IDEAS home Printed from https://ideas.repec.org/a/spr/pubtra/v10y2018i3d10.1007_s12469-018-0190-6.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12469-018-0190-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12469-018-0190-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    7. 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.
    8. 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.
    9. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mohammad Masoud Rahimi & Elham Naghizade & Mark Stevenson & Stephan Winter, 2023. "SentiHawkes: a sentiment-aware Hawkes point process to model service quality of public transport using Twitter data," Public Transport, Springer, vol. 15(2), pages 343-376, June.
    2. Rossetti, Tomás & Daziano, Ricardo A., 2024. "Crowding multipliers on shared transportation in New York City: The effects of COVID-19 and implications for a sustainable future," Transport Policy, Elsevier, vol. 145(C), pages 224-236.
    3. Varghese, Varun & Moniruzzaman, Md. & Chikaraishi, Makoto, 2023. "Environmental sustainability or equity in welfare? Analysing passenger flows of a mass rapid transit system with heterogeneous demand," Research in Transportation Economics, Elsevier, vol. 97(C).
    4. Aghabayk, Kayvan & Esmailpour, Javad & Shiwakoti, Nirajan, 2021. "Effects of COVID-19 on rail passengers’ crowding perceptions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 186-202.
    5. Thomas, Neenu & Jana, Arnab & Bandyopadhyay, Santanu, 2022. "Physical distancing on public transport in Mumbai, India: Policy and planning implications for unlock and post-pandemic period," Transport Policy, Elsevier, vol. 116(C), pages 217-236.
    6. Sanmay Shelat & Oded Cats & Sander van Cranenburgh, 2021. "Traveller behaviour in public transport in the early stages of the COVID-19 pandemic in the Netherlands," Papers 2104.10973, arXiv.org, revised Apr 2022.
    7. Prasanta K. Sahu & Babak Mehran & Surya P. Mahapatra & Satish Sharma, 2021. "Spatial data analysis approach for network-wide consolidation of bus stop locations," Public Transport, Springer, vol. 13(2), pages 375-394, June.
    8. Nasution Abdillah Arif & Erwin Keulana, 2019. "Analysis of Transportation Selection for Travel Work," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 10(2), pages 1-10, November.
    9. Dibya Nandan Mishra & Rajeev Kumar Panda, 2023. "Decoding customer experiences in rail transport service: application of hybrid sentiment analysis," Public Transport, Springer, vol. 15(1), pages 31-60, March.
    10. Hong, Seo-Young & Cho, Shin-Hyung & Park, Ho-Chul, 2024. "Behavioral differences between young adults and elderly travelers concerning the crowding effect on public transit after the COVID-19 pandemic," Research in Transportation Economics, Elsevier, vol. 106(C).
    11. Sharma, Gajanand & Patil, Gopal R., 2021. "Public transit accessibility approach to understand the equity for public healthcare services: A case study of Greater Mumbai," Journal of Transport Geography, Elsevier, vol. 94(C).
    12. Shelat, Sanmay & Cats, Oded & van Cranenburgh, Sander, 2022. "Traveller behaviour in public transport in the early stages of the COVID-19 pandemic in the Netherlands," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 357-371.
    13. Majumdar, Bandhan Bandhu & Jayakumar, Malavika & Sahu, Prasanta K. & Potoglou, Dimitris, 2021. "Identification of key determinants of travel satisfaction for developing policy instrument to improve quality of life: An analysis of commuting in Delhi," Transport Policy, Elsevier, vol. 110(C), pages 281-292.
    14. Suchi Kapoor Malhotra & Howard White & Nina Ashley O. Dela Cruz & Ashrita Saran & John Eyers & Denny John & Ella Beveridge & Nina Blöndal, 2021. "Studies of the effectiveness of transport sector interventions in low‐ and middle‐income countries: An evidence and gap map," Campbell Systematic Reviews, John Wiley & Sons, vol. 17(4), December.
    15. Wang, Qing & Zhao, Wenjing & Ma, Shoufeng & Schonfeld, Paul M. & Zheng, Yue & Xue, Dabin, 2023. "Effects of a price incentive policy on urban rail transit passengers: A case study in Nanjing, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:diw:diwwpp:dp1293 is not listed on IDEAS
    2. 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.
