1. 1 The Aggregate Rail Ridership Forecasting Model: Overview Dave Schmitt, AICP Southeast Florida Users Group November 14 th 2008

2. 2 What is It? A sketch-planning tool consisting of CTPP 2000 data, GIS info, programs, control files, and a spreadsheet collectively used to develop an estimate of the ridership potential for a new rail system A sketch-planning tool consisting of CTPP 2000 data, GIS info, programs, control files, and a spreadsheet collectively used to develop an estimate of the ridership potential for a new rail system Based on 20 recently-built light and commuter rail projects Based on 20 recently-built light and commuter rail projects Two spreadsheets: light rail and commuter rail; all other materials are identical Two spreadsheets: light rail and commuter rail; all other materials are identical Sponsored by FTA; developed by AECOM Sponsored by FTA; developed by AECOM

3. 3 CTPP 2000 Data Part 1 – Workers at home-end Part 2 – Workers at work-end Part 3 – Flows CTPP1INC_TZ.exe, CTPP1INC_BG.exe, and CTPP1INC_TR.exe programs Calculates proportion of households in low, medium and high income categories by geographic unit CTPP2EMP_TZ.exe, CTPP2EMP_BG.exe, and CTPP2EMP_TR.exe programs Calculates workers in each geographic unit and estimates employment density CTPP3.exe program Helps to extract tract-level data from region- or state- wide files GIS info Rail station points; Proportion of tracts/zones within range of stations RailMarket.exe program Calculates the number of workers who both live and work within particular distances of a rail station by income group and employment density category Spreadsheet Records service variables and RailMarket results; produces ridership potential estimate

4. 4 LRT Model Equation Total Weekday Unlinked Rail Trips = Weekday Unlinked Drive Access to Work Rail Trips + Weekday Unlinked Other Rail Trips Weekday Unlinked Drive Access to Work Rail Trips = 0.030 * CTPP PNR 6 -to-1 Mile JTW Flows (<50K Den) + 0.202 * CTPP PNR 6 -to-1 Mile JTW Flows (>50K Den) Weekday Unlinked Other (Non-Drive Access to Work) Rail Trips = 0.395 * CTPP 2 -to-1 Mile JTW Flows (<50K Den) + 0.445 * CTPP 2 -to-1 Mile JTW Flows (>50K Den)

5. 5 CR Model Equation Commuter Rail Weekday Unlinked Trips = Nominal Ridership x Demand Adjustment Factor Nominal Ridership = 0.069*High Income CTPP PNR 6-to-1 JTW flows + 0.041*Medium Income CTPP PNR 6-to-1 JTW flows + 0.151*Low Income CTPP 2-to-1 JTW flows Demand Adjustment Factor=(1+0.3*Percent Deviation in Average System Speed) x (1+0.3*Percent Deviation in Train Miles per Mile) x Rail Connection Index

6. 6 CR Model Equation (2) Percent Deviation in Average System Speed= System Average Speed-35.7 mph / [ System Average Speed+35.7)/2] System Average Speed= Annual Revenue Vehicle Miles/Annual Revenue Vehicle Hours Percent Deviation in Train Miles per Mile= Weekday Train Miles per Directional Route Mile-10.3 / [(Weekday Train Miles per Directional Route Mile+10.3)/2] Weekday Train Miles per Directional Route Mile= Annual Revenue Vehicle Miles/250/Average Train Length

7. 7 Applications Applications

8. 8 Applications – City A New rail line between CBD and suburban activity centers; strong corridor bus ridership & service New rail line between CBD and suburban activity centers; strong corridor bus ridership & service Compared ARRF LRT model with travel demand model results Compared ARRF LRT model with travel demand model results Results Results ARRF LRT model results were 100% higher than travel demand model estimates ARRF LRT model results were 100% higher than travel demand model estimates Stronger motivation to investigate transit model parameters; subsequently identified issues with walk- and auto-access connector methodology Stronger motivation to investigate transit model parameters; subsequently identified issues with walk- and auto-access connector methodology

9. 9 Applications – City A (cont’d) Conclusions Conclusions ARRF model may partially explain attractiveness of rail over existing bus service ARRF model may partially explain attractiveness of rail over existing bus service TDM path-builder probably better at evaluating bus/rail competition: TDM path-builder probably better at evaluating bus/rail competition: Equal service levels for bus & rail Equal service levels for bus & rail Buses are just as close or closer to corridor activity centers Buses are just as close or closer to corridor activity centers

