IDEAS home Printed from https://ideas.repec.org/a/eee/jotrge/v76y2019icp235-244.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0966692317300984
    Download Restriction: no

    File URL: https://libkey.io/10.1016/j.jtrangeo.2017.11.004?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
    ---><---

    References listed on IDEAS

    as
    1. Brendan Pender & Graham Currie & Alexa Delbosc & Nirajan Shiwakoti, 2014. "Social Media Use during Unplanned Transit Network Disruptions: A Review of Literature," Transport Reviews, Taylor & Francis Journals, vol. 34(4), pages 501-521, July.
    2. Simon Oh & Young-Ji Byon & Kitae Jang & Hwasoo Yeo, 2015. "Short-term Travel-time Prediction on Highway: A Review of the Data-driven Approach," Transport Reviews, Taylor & Francis Journals, vol. 35(1), pages 4-32, January.
    3. Ian Philips & Graham Clarke & David Watling, 2017. "A Fine Grained Hybrid Spatial Microsimulation Technique for Generating Detailed Synthetic Individuals from Multiple Data Sources: An Application To Walking And Cycling," International Journal of Microsimulation, International Microsimulation Association, vol. 10(1), pages 167-200.
    4. Steenbruggen, John & Tranos, Emmanouil & Nijkamp, Peter, 2015. "Data from mobile phone operators: A tool for smarter cities?," Telecommunications Policy, Elsevier, vol. 39(3), pages 335-346.
    5. Crawford, F. & Watling, D.P. & Connors, R.D., 2017. "A statistical method for estimating predictable differences between daily traffic flow profiles," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 196-213.
    6. Loidl, Martin & Traun, Christoph & Wallentin, Gudrun, 2016. "Spatial patterns and temporal dynamics of urban bicycle crashes—A case study from Salzburg (Austria)," Journal of Transport Geography, Elsevier, vol. 52(C), pages 38-50.
    7. Zhu, Shanjiang & Levinson, David & Liu, Henry X. & Harder, Kathleen, 2010. "The traffic and behavioral effects of the I-35W Mississippi River bridge collapse," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(10), pages 771-784, December.
    8. Tao, Sui & Rohde, David & Corcoran, Jonathan, 2014. "Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap," Journal of Transport Geography, Elsevier, vol. 41(C), pages 21-36.
    9. Yu Liu & Zhengwei Sui & Chaogui Kang & Yong Gao, 2014. "Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.
    10. Hara, Yusuke & Kuwahara, Masao, 2015. "Traffic Monitoring immediately after a major natural disaster as revealed by probe data – A case in Ishinomaki after the Great East Japan Earthquake," Transportation Research Part A: Policy and Practice, Elsevier, vol. 75(C), pages 1-15.
    11. repec:ijm:journl:v109:y:2017:i:1:p:167-200 is not listed on IDEAS
    12. Saadi, Ismaïl & Boussauw, Kobe & Teller, Jacques & Cools, Mario, 2016. "Trends in regional jobs-housing proximity based on the minimum commute: The case of Belgium," Journal of Transport Geography, Elsevier, vol. 57(C), pages 171-183.
    13. Peter Widhalm & Yingxiang Yang & Michael Ulm & Shounak Athavale & Marta González, 2015. "Discovering urban activity patterns in cell phone data," Transportation, Springer, vol. 42(4), pages 597-623, July.
    14. Tang, Jinjun & Liu, Fang & Wang, Yinhai & Wang, Hua, 2015. "Uncovering urban human mobility from large scale taxi GPS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 140-153.
    15. Sun, Zhanbo & Zan, Bin & Ban, Xuegang (Jeff) & Gruteser, Marco, 2013. "Privacy protection method for fine-grained urban traffic modeling using mobile sensors," Transportation Research Part B: Methodological, Elsevier, vol. 56(C), pages 50-69.
    16. Mei-Po Kwan, 2016. "Algorithmic Geographies: Big Data, Algorithmic Uncertainty, and the Production of Geographic Knowledge," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 106(2), pages 274-282, March.
    17. Ambrosino, Giorgio & Nelson, John D. & Boero, Marco & Pettinelli, Irene, 2016. "Enabling intermodal urban transport through complementary services: From Flexible Mobility Services to the Shared Use Mobility Agency," Research in Transportation Economics, Elsevier, vol. 59(C), pages 179-184.
    18. Rodrigues da Silva, Antônio Nélson & Manzato, Gustavo Garcia & Pereira, Heber Tiago Santos, 2014. "Defining functional urban regions in Bahia, Brazil, using roadway coverage and population density variables," Journal of Transport Geography, Elsevier, vol. 36(C), pages 79-88.
    19. Tamblay, Sebastián & Galilea, Patricia & Iglesias, Paula & Raveau, Sebastián & Muñoz, Juan Carlos, 2016. "A zonal inference model based on observed smart-card transactions for Santiago de Chile," Transportation Research Part A: Policy and Practice, Elsevier, vol. 84(C), pages 44-54.
    20. Jestico, Ben & Nelson, Trisalyn & Winters, Meghan, 2016. "Mapping ridership using crowdsourced cycling data," Journal of Transport Geography, Elsevier, vol. 52(C), pages 90-97.
    21. Morten Skou Nicolaisen & Patrick Arthur Driscoll, 2014. "Ex-Post Evaluations of Demand Forecast Accuracy: A Literature Review," Transport Reviews, Taylor & Francis Journals, vol. 34(4), pages 540-557, July.
    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. Rawad Choubassi & Lamia Abdelfattah, 2020. "How Big Data is Transforming the Way We Plan Our Cities," Briefs, Fondazione Eni Enrico Mattei, December.
    2. Christian Werner & Martin Loidl, 2021. "Bicycle Mobility Data: Current Use and Future Potential. An International Survey of Domain Professionals," Data, MDPI, vol. 6(11), pages 1-11, November.
    3. Nadav Shalit & Michael Fire & Eran Ben-Elia, 2023. "A supervised machine learning model for imputing missing boarding stops in smart card data," Public Transport, Springer, vol. 15(2), pages 287-319, June.
    4. 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.
    5. 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.
    6. Kevin Credit & Zander Arnao, 2023. "A method to derive small area estimates of linked commuting trips by mode from open source LODES and ACS data," Environment and Planning B, , vol. 50(3), pages 709-722, March.
    7. 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.
    8. 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.
    9. 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).
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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).
    15. 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).

    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. Claudio Gariazzo & Armando Pelliccioni & Maria Paola Bogliolo, 2019. "Spatiotemporal Analysis of Urban Mobility Using Aggregate Mobile