Longer-term changes in mode choice decisions in Chennai: a comparison between cross-sectional and dynamic models
- 264 Downloads
The rapid and continuing changes in travel and mobility needs in India over the last decade necessitates the development and use of dynamic models for travel demand forecasting rather than cross-sectional models. In this context, this paper investigates mode choice dynamics among workers in Chennai city, India over a period of five years (1999–2004). Dynamics in mode choice is captured at four levels: exogenous variable change, state-dependence, changes in users’ sensitivity to attributes, and unobserved error terms. The results show that the dynamic models provide a substantial improvement (of over 500 log-likelihood points and ρ2 increases from 44% to 68%) over the cross-sectional model. The performance was compared using two illustrative policy scenarios with important methodological and practical implications. The results indicate that cross-sectional models tend to provide inflated estimates of potential improvement measures. Improving the Level of Service (LOS) alone will not produce the anticipated benefits to transit agencies, as it fails to overcome the persistent inertia captured in the state-dependence factors. The results and models have important applications in the context of growing motorization and congestion management in developing countries.
KeywordsDynamics Mode choice Mixed logit Retrospective study Reverse State dependence
This paper is based on research sponsored by the Interdisciplinary Infrastructure Research Group at the Indian Institute of Technology, Madras. This support is gratefully acknowledged. The authors would like to thank Mr. Gitakrishnan Ramadurai, project associate, and many enumerators for their tireless efforts and assistance in the data collection and compilation stage of this study.
- Ben-Akiva, M., Bolduc, D.: Multinomial Probit with Logit Kernel and a General Parametric Specification of the Covariance Structure. Working paper, Department of Economics, Massachusetts Institute of Technology, Cambridge (1996)Google Scholar
- Ben-Akiva, M., Lerman, S.R.: Discrete Choice Analysis: Theory and Application to Travel Demand. Cambridge, MA: MIT Press (1985)Google Scholar
- Census 2001: Directorate of Census operation Tamil Nadu – Primary census abstract – census 2001. http://www.census.tn.nic.in/pca2001.aspx Date Accessed: March, 2005Google Scholar
- Dissanayake, D., Morikawa, T.: Investigation of Household Travel Behaviour in Developing Countries using a Nested Logit Model of Vehicle-Ownership, Mode Choice and Trip Chaining. Transportation Research Board, Washington, DC (2002)Google Scholar
- Goulias, K.G., Kilgren, N., Kim, T.: 10th International conference on travel behaviour research, moving through nets: the physical and social dimensions of travel (2003)Google Scholar
- Kitamura, R.: Panel analysis in transport planning: an overview. Transport. Res. 24A(6), 401–415 (1990)Google Scholar
- National Council of Applied Economic Research Report. http://www.ncaer.org/Lbrry.asp Date Accessed: April, 2006Google Scholar
- Ortuzar, J.D., Willumsen, L.G.: Modeling Transport. John Wiley & Sons Ltd (2001)Google Scholar
- Pallavan Transport Consultancy Services (PTCS). Updating the CTTS Carried out in 1992–95. Publication draft final report. Tamil Nadu Urban Infrastructure Financial Services Limited, July 2004Google Scholar
- Ramadurai , G., Srinivasan, K.K.: Dynamics and Variability in Within-Day Mode Choice Decisions: Role of State Dependence, Habit Persistence, and Unobserved Heterogeneity. Transportation Research Board 85th Annual Meeting, Washington, DC (2006)Google Scholar
- Srinivasan, K.K.: Dynamic decision and adjustment processes in commuter behavior under real-time traffic information. Dissertation, University of Texas at Austin, Texas, USA (2000)Google Scholar