10. 10 Applications – City B New rail line between CBD and suburban residential areas New rail line between CBD and suburban residential areas Used ARRF to develop rationale for alternative-specific constant Used ARRF to develop rationale for alternative-specific constant Results on next slide… Results on next slide…

11. 11 Ridership Forecasts – City B Walk Drive/ Drop-Off Total ARRF14,7946,54821,342 TDM Model (no bias) 11,5204,55616,076 TDM Model (7.5 minute walk, 15 minute drive) 13,1456,34119,487 TDM Model (10 minute walk, 15 minute drive) 14,7706,27721,047

12. 12 Applications – City C Streetcar in low density urban activity center; existing service is local & primarily captive market Streetcar in low density urban activity center; existing service is local & primarily captive market ARRF LRT model compared with travel demand model (2000 trip tables, 2030 networks) ARRF LRT model compared with travel demand model (2000 trip tables, 2030 networks)

13. 13 Applications – City C (cont’d) Result Result Aggregate model forecast 120% higher than travel demand model Aggregate model forecast 120% higher than travel demand model Conclusion Conclusion ARRF model may partially explain attractiveness of rail over existing service, but does not well-represent benefits of project since: ARRF model may partially explain attractiveness of rail over existing service, but does not well-represent benefits of project since: The project mode is different than calibrated mode The project mode is different than calibrated mode Lack of choice market not consistent with LRT sample cities Lack of choice market not consistent with LRT sample cities

14. 14 Applications – City D Commuter rail between two adjacent metropolitan areas; some express bus service to each CBD, but no service between CBD’s Commuter rail between two adjacent metropolitan areas; some express bus service to each CBD, but no service between CBD’s Commuter rail ARRF model compared with travel demand model (2000 trip tables, 2030 networks) applied to each CBD Commuter rail ARRF model compared with travel demand model (2000 trip tables, 2030 networks) applied to each CBD

15. 15 Applications – City D (cont’d) Result Result Aggregate model forecast 130% higher than travel demand model Aggregate model forecast 130% higher than travel demand model Conclusion Conclusion ARRF model may partially explain attractiveness of rail over existing commuter bus service, but does not well-represent benefits of project since lack of service between CBDs unlike CR sample cities ARRF model may partially explain attractiveness of rail over existing commuter bus service, but does not well-represent benefits of project since lack of service between CBDs unlike CR sample cities

16. 16 Applications – City E New commuter rail line to high mode share CBD with established “choice market” commuter bus service from large park and ride facilities New commuter rail line to high mode share CBD with established “choice market” commuter bus service from large park and ride facilities Commuter rail ARRF model compared with travel demand model (2000 trip tables, 2030 networks) applied Commuter rail ARRF model compared with travel demand model (2000 trip tables, 2030 networks) applied

17. 17 Applications – City E (cont’d) Result Result Aggregate model forecast 30% lower than travel demand model Aggregate model forecast 30% lower than travel demand model Conclusion Conclusion Existing commuter (“choice”) market in corridor stronger than CR sample cities Existing commuter (“choice”) market in corridor stronger than CR sample cities

18. 18 Process Process

19. 19 General Procedure 1. Obtain basic input files 2. Determine the socio-economic characteristics of the geography 3. Prepare the CTPP Part 3 flow data 4. Determine the relationships between rail stations & geography 5. Run RailMarket program to determine the number of work for both live & work nearby rail stations 6. Enter the information from RailMarket into the model spreadsheet

20. 20 Station Buffers

21. 21 Spreadsheets

22. 22 Materials Available from FTA Detailed documentation Detailed documentation Part-1: Model Application Guide Part-1: Model Application Guide Part-2: Input Data Development Guide Part-2: Input Data Development Guide Part-3: Model Calibration Report Part-3: Model Calibration Report CTPP and RailMarket programs CTPP and RailMarket programs Spreadsheets: LRT and CR Spreadsheets: LRT and CR Contact: Nazrul.Islam@dot.gov Contact: Nazrul.Islam@dot.govNazrul.Islam@dot.gov

23. 23 Thank you! Thank you!