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Longer-term changes in mode choice decisions in Chennai: a comparison between cross-sectional and dynamic models

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  • Karthik Srinivasan

  • P. Bhargavi

Abstract

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 ρ 2 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. Copyright Springer Science+Business Media, LLC 2007

Suggested Citation

  • Karthik Srinivasan & P. Bhargavi, 2007. "Longer-term changes in mode choice decisions in Chennai: a comparison between cross-sectional and dynamic models," Transportation, Springer, vol. 34(3), pages 355-374, May.
    • Handle: RePEc:kap:transp:v:34:y:2007:i:3:p:355-374
    DOI: 10.1007/s11116-007-9116-x
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    References listed on IDEAS

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    1. repec:cdl:uctcwp:qt1676t0bp is not listed on IDEAS
    2. Karthik K. Srinivasan & Hani S. Mahmassani, 2005. "A Dynamic Kernel Logit Model for the Analysis of Longitudinal Discrete Choice Data: Properties and Computational Assessment," Transportation Science, INFORMS, vol. 39(2), pages 160-181, May.
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    4. Chandra R. Bhat, 2000. "Incorporating Observed and Unobserved Heterogeneity in Urban Work Travel Mode Choice Modeling," Transportation Science, INFORMS, vol. 34(2), pages 228-238, May.
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    Cited by:

    1. Kamruzzaman, Md. & Baker, Douglas & Washington, Simon & Turrell, Gavin, 2013. "Residential dissonance and mode choice," Journal of Transport Geography, Elsevier, vol. 33(C), pages 12-28.
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    3. Rubin, Ori & Mulder, Clara H. & Bertolini, Luca, 2014. "The determinants of mode choice for family visits – evidence from Dutch panel data," Journal of Transport Geography, Elsevier, vol. 38(C), pages 137-147.
    4. Chenfeng Xiong & Xiqun Chen & Xiang He & Wei Guo & Lei Zhang, 2015. "The analysis of dynamic travel mode choice: a heterogeneous hidden Markov approach," Transportation, Springer, vol. 42(6), pages 985-1002, November.
    5. Gadepalli, Ravi & Tiwari, Geetam & Bolia, Nomes, 2020. "Role of user's socio-economic and travel characteristics in mode choice between city bus and informal transit services: Lessons from household surveys in Visakhapatnam, India," Journal of Transport Geography, Elsevier, vol. 88(C).
    6. Donna, Javier D., 2018. "Measuring Long-Run Price Elasticities in Urban Travel Demand," MPRA Paper 90260, University Library of Munich, Germany.
    7. Elisabetta Cherchi & Francesco Manca, 2011. "Accounting for inertia in modal choices: some new evidence using a RP/SP dataset," Transportation, Springer, vol. 38(4), pages 679-695, July.
    8. Javier D. Donna, 2021. "Measuring long‐run gasoline price elasticities in urban travel demand," RAND Journal of Economics, RAND Corporation, vol. 52(4), pages 945-994, December.
    9. Doddamani, Chetan & Manoj, M. & Maurya, Yashasvi, 2021. "Geographical scale of residential relocation and its impacts on vehicle ownership and travel behavior," Journal of Transport Geography, Elsevier, vol. 94(C).
    10. Cinzia Cirillo & Kay Axhausen, 2010. "Dynamic model of activity-type choice and scheduling," Transportation, Springer, vol. 37(1), pages 15-38, January.
    11. Doddamani, Chetan & Manoj, M., 2022. "Residential relocation and changes in household vehicle ownership and travel behavior: Exploring the context of Hubli-Dharwad twin-cities in India from a planning viewpoint," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 134-155.
    12. Kiron Chatterjee, 2011. "Modelling the dynamics of bus use in a changing travel environment using panel data," Transportation, Springer, vol. 38(3), pages 487-509, May.
    13. Kun Gao & Minhua Shao & Kay W. Axhausen & Lijun Sun & Huizhao Tu & Yihong Wang, 2022. "Inertia effects of past behavior in commuting modal shift behavior: interactions, variations and implications for demand estimation," Transportation, Springer, vol. 49(4), pages 1063-1097, August.
    14. Thorhauge, Mikkel & Swait, Joffre & Cherchi, Elisabetta, 2020. "The habit-driven life: Accounting for inertia in departure time choices for commuting trips," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 272-289.
    15. Chenfeng Xiong & Lei Zhang, 2017. "Dynamic travel mode searching and switching analysis considering hidden model preference and behavioral decision processes," Transportation, Springer, vol. 44(3), pages 511-532, May.
    16. Juan de Dios Ortúzar & Elisabetta Cherchi & Luis Ignacio Rizzi, 2014. "Transport research needs," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 29, pages 688-698, Edward Elgar Publishing.

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