    3. Márquez, Luis & Alfonso A, Julieth V. & Poveda, Juan C., 2019. "In-vehicle crowding: Integrating tangible attributes, attitudes, and perceptions in a choice context between BRT and metro," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 452-465.
    4. Matthieu Lapparent & Martin Koning, 2016. "Analyzing time sensitivity to discomfort in the Paris subway: an interval data model approach," Transportation, Springer, vol. 43(5), pages 913-933, September.
    5. Björklund, Gunilla & Swärdh, Jan-Erik, 2017. "Estimating policy values for in-vehicle comfort and crowding reduction in local public transport☆," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 453-472.
    6. Junya Kumagai & Mihoko Wakamatsu & Shunsuke Managi, 2021. "Do commuters adapt to in-vehicle crowding on trains?," Transportation, Springer, vol. 48(5), pages 2357-2399, October.
    7. Tirachini, Alejandro & Hurtubia, Ricardo & Dekker, Thijs & Daziano, Ricardo A., 2017. "Estimation of crowding discomfort in public transport: Results from Santiago de Chile," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 311-326.
    8. Liudan Jiao & Liyin Shen & Chenyang Shuai & Yongtao Tan & Bei He, 2017. "Measuring Crowdedness between Adjacent Stations in an Urban Metro System: a Chinese Case Study," Sustainability, MDPI, vol. 9(12), pages 1-14, December.
    9. Björklund, Gunilla & Swärdh, Jan-Erik, 2015. "Valuing in-vehicle comfort and crowding reduction in public transport," Working papers in Transport Economics 2015:12, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    10. repec:diw:diwwpp:dp1535 is not listed on IDEAS
    11. Aghabayk, Kayvan & Esmailpour, Javad & Shiwakoti, Nirajan, 2021. "Effects of COVID-19 on rail passengers’ crowding perceptions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 186-202.
    12. Haywood, Luke & Koning, Martin & Monchambert, Guillaume, 2017. "Crowding in public transport: Who cares and why?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 215-227.
    13. Björklund, Gunilla & Swärdh, Jan-Erik, 2016. "Valuing in-vehicle comfort and crowding reduction in public transport," Working papers in Transport Economics 2016:2, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    14. Bouscasse, Hélène & de Lapparent, Matthieu, 2019. "Perceived comfort and values of travel time savings in the Rhône-Alpes Region," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 370-387.
    15. Tirachini, Alejandro & Sun, Lijun & Erath, Alexander & Chakirov, Artem, 2016. "Valuation of sitting and standing in metro trains using revealed preferences," Transport Policy, Elsevier, vol. 47(C), pages 94-104.
    16. Lin, Joanne Yuh-Jye & Jenelius, Erik & Cebecauer, Matej & Rubensson, Isak & Chen, Cynthia, 2023. "The equity of public transport crowding exposure," Journal of Transport Geography, Elsevier, vol. 110(C).
    17. Batarce, Marco & Muñoz, Juan Carlos & Ortúzar, Juan de Dios, 2016. "Valuing crowding in public transport: Implications for cost-benefit analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 91(C), pages 358-378.
    18. Li, Hao & Gao, Kun & Tu, Huizhao, 2017. "Variations in mode-specific valuations of travel time reliability and in-vehicle crowding: Implications for demand estimation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 250-263.
    19. Guo, Ren-Yong & Szeto, W.Y. & Long, Jiancheng, 2020. "Trial-and-error operation schemes for bimodal transport systems," Transportation Research Part B: Methodological, Elsevier, vol. 131(C), pages 106-123.
    20. Paudel, Jayash, 2021. "Bus ridership and service reliability: The case of public transportation in Western Massachusetts," Transport Policy, Elsevier, vol. 100(C), pages 98-107.
    21. Haywood, Luke & Koning, Martin & Prud'homme, Remy, 2018. "The economic cost of subway congestion: Estimates from Paris," Economics of Transportation, Elsevier, vol. 14(C), pages 1-8.
    22. Hörcher, Daniel & Tirachini, Alejandro, 2021. "A review of public transport economics," Economics of Transportation, Elsevier, vol. 25(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:pubtra:v:10:y:2018:i:3:d:10.1007_s12469-018-0190-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.