IDEAS home Printed from https://ideas.repec.org/a/eee/retrec/v69y2018icp615-620.html
   My bibliography  Save this article

Workshop 8 report: Big data in the digital age and how it can benefit public transport users

Author

Listed:
  • Yap, Menno
  • Munizaga, Marcela

Abstract

This paper synthesizes evidence from Workshop 8 ‘Big data in the digital age and how it can benefit public transport users’ of the 15th International Conference on Competition and Ownership in Land Passenger Transport. Big data in public transportation has increasingly attracted the attention from both scientists and practitioners, resulting in an increasing number of scientific studies and practical applications in this field. However, compared to the scientific developments, we see that practical big data applications are relatively limited, and that these are applied with a relatively low pace. This indicates that big data has not been used to its full potential in practice yet, meaning that public transport passengers currently do not fully benefit from the opportunities big data offers in terms of public transport quality and attractiveness. Based on literature study and input gained from a qualitative expert session with scientists, public transport authorities, public transport operators and transport consultants together during the conference workshop, we come to the conclusion that the challenges to stimulate further and faster use of big data in practice are institutional rather than technical. This complexity results from required coordination and cooperation among public and private entities that are not always aligned. A framework has been proposed with four components to stimulate a further and faster adoption of big data in practice, directing to different stakeholders or relations between stakeholders: align technical ambitions of big data applications with their institutional environment; enable/ease the use of big data by PT authorities by developing common definitions, data standards and consolidation; incorporate the use of big data by PT operators in the contract between authority and operator; quantify and visualize the business value of big data for PT operators. We illustrate our framework by successful case studies in Chile, the Netherlands and Sweden.

