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Transportation

, Volume 36, Issue 1, pp 1–25 | Cite as

Time taken for residents to adopt a new public transport service: examining heterogeneity through duration modelling

  • Kiron ChatterjeeEmail author
  • Kang-Rae Ma
Article

Abstract

When a new public transport service is introduced it would be valuable for public authorities, financing organisations and transport operators to know how long it will take for people to start to use the service and what factors influence this. This paper presents results from research analysing the time taken for residents living close to a new guided bus service to start to use (or adopt) the service. Data was obtained from a sample of residents on whether they used the new service and the number of weeks after the service was introduced before they first used it. Duration modelling has been used to analyse how the likelihood of starting to use the new service changes over time (after the introduction of the service) and to examine what factors influence this. It is found that residents who have not used the new service are increasingly unlikely to use it as time passes. Those residents gaining greater accessibility benefits from the new service are found to be quicker to use the service, although the size of this effect is modest compared to that of other between-resident differences. Allowance for the possibility that there existed a proportion of the sample that would never use the new service was tested using a split population model (SPD) model. The SPD model indicates that 36% of residents will never use the new service and is informative in differentiating factors that influence whether Route 20 is used and when it is used.

Keywords

Panel data Travel behaviour Dynamics Response lag Duration modelling 

Notes

Acknowledgements

The research reported in this paper has been funded by a grant from the UK Engineering and Physical Sciences Research Council (EPSRC).

References

  1. Bhat, C.R.: A hazard-based duration model of shopping activity with nonparametric baseline specification and nonparametric control for unobserved heterogeneity. Transp. Res. Part B 30, 189–207 (1996)CrossRefGoogle Scholar
  2. Bhat, C.R.: Duration models. In: Hensher, D.A., Button, K.J. (eds.) Handbook of Transport Modelling, pp. 91–111. Elsevier, Oxford (2000)Google Scholar
  3. Box-Steffensmeier, J.M., Jones, B.S.: Time is of the essence: event history models in political science. Am J Political Sci 41, 1414–1461 (1997)CrossRefGoogle Scholar
  4. Chang, H.-L., Yeh, T.-H.: Exploratory analysis of motorcycle holding time heterogeneity using a split-population duration model. Transp. Res. Part A 41, 587–596 (2007)Google Scholar
  5. Chatterjee, K.: Towards the dynamic modelling of travel demand. Unpublished Paper presented at the 40th Annual Conference of the Universities’ Transport Study Group, Portsmouth, 2008Google Scholar
  6. Chatterjee, K., Ma, K.: Modelling the timing of user responses to a new urban public transport service: application of duration modelling. Transp Res Rec 2010, 62–72 (2007)CrossRefGoogle Scholar
  7. Cushing-Daniels, B.: Even the errors discriminate: how the split-population model of criminal recidivism makes justice even less colorblind. Rev Black Political Econ 33, 25–40 (2005)CrossRefGoogle Scholar
  8. DfT: Major Scheme Appraisal in Local Transport Plans Part 1: Detailed Guidance on Public Transport and Highways Schemes. Department for Transport, London (2003)Google Scholar
  9. Douglas, N.: Patronage ramp-up factors for new rail services. Unpublished Paper. Available via Chartered Institute in Logistics and Transport in New Zealand. http://www.cilt.co.nz. Accessed 30 May 2006 (2003)
  10. Fastway: Fastway homepage. Available via West Sussex County Council. http://www.westsussex.gov.uk/ccm/navigation/roads-and-transport/public-transport/fastway/. Accessed 9 May 2008
  11. Greene, W.H.: LIMDEP Version 8.0: Econometric Modelling Guide volume 2. Econometric Software, Plainview, NY (2002)Google Scholar
  12. GSR: The Magenta Book: Guidance Notes for Policy Evaluation and Analysis. Background paper 5: what is sampling? Government Social Research Unit, London. Available via http://www.gsr.gov.uk/professional_guidance/magenta_book/index.asp. Accessed 9 May 2008 (2004)
  13. Hensher, D.A.: The timing of change: discrete and continuous time panels in transportation. In: Golob, T.F., Kitamura, R., Long, L. (eds.) Panels for Transportation Planning, pp. 305–319. Kluwer, Boston (1997)Google Scholar
  14. Hensher, D.A.: The timing of change for automobile transactions: a competing risk multispell specification. In: Ortuzar, J.D., Hensher, D.A., Jara-Diaz, S. (eds.) Travel Behaviour Research: Updating the State of Play, pp. 487–506. Pergamon, Oxford (1998)CrossRefGoogle Scholar
  15. Hensher, D.A., Mannering, F.L.: Hazard-based duration models and their application to transport analysis. Transp Rev 14, 63–82 (1994)CrossRefGoogle Scholar
  16. Jenkins, S.P.: Discrete time proportional hazards regression. Stata Tech Bull 39, 22–32 (1997)Google Scholar
  17. Jenkins, S.P.: Survival Analysis. Unpublished manuscript, Institute for Social and Economic Research, University of Essex, UK. Available via Institute for Social and Economic Research. http://www.iser.essex.ac.uk/teaching/degree/stephenj/ec968/pdfs/ec968lnotesv6.pdf. Accessed 9 May 2008 (2004)
  18. Lambert, P.C.: Modeling of the cure fraction in survival studies. The Stata Journal 7, 1–25 (2007)Google Scholar
  19. Le, C.T.: Applied Survival Analysis. Wiley, New York (1997)Google Scholar
  20. Petersen, T.: The statistical analysis of event histories. Soc Methods Res 19, 270–323 (1991)CrossRefGoogle Scholar
  21. Prentice, R.L., Gloeckler, L.: Regression analysis of grouped survival data with application to breast cancer data. Biometrics 34, 57–67 (1978)CrossRefGoogle Scholar
  22. Schmidt, P., Witte, A.D.: Predicting criminal recidivism using ‘split population’ survival time models. J Econ 40, 141–159 (1989)Google Scholar
  23. StataCorp: Stata Statistical Software: Release 9. StataCorp LP, College Station, TX (2005)Google Scholar
  24. Transport Direct: Transport Direct homepage. Available via Transport Direct. http://www.transportdirect.info. Accessed 9 May 2008
  25. Washington, S.P., Karlaftis, M.G., Mannering, F.L.: Statistical and Econometric Methods for Transportation Data Analysis, pp. 217–237. Chapman and Hall/CRC, Boca Raton (2003)Google Scholar
  26. Yamaguchi, K.: Event History Analysis. Sage, Newbury Park (1991)Google Scholar

Copyright information

© Springer Science+Business Media, LLC. 2008

Authors and Affiliations

  1. 1.Centre for Transport & Society, School of the Built and Natural EnvironmentUniversity of the West of EnglandBristolUK
  2. 2.Department of Urban and Regional PlanningChung-Ang UniversityAnseong-siKorea

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