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Big data and understanding change in the context of planning transport systems

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  • Milne, Dave
  • Watling, David

Abstract

This paper considers the implications of so-called ‘big data’ for the analysis, modelling and planning of transport systems. The primary conceptual focus is on the needs of the practical context of medium-term planning and decision-making, from which perspective the paper seeks to achieve three goals: (i) to try to identify what is truly ‘special’ about big data; (ii) to provoke debate on the future relationship between transport planning and big data; and (iii) to try to identify promising themes for research and application. Differences in the information that can be derived from the data compared to more traditional surveys are discussed, and the respects in which they may impact on the role of models in supporting transport planning and decision-making are identified. It is argued that, over time, changes to the nature of data may lead to significant differences in both modelling approaches and in the expectations placed upon them. Furthermore, it is suggested that the potential widespread availability of data to commercial actors and travellers will affect the performance of the transport systems themselves, which might be expected to have knock-on effects for planning functions. We conclude by proposing a series of research challenges that we believe need to be addressed and warn against adaptations based on minimising change from the status quo.

Suggested Citation

  • Milne, Dave & Watling, David, 2019. "Big data and understanding change in the context of planning transport systems," Journal of Transport Geography, Elsevier, vol. 76(C), pages 235-244.
  • Handle: RePEc:eee:jotrge:v:76:y:2019:i:c:p:235-244
    DOI: 10.1016/j.jtrangeo.2017.11.004
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    5. F. Crawford & D. P. Watling & R. D. Connors, 2023. "Analysing Spatial Intrapersonal Variability of Road Users Using Point-to-Point Sensor Data," Networks and Spatial Economics, Springer, vol. 23(2), pages 373-406, June.
    6. Laila Oubahman & Szabolcs Duleba, 2022. "A Comparative Analysis of Homogenous Groups’ Preferences by Using AIP and AIJ Group AHP-PROMETHEE Model," Sustainability, MDPI, vol. 14(10), pages 1-18, May.
    7. Dong, Bing & Liu, Yapan & Fontenot, Hannah & Ouf, Mohamed & Osman, Mohamed & Chong, Adrian & Qin, Shuxu & Salim, Flora & Xue, Hao & Yan, Da & Jin, Yuan & Han, Mengjie & Zhang, Xingxing & Azar, Elie & , 2021. "Occupant behavior modeling methods for resilient building design, operation and policy at urban scale: A review," Applied Energy, Elsevier, vol. 293(C).
    8. Yang, Binyu & Tian, Yuan & Wang, Jian & Hu, Xiaowei & An, Shi, 2022. "How to improve urban transportation planning in big data era? A practice in the study of traffic analysis zone delineation," Transport Policy, Elsevier, vol. 127(C), pages 1-14.
    9. Masanobu Kii & Yuki Goda & Varameth Vichiensan & Hiroyuki Miyazaki & Rolf Moeckel, 2021. "Assessment of Spatiotemporal Peak Shift of Intra-Urban Transportation Taking a Case in Bangkok, Thailand," Sustainability, MDPI, vol. 13(12), pages 1-16, June.
    10. Saud, Veronica & Thomopoulos, Nikolas, 2021. "Towards inclusive transport landscapes: Re-visualising a Bicycle Sharing Scheme in Santiago Metropolitan Region," Journal of Transport Geography, Elsevier, vol. 92(C).
    11. Ghaffarpasand, Omid & Pope, Francis D., 2024. "Telematics data for geospatial and temporal mapping of urban mobility: New insights into travel characteristics and vehicle specific power," Journal of Transport Geography, Elsevier, vol. 115(C).
    12. Marko Šoštarić & Krešimir Vidović & Marijan Jakovljević & Orsat Lale, 2021. "Data-Driven Methodology for Sustainable Urban Mobility Assessment and Improvement," Sustainability, MDPI, vol. 13(13), pages 1-22, June.
    13. Liu, Xu & Dijk, Marc, 2022. "How more data reinforces evidence-based transport policy in the Short and Long-Term: Evaluating a policy pilot in two Dutch cities," Transport Policy, Elsevier, vol. 128(C), pages 166-178.
    14. Willis, George & Tranos, Emmanouil, 2020. "Using ‘Big Data’ to understand the impacts of Uber on taxis in New York City," SocArXiv 25fxs, Center for Open Science.
    15. Andrew Sudmant & Vincent Viguié & Quentin Lepetit & Lucy Oates & Abhijit Datey & Andy Gouldson & David Watling, 2021. "Fair weather forecasting? The shortcomings of big data for sustainable development, a case study from Hubballi‐Dharwad, India," Sustainable Development, John Wiley & Sons, Ltd., vol. 29(6), pages 1237-1248, November.
    16. Chu, Chih-Peng & Chou, Yu-Hsin, 2021. "Using cellular data to analyze the tourists' trajectories for tourism destination attributes: A case study in Hualien, Taiwan," Journal of Transport Geography, Elsevier, vol. 96(C).

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