Public Transport

, Volume 10, Issue 3, pp 379–398 | Cite as

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

  • Prasanta K. Sahu
  • Gajanand Sharma
  • Anirban Guharoy
Case Study and Application


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.


Mumbai Crowding effect Public transport Choice modelling Stated preference In-vehicle time Waiting time 


  1. Aklekar R (2011) Railway think tank rules out metro-like seats in locals. Daily news and analysis. 7 Dec 2011. Accessed 5 Nov 2017
  2. Aklekar R (2015) MRVC suggests different fares for slow, fast locals. Mumbai Mirror. 13 Aug 2015. Accessed 12 May 2018
  3. Basu D, Hunt JD (2012) Valuing of attributes influencing the attractiveness of suburban train service in mumbai city: a stated preference approach. Transp Res Part A Policy Pract 46(9):1465–1476. CrossRefGoogle Scholar
  4. Brakewood C, Barbeau S, Watkins K (2014) An experiment evaluating the impacts of real-time transit information on bus riders in Tampa, Florida. Transp Res Part A Policy Pract 69:409–422. CrossRefGoogle Scholar
  5. Chang JS, Jung D (2017) Valuations on quality of service for intercity travels using high-speed rail. Transp Lett 9(4):228–242. CrossRefGoogle Scholar
  6. Cox T, Houdmont J, Griffiths A (2006) Rail passenger crowding, stress, health and safety in Britain. Transp Res Part A Policy Pract 40(3):244–258. CrossRefGoogle Scholar
  7. Douglas N, Karpouzis G (2006) Estimating the passenger cost of train overcrowding. In: Proceedings of 29th Australasian Transport Research Forum, Gold Coast.
  8. Duarte A, Garcia C, Giannarakis G, Limao S, Polydoropoulou A, Litinas N (2010) New approaches in transportation planning: happiness and transport economics. Netnomics 11(1):5–32CrossRefGoogle Scholar
  9. Haywood L, Koning M (2015) The distribution of crowding costs in public transport: new evidence from Paris. Transp Res Part A Policy Pract 77:182–201. CrossRefGoogle Scholar
  10. Hensher DA (2010) Hypothetical bias, choice experiments and willingness to pay. Transp Res Part B Methodol 44(6):735–752. CrossRefGoogle Scholar
  11. Hensher DA, Rose JM, Greene WH (2005) Applied choice analysis: a primer. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  12. Hensher DA, Rose JM, Collins AT (2011) Identifying commuter preferences for existing modes and a proposed metro in Sydney, Australia with special reference to crowding. Public Transp 3(2):109–147. CrossRefGoogle Scholar
  13. Koppelman FS, Bhat CR (2006) A self-instructing course in mode choice modelling: multinomial and nested logit models. Prepared for U.S. Department of Transportation Federal Transit Administration. Accessed 12 May 2018
  14. Li Z, Hensher DA (2011) Crowding and public transport: a review of willingness to pay evidence and its relevance in project appraisal. Transp Policy 18(6):880–887. CrossRefGoogle Scholar
  15. Li Z, Hensher DA (2013) Crowding in public transport: a review of objective and subjective measures. J Public Transp 16(2):107–134. CrossRefGoogle Scholar
  16. Limtanakool N, Dijst M, Schwanen T (2006) The influence of socioeconomic characteristics, land use and travel time considerations on mode choice for medium-and longer-distance trips. J Transp Geogr 14(5):327–341. CrossRefGoogle Scholar
  17. Lu H, Fowkes T, Wardman M (2006) The influence of SP design on the incentive to bias in responses. European transport conference, Strasbourg. Accessed 12 May 2018
  18. McFadden D (1974) Conditional logit analysis of qualitative choice behavior. In: Zarembka P (ed) Frontiers in econometrics. Academic Press, New York, pp 105–142Google Scholar
  19. M-Indicator (2017) Mumbai suburban rail system timetable. Accessed 20 June 2017
  20. Ojha S (2016) Able and travelling in handicapped coach of Mumbai local? Stand on crutches. Mid-Day. Accessed 12 May 2018
  21. Railway Gazette (2010) Loan to relieve world’s most overcrowded trains. 6 July 2010. Accessed 12 May 2018
  22. Rao S (2015) Senior citizens to have exclusive seats in local trains. Mid-Day. Accessed 5 Oct 2017
  23. The Hindu (2016) 3,304 deaths on Mumbai locals in 2015. 27 Jan 2016. Accessed 30 Jan 2017
  24. The Indian Express (2015) Deaths due to fall from overcrowded Mumbai local trains go up, reveals RTI. 13 Sept 2015. Accessed 30 Jan 2017
  25. The Times of India (2018) Mumbai local train. Accessed 27 April 2018
  26. Tirachini A, Hensher DA, Rose JM (2013) Crowding in public systems: effects on users, operation and implications for the estimation of demand. Transp Res Part A Policy Pract 53:36–52. CrossRefGoogle Scholar
  27. Train K (2002) Discrete choice methods with simulation. Cambridge University Press, CambridgeGoogle Scholar
  28. Vuchic VR (2005) Urban transit system and technology. Wiley, New YorkGoogle Scholar
  29. Wardman M, Whelan G (2011) Twenty years of rail crowding valuation studies: evidence and lessons from British experience. Transp Rev 31(3):379–398. CrossRefGoogle Scholar
  30. Whelan GA, Crockett J (2009) An investigation of the willingness to pay to reduce rail overcrowding. In: International conference on choice modelling, HarrogateGoogle Scholar
  31. Wilbur Smith Associates (2013) Executive Summary Report—Mumbai sub-urban rail surveys and analysis. Mumbai Rail Vikas Corporation Limited, MumbaiGoogle Scholar
  32. Yook D, Heaslip K (2015) The effect of crowding on public transit user travel behavior in a large-scale public transportation system through modeling daily variations. Transp Plan Technol 38(8):935–953. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Civil EngineeringBirla Institute of Technology and Science PilaniHyderabadIndia
  2. 2.Department of Civil EngineeringBirla Institute of Technology and Science PilaniPilaniIndia

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