THE HIERARCHICAL MODAL SPLIT MODEL
Working from the bottom of the hierarchy up to the top, the first analysis is that of the passenger rail mode versus the bus mode. As shown in Exhibit B-4, the model was effectively calibrated for the trip purposes with reasonable parameters and R2 and t values. All the coefficients have the correct signs such that demand increases or decreases in the correct direction as travel times or costs are increased or decreased, and all the coefficients appear to be reasonable in terms of the size of their impact.
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(1) EXHIBIT B-4: RAIL VERSUS BUS MODAL SPLIT MODEL COEFFICIENTS
2 Business log(PRail/PBus) = 3.76 - 0.0045 GCRail + 0.041 GCBus R=0.73 (57) (96)
2 Commuter log(PRail/PBus) = 2.64 - 0.0033 GCRail + 0.024 GCBus R=0.78 (69) (60)
2 Social/Recreational log(PRail/PBus) = 2.36 - 0.0029 GCRail + 0.020 GCBus R=0.86 (79) (82)
(1) t-statistics are given in parentheses.
The constant term in each equation indicates the degree of bias towards one mode or the other. For example, if the constant term is positive, there is a bias towards passenger rail travel that is not explained by the variables (e.g., times, costs, frequencies, reliability) used to model the modes. In considering the bias it is important to recognize that small values indicate little or no bias, and that small values have error ranges that include both positive and negative values. However, large biases may well reflect strong feelings to a modal option due to its innate character or network structure.
For the second level of the hierarchy, the mode choice analysis is for the surface modes (i.e., passenger rail and bus) versus air. Accordingly, the utility of the surface modes is obtained by deriving the logsum of the utilities of passenger rail and bus. The air mode for long distance travel displays a powerful bias against both passenger rail and bus as it provides a much faster alternative if more expensive. As shown in Exhibit B-5, the model calibrations for all trip purposes are statistically significant, with good R2 and t values and reasonable parameters.
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(1) EXHIBIT B-5: SURFACE VERSUS AIR MODAL SPLIT MODEL COEFFICIENTS
2 Business log(PSurf/PAir) = -5.91 + 0.958 VSurf + 0.008 GC Air R =0.78 (32) (48)
where VSurf = log[exp(3.76 -0.0045 GCRail) + exp(-0.041 GCBus)]
2 Commuter log(PSurf/PAir) = -3.76 + 0.792 VSurf + 0.006 GC Air R =0.41 (11) (14)
where VSurf = log[exp(2.64 -0.0033 GCRail ) + exp(-0.024 GCBus)]
2 Social/Recreational log(PSurf/PAir) = -3.22 + 0.751 VSurf + 0.005 GC Air R =0.76 (40) (50)
where VSurf = log[exp(2.36 -0.0029 GCRail ) + exp(-0.02 GCBus)]
(1) t-statistics are given in parentheses.
The analysis for the top level of the hierarchy is of auto versus the public modes. The utility of the public modes is obtained by deriving the logsum of the utilities of the air, passenger rail and bus modes in the three-level model hierarchy. As shown in Exhibit B-6, the model calibrations for both trip purposes are all statistically significant, with good R2 and t values and reasonable parameters.
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(1) EXHIBIT B-6: PUBLIC VERSUS AUTO MODAL SPLIT MODEL COEFFICIENTS
2 Business log(PPub/PAuto) = -4.69 + 0.965 VPub + 0.015 GCAuto R =0.82 (116) (78)
where VPub = log[exp(-5.91+ 0.958 VSurf ) + exp(-0.008 GCAir)]
2 Commuter log(PPub/PAuto) = -5.53 + 0.719 VPub + 0.016 GCAuto R =0.88 (88) (66)
where VPub = log[exp(-3.76+ 0.792 VSurf ) + exp(-0.006 GCAir)]
2 Social/Recreational log(PPub/PAuto) = -5.73 + 0.658 VPub + 0.015 GCAuto R =0.81 (94) (58)
where VPub = log[exp(-3.22+0.751 VSurf ) + exp(-0.005 GCAir)]
(1) t-statistics are given in parentheses.
B.3.4 INCREMENTAL FORM OF THE MODAL SPLIT MODEL
Using the same reasoning as previously described, the modal split models are applied incrementally to the base data rather than imposing the model estimated modal shares. Different regions of the corridor may have certain biases toward one form of travel over another and these differences cannot be captured with a single model for the entire system. Using the “pivot point” method, many of these differences can be retained. To apply the modal split models incrementally, the following reformulation of the hierarchical modal split models is used (Equation 6):
Equation 6:
f P A ( f ) P β ( GC f − GC b ) + γ ( GC f − GC b ) B = A B B B b e P A ( b ) P B
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For hierarchical modal split models that involve composite utilities instead of generalized costs, the composite utilities would be used in the above formula in place of generalized costs. Once again, the constant term is not used and the drivers for modal shifts are changed in generalized cost from base conditions.
Another consequence of the pivot point method is that it prevents possible extreme modal changes from current trip-making levels as a result of the calibrated modal split model, thus that avoid over- or under- estimating future demand for each mode.
