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

Incorporating crowding into the San Francisco activity-based travel model



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 


  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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