Suggested Citation

  • Yap, Menno & Munizaga, Marcela, 2018. "Workshop 8 report: Big data in the digital age and how it can benefit public transport users," Research in Transportation Economics, Elsevier, vol. 69(C), pages 615-620.
    • Handle: RePEc:eee:retrec:v:69:y:2018:i:c:p:615-620
    DOI: 10.1016/j.retrec.2018.08.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0739885918302087
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.retrec.2018.08.008?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. Sánchez-Martínez, Gabriel E. & Munizaga, Marcela, 2016. "Workshop 5 report: Harnessing big data," Research in Transportation Economics, Elsevier, vol. 59(C), pages 236-241.
    2. Idris, Ahmed Osman & Nurul Habib, Khandker M. & Shalaby, Amer, 2015. "An investigation on the performances of mode shift models in transit ridership forecasting," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 551-565.
    3. Cats, Oded & Wang, Qian & Zhao, Yu, 2015. "Identification and classification of public transport activity centres in Stockholm using passenger flows data," Journal of Transport Geography, Elsevier, vol. 48(C), pages 10-22.
    4. Yap, M.D. & Nijënstein, S. & van Oort, N., 2018. "Improving predictions of public transport usage during disturbances based on smart card data," Transport Policy, Elsevier, vol. 61(C), pages 84-95.
    5. Chuan Ding & Donggen Wang & Xiaolei Ma & Haiying Li, 2016. "Predicting Short-Term Subway Ridership and Prioritizing Its Influential Factors Using Gradient Boosting Decision Trees," Sustainability, MDPI, vol. 8(11), pages 1-16, October.
    6. Gschwender, Antonio & Munizaga, Marcela & Simonetti, Carolina, 2016. "Using smart card and GPS data for policy and planning: The case of Transantiago," Research in Transportation Economics, Elsevier, vol. 59(C), pages 242-249.
    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. Yona, Moran & Birfir, Genadi & Kaplan, Sigal, 2021. "Data science and GIS-based system analysis of transit passenger complaints to improve operations and planning," Transport Policy, Elsevier, vol. 101(C), pages 133-144.
    2. Popkova, Elena G. & Sergi, Bruno S., 2020. "A Digital Economy to Develop Policy Related to Transport and Logistics. Predictive Lessons from Russia," Land Use Policy, Elsevier, vol. 99(C).
    3. Merkert, Rico & Wong, Yale Z., 2020. "Emerging business models and implications for the transport ecosystem," Research in Transportation Economics, Elsevier, vol. 83(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. Yap, M.D. & Nijënstein, S. & van Oort, N., 2018. "Improving predictions of public transport usage during disturbances based on smart card data," Transport Policy, Elsevier, vol. 61(C), pages 84-95.
    2. Pengfei Lin & Jiancheng Weng & Dimitrios Alivanistos & Siyong Ma & Baocai Yin, 2020. "Identifying and Segmenting Commuting Behavior Patterns Based on Smart Card Data and Travel Survey Data," Sustainability, MDPI, vol. 12(12), pages 1-18, June.
    3. Toşa, Cristian & Sato, Hitomi & Morikawa, Takayuki & Miwa, Tomio, 2018. "Commuting behavior in emerging urban areas: Findings of a revealed-preferences and stated-intentions survey in Cluj-Napoca, Romania," Journal of Transport Geography, Elsevier, vol. 68(C), pages 78-93.
    4. Zhang, Jie & Wang, David Z.W. & Meng, Meng, 2018. "Which service is better on a linear travel corridor: Park & ride or on-demand public bus?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 803-818.
    5. Egu, Oscar & Bonnel, Patrick, 2021. "Medium-term public transit route ridership forecasting: What, how and why? A case study in Lyon," Transport Policy, Elsevier, vol. 105(C), pages 124-133.
    6. Cats, O., 2016. "The robustness value of public transport development plans," Journal of Transport Geography, Elsevier, vol. 51(C), pages 236-246.
    7. Rubensson, Isak & Susilo, Yusak & Cats, Oded, 2020. "Is flat fare fair? Equity impact of fare scheme change," Transport Policy, Elsevier, vol. 91(C), pages 48-58.
    8. Tu, Wei & Cao, Rui & Yue, Yang & Zhou, Baoding & Li, Qiuping & Li, Qingquan, 2018. "Spatial variations in urban public ridership derived from GPS trajectories and smart card data," Journal of Transport Geography, Elsevier, vol. 69(C), pages 45-57.
    9. Jeongwoo Lee & Marlon Boarnet & Douglas Houston & Hilary Nixon & Steven Spears, 2017. "Changes in Service and Associated Ridership Impacts near a New Light Rail Transit Line," Sustainability, MDPI, vol. 9(10), pages 1-27, October.
    10. Krembsler, Jonas & Spiegelberg, Sandra & Hasenfelder, Richard & Kämpf, Nicki Lena & Winter, Thomas & Winter, Nicola & Knappe, Robert, 2024. "Fare revenue forecast in public transport: A comparative case study," Research in Transportation Economics, Elsevier, vol. 105(C).
    11. Xuesong Feng & Zhibin Tao & Xuejun Niu & Zejing Ruan, 2021. "Multi-Objective Land Use Allocation Optimization in View of Overlapped Influences of Rail Transit Stations," Sustainability, MDPI, vol. 13(23), pages 1-14, November.
    12. Rubensson, Isak & Susilo, Yusak & Cats, Oded, 2020. "Fair accessibility – Operationalizing the distributional effects of policy interventions," Journal of Transport Geography, Elsevier, vol. 89(C).
    13. Joshua Auld & Hubert Ley & Omer Verbas & Nima Golshani & Josiane Bechara & Angela Fontes, 2020. "A stated-preference intercept survey of transit-rider response to service disruptions," Public Transport, Springer, vol. 12(3), pages 557-585, October.
    14. Cortés, Cristián E. & Donoso, Pedro & Gutiérrez, Leonel & Herl, Daniel & Muñoz, Diego, 2023. "A recursive stochastic transit equilibrium model estimated using passive data from Santiago, Chile," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    15. Pezoa, Raúl & Basso, Franco & Quilodrán, Paulina & Varas, Mauricio, 2023. "Estimation of trip purposes in public transport during the COVID-19 pandemic: The case of Santiago, Chile," Journal of Transport Geography, Elsevier, vol. 109(C).
    16. Shruti Sachdeva & Tarunpreet Bhatia & A. K. Verma, 2018. "GIS-based evolutionary optimized Gradient Boosted Decision Trees for forest fire susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(3), pages 1399-1418, July.
    17. Uğur Baç, 2020. "An Integrated SWARA-WASPAS Group Decision Making Framework to Evaluate Smart Card Systems for Public Transportation," Mathematics, MDPI, vol. 8(10), pages 1-24, October.
    18. Rashedi, Zohreh & Mahmoud, Mohamed & Hasnine, Sami & Habib, Khandker Nurul, 2017. "On the factors affecting the choice of regional transit for commuting in Greater Toronto and Hamilton Area: Application of an advanced RP-SP choice model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 105(C), pages 1-13.
    19. Zhou, Yang & Thill, Jean-Claude & Xu, Yang & Fang, Zhixiang, 2021. "Variability in individual home-work activity patterns," Journal of Transport Geography, Elsevier, vol. 90(C).
    20. Dandan Xu & Yang Bain & Shinan Shu & Xiaodong Zhang, 2022. "Staged Transition Process from Driving to Bicycling Based on the Effects of Latent Variables," Sustainability, MDPI, vol. 14(18), pages 1-14, September.

    More about this item

    Keywords

    Big data; Public transport; Smart card data; User perspective;
    All these keywords.

    JEL classification:

    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning

    Statistics

    Access and download statistics

    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:eee:retrec:v:69:y:2018:i:c:p:615-620. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/620614/description#description .

    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.