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

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The aggregate rail ridership forecasting model overview l.jpg

The Aggregate Rail Ridership Forecasting Model: Overview

Dave Schmitt, AICP

Southeast Florida Users Group

November 14th 2008


What is it l.jpg
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

  • Based on 20 recently-built light and commuter rail projects

  • Two spreadsheets: light rail and commuter rail; all other materials are identical

  • Sponsored by FTA; developed by AECOM


Lrt model equation l.jpg
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)


Cr model equation l.jpg
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


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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



Applications city a l.jpg
Applications – City A

  • New rail line between CBD and suburban activity centers; strong corridor bus ridership & service

  • Compared ARRF LRT model with travel demand model results

  • Results

    • 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


Applications city a cont d l.jpg
Applications – City A (cont’d)

  • Conclusions

    • ARRF model may partially explain attractiveness of rail over existing bus service

    • TDM path-builder probably better at evaluating bus/rail competition:

      • Equal service levels for bus & rail

      • Buses are just as close or closer to corridor activity centers


Applications city b l.jpg
Applications – City B

  • New rail line between CBD and suburban residential areas

  • Used ARRF to develop rationale for alternative-specific constant

  • Results on next slide…



Applications city c l.jpg
Applications – City C

  • 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)


Applications city c cont d l.jpg
Applications – City C (cont’d)

  • Result

    • Aggregate model forecast 120% higher than travel demand model

  • Conclusion

    • 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

      • Lack of choice market not consistent with LRT sample cities


Applications city d l.jpg
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 ARRF model compared with travel demand model (2000 trip tables, 2030 networks) applied to each CBD


Applications city d cont d l.jpg
Applications – City D (cont’d)

  • Result

    • Aggregate model forecast 130% higher than travel demand model

  • 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


Applications city e l.jpg
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

  • Commuter rail ARRF model compared with travel demand model (2000 trip tables, 2030 networks) applied


Applications city e cont d l.jpg
Applications – City E (cont’d)

  • Result

    • Aggregate model forecast 30% lower than travel demand model

  • Conclusion

    • Existing commuter (“choice”) market in corridor stronger than CR sample cities



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General Procedure

  • Obtain basic input files

  • Determine the socio-economic characteristics of the geography

  • Prepare the CTPP Part 3 flow data

  • Determine the relationships between rail stations & geography

  • Run RailMarket program to determine the number of work for both live & work nearby rail stations

  • Enter the information from RailMarket into the model spreadsheet




Materials available from fta l.jpg
Materials Available from FTA

  • Detailed documentation

    • Part-1: Model Application Guide

    • Part-2: Input Data Development Guide

    • Part-3: Model Calibration Report

  • CTPP and RailMarket programs

  • Spreadsheets: LRT and CR

  • Contact: [email protected]



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