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

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  • 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
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    References listed on IDEAS

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    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.
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    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

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