, Volume 39, Issue 4, pp 755–771 | Cite as

Incorporating crowding into the San Francisco activity-based travel model

  • Lisa Zorn
  • Elizabeth SallEmail author
  • Daniel Wu


Information produced by travel demand models plays a large role decision making in many metropolitan areas, and San Francisco is no exception. Being a transit first city, one of the most common uses for San Francisco’s travel model SF-CHAMP is to analyze transit demand under various circumstances. SF-CHAMP v 4.1 (Harold) is able to capture the effects of several aspects of transit provision including headways, stop placement, and travel time. However, unlike how auto level of service in a user equilibrium traffic assignment is responsive to roadway capacity, SF-CHAMP Harold is unable to capture any benefit related to capacity expansion, crowding’s effect on travel time nor or any of the real-life true capacity limitations. The failure to represent these elements of transit travel has led to significant discrepancies between model estimates and actual ridership. Additionally it does not allow decision-makers to test the effects of policies or investments that increase the capacity of a given transit service. This paper presents the framework adopted into a more recent version of SF-CHAMP (Fury) to represent transit capacity and crowding within the constraints of our current modeling software.


Transportation planning Transit planning Activity-based travel models Transit crowding 



Zabe Bent, Rachel Hiatt, Jesse Koehler, and Billy Charlton at the San Francisco County Transportation Authority, Viktoriya Wise at the San Francisco Planning Department, and Julie Kirshbaum and Peter Straus at the San Francisco Municipal Transportation Agency.


  1. Davidson, W., Vovsha, P., Garland, R.: Impact of crowding on rail ridership: Sydney metro experience and forecasting approach. Paper presented at the TRB Planning Applications Conference. Reno, NV (2011)Google Scholar
  2. Dueker, K.J., Kimpel, T.J., Strathman, J.G., Callas, S.: Determinants of bus dwell time. J. Public Transp. 7(1), 21–40 (2004)Google Scholar
  3. Erhardt, G.D., Charlton, B., Freedman, J., Castiglione, J., Bradley, M.: Enhancement and application of an activity-based travel model for congestion pricing. Paper presented at TRB Innovations in Travel Modeling. Portland, OR (2008)Google Scholar
  4. Hood, J., Sall, E., Charlton, B.: A GPS-based bicycle route choice model for San Francisco, California. Transp. Lett. Int. J. Transp. Res. 3(1), 63–75 (2011)CrossRefGoogle Scholar
  5. Krantz, J.: New York City Transit. (D. Wu, Interviewer) (2010)Google Scholar
  6. Lam, W.H., Cheung, C.-Y., Lam, C.F.: A study of crowding effects at the Hong Kong light rail transit stations. Transp. Res. 33(5), 401–415 (1999)CrossRefGoogle Scholar
  7. Maier, H.: CAPTRAS and CONGTRAS: alternative ways of modeling crowding in RAILPLAN. Paper presented at UK EMME User’s Conference. London, UK (2011)Google Scholar
  8. Meyer, M.D., Miller, E.J.: Urban Transportation Planning. McGraw-Hill, New York (2000)Google Scholar
  9. Milkovits, M.: Modeling the factors affecting bus stop dwell time: use of automatic passenger counting, automatic fare counting, and automatic vehicle location data. Transp. Res. Rec. 2072, 125–130 (2008)CrossRefGoogle Scholar
  10. MWCOG: Air Quality Conformity Assessment—Maryland Department of Transportation/District of Columbia Department of Public Works Amendments to 1999 Constrained Long Range Plan and FY2000-2006 Transportation Improvement Program. Retrieved from (2010, December)
  11. Outwater, M.L., Charlton, B.: The San Francisco model in practice: validation, testing, and application. Innovations in travel demand modeling: summary of a conference, vol 2, Papers, Number 42 in Transportation Research Board Conference Proceedings, (pp. 24–29) (2008)Google Scholar
  12. Padgette, R.: American Public Transportation Association. (E. Sall, & D. Wu, Interviewers) (2010)Google Scholar
  13. San Francisco Municipal Transportation Agency: Transit Effectiveness Project (TEP)—market analysis. Retrieved from (2010, December)
  14. Transit First Policy: San Francisco California City Charter. 115 Section 8A. (1978)Google Scholar
  15. Transport for London: RailPlan Modeling User Guide. (2006)Google Scholar
  16. Transportation Research Board: Report 100 Transit Capacity and Quality of Service Manual. Transportation Research Board of the National Academies, Washington, DC (2003)Google Scholar
  17. Transportation Research Board: Special Report 288 Metropolitan Travel Forecasting: Current Practice and Future Direction. Transportation Research Board of the National Academies, Washington, DC (2007)Google Scholar
  18. Wardman, M.: The value of travel time: a review of British evidence. J. Transp. Econ. Policy 32(3), 285–316 (1998)Google Scholar
  19. Zorn, L.M., Sall, E., Charlton, B.: Incorporating Discrete Characteristics and Network Relationships of Parking into the SF-CHAMP Travel Model. TRB Innovations in Travel Modeling, Tempe, AZ (2011)Google Scholar

Copyright information

© Springer Science+Business Media, LLC. 2012

Authors and Affiliations

  1. 1.San Francisco County Transportation AuthoritySan FranciscoUSA
  2. 2.Cambridge SystematicsCambridgeUSA

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