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Commuter travel cost estimation at different levels of crowding in a suburban rail system: a case study of Mumbai

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  • Prasanta K. Sahu

    (Birla Institute of Technology and Science Pilani)

  • Gajanand Sharma

    (Birla Institute of Technology and Science Pilani)

  • Anirban Guharoy

    (Birla Institute of Technology and Science Pilani)

Abstract

This research values travel attributes such as waiting time, in-vehicle time and crowding levels using behavioural data obtained from Mumbai local train commuters through a stated preference experiment. Actual on-board crowding images are considered to perceive the crowding more realistically by the train users. A multinomial logit modelling technique is used for estimating commuter travel cost (time) at different crowding levels. Results show that there is an increase in perceived in-vehicle travel time with the increase in crowding level. Traveling in a crowded seating condition increases the travel cost by 0.81 min per 1 min travel. A crowded seat leads the user to perceive an 81% increase in in-vehicle travel time, whereas this perception increases by 282% more during travel in super dense crush crowding compared to normal travel conditions. The generalized travel cost increases to a maximum in the super dense crush crowding. An effect analysis was carried out to understand the sensitivity of gender, age, income, and trip length on travel attributes. Female users tend to perceive more decrease in utility due to crowding than male users. A reduction of 60% in seating capacity will lower the perceived travel cost per minute by 43% during peak hours of travel. Essentially, a reduction in seat capacity will increase the standee capacity which in turn offers more comfort to standees while standing in super dense crush load condition. Presented discussions in this research are important to policy-makers and planners in the Mumbai Railway Vikas Corporation to monitor, measure and develop programs for the local train operation service quality. The study findings will be useful for developing a policy framework to deal with issues related to the level of service improvement for the suburban rail system in India and other developing economies.

Suggested Citation

  • Prasanta K. Sahu & Gajanand Sharma & Anirban Guharoy, 2018. "Commuter travel cost estimation at different levels of crowding in a suburban rail system: a case study of Mumbai," Public Transport, Springer, vol. 10(3), pages 379-398, December.
  • Handle: RePEc:spr:pubtra:v:10:y:2018:i:3:d:10.1007_s12469-018-0190-6
    DOI: 10.1007/s12469-018-0190-6
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    1. Hensher, David A., 2010. "Hypothetical bias, choice experiments and willingness to pay," Transportation Research Part B: Methodological, Elsevier, vol. 44(6), pages 735-752, July.
    2. Donghyung Yook & Kevin Heaslip, 2015. "The effect of crowding on public transit user travel behavior in a large-scale public transportation system through modeling daily variations," Transportation Planning and Technology, Taylor & Francis Journals, vol. 38(8), pages 935-953, December.
    3. Brakewood, Candace & Barbeau, Sean & Watkins, Kari, 2014. "An experiment evaluating the impacts of real-time transit information on bus riders in Tampa, Florida," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 409-422.
    4. André Duarte & Camila Garcia & Grigoris Giannarakis & Susana Limão & Amalia Polydoropoulou & Nikolaos Litinas, 2010. "New approaches in transportation planning: happiness and transport economics," Netnomics, Springer, vol. 11(1), pages 5-32, April.
    5. Cox, Tom & Houdmont, Jonathan & Griffiths, Amanda, 2006. "Rail passenger crowding, stress, health and safety in Britain," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(3), pages 244-258, March.
    6. Haywood, Luke & Koning, Martin, 2015. "The distribution of crowding costs in public transport: New evidence from Paris," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 182-201.
    7. Li, Zheng & Hensher, David A., 2011. "Crowding and public transport: A review of willingness to pay evidence and its relevance in project appraisal," Transport Policy, Elsevier, vol. 18(6), pages 880-887, November.
    8. Basu, Debasis & Hunt, John Douglas, 2012. "Valuing of attributes influencing the attractiveness of suburban train service in Mumbai city: A stated preference approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(9), pages 1465-1476.
    9. Mark Wardman & Gerard Whelan, 2011. "Twenty Years of Rail Crowding Valuation Studies: Evidence and Lessons from British Experience," Transport Reviews, Taylor & Francis Journals, vol. 31(3), pages 379-398.
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