B.4 INDUCED DEMAND MODEL
Induced demand refers to changes in travel demand related to improvements in a transportation system, as opposed to changes in socioeconomic factors that contribute to growth in demand. The quality or utility of the transportation system is measured in terms of total travel time, travel cost, and worth of travel by all modes for a given trip purpose. The induced demand model used the increased utility resulting from system changes to estimate the amount of new (latent) demand that will result from the implementation of the new system adjustments. The model works simultaneously with the mode split model coefficients to determine the magnitude of the modal induced demand based on the total utility changes in the system. It should be noted that the model will also forecast a reduction in trips if the quality of travel falls due to increased congestions, higher car operating costs, or increased tolls. The utility function is acting like a demand curve increasing or decreasing travel based on changes in price (utility) for travel. It assumes travel is a normal good and subject to the laws of supply and demand.
B.5 REFERENCES
[Ben-Akiva and Lerman, 1985], M.E. Ben-Akiva and S.R. Lerman, Discrete Choice Analysis: Theory and Application to Travel Demand, MIT Press, 1985. [Cascetta, 1996], E. Cascetta, Proceedings of the 13th International Symposium on the the Theory of Road Traffic Flow (Lyon, France),1996. [Daly, A, 1987], A. Daly, Estimating “tree” logit models. Transportation Research B, 21(4):251-268, 1987. [Daly, A., et.al., 2004], A. Daly, J. Fox and J.G.Tuinenga, Pivot-Point Procedures in Practical Travel Demand Forecasting, RAND Europe, 2005 [Domenich and McFadden, 1975], T.A. Domenich and D. McFadden, Urban Travel Demand: A behavioral analysis, North-Holland Publishing Company, 1975. [Garling et.al., 1998], T. Garling, T. Laitila, and K. Westin, Theoretical Foundations of Travel Choice Modeling, 1998. [Hensher and Johnson, 1981], D.A. Hensher and L.W. Johnson, Applied discrete choice modelling. Croom Helm, London, 1981
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[Horowitz, et.al., 1986], J.L. Horowitz, F.S. Koppelman, and S.R. Lerman, A self- instructing course in disaggregate mode choice modeling, Technology Sharing Program, USDOT, 1986. [Koppelman, 1975], F.S. Koppelman, Travel Prediction with Models of Individual Choice Behavior, PhD Submittal, Massachusetts Institute, 1975. [Louviere, et.al., 2000], J.J.Louviere, D.A.Hensher, and J.D.Swait, Stated Choice Methods: Analysis and Application, Cambridge, 2000 [Luce and Suppes, 1965], R.D. Luce and P. Suppes, Handbook of Mathematical Psychology, 1965. [Rogers et al., 1970], K.G. Rogers, G.M. Townsend and A.E. Metcalf, Planning for the work. Journey –a generalized explanation of modal choice, Report C67, Reading, 1970. [Wilson, 1967], A.G. Wilson, A Statistical Theory of Spatial Distribution models, Transport Research, Vol. 1, 1967. [Quarmby, 1967], D. Quarmby, Choice of Travel Mode for the Journey to Work: Some Findings, Journal of Transport Economics and Policy, Vol. 1, No. 3, 1967. [Yai, et.al., 1997], T. Yai, S. Iwakura, and S. Morichi, Multinominal probit with structured covariance for route choice behavior, Transportation Research B, 31(3):195-208, 1997.
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C HAMPTON ROADS-RICHMOND- WASHINGTON CORRIDOR STATION VOLUMES
2025 Step1 2025 Step2
Station Volume Station Volume Washington 230,632 Washington 533,415 Alexandria 78,310 Alexandria 206,960 Franconia 44,242 Franconia 128,660 Woodbridge 13,162 Woodbridge 74,591 Quantico 28,500 Quantico 72,516 Fredericksburg 54,897 Fredericksburg 141,582 Ashland 13,950 Ashland 38,943 Richmond, Staples 156,523 Richmond, Staples 388,909 Richmond, Main Street 24,987 Richmond, Main Street 272,124 Williamsburg 75,188 Williamsburg 149,097 Newport News (Airport) 0 Newport News (Airport) 0 Newport News (Downtown) 124,624 Newport News (Downtown) 256,398 Petersburg 72,532 Petersburg 238,104 Suffolk 0 Suffolk 0 Bowers Hill 0 Bowers Hill 188,592 Norfolk 234,160 Norfolk 504,123 Total 1,151,707 Total 3,194,014
2025 Step3 2025 Step4
Station Volume Station Volume Washington 954,105 Washington 1,263,941 Alexandria 371,356 Alexandria 521,172 Franconia 300,429 Franconia 472,338 Woodbridge 157,981 Woodbridge 236,015 Quantico 127,675 Quantico 175,486 Fredericksburg 253,900 Fredericksburg 351,437 Ashland 71,904 Ashland 99,054 Richmond, Staples 703,007 Richmond, Staples 971,242 Richmond, Main Street 580,197 Richmond, Main Street 806,899 Williamsburg 288,560 Williamsburg 405,036 Newport News (Airport) 264,307 Newport News (Airport) 372,972 Newport News (Downtown) 261,835 Newport News (Downtown) 372,294 Petersburg 475,142 Petersburg 678,509 Suffolk 146,060 Suffolk 201,534 Bowers Hill 235,057 Bowers Hill 322,078 Norfolk 1,021,396 Norfolk 1,403,223 Total 6,212,911 Total 8,653,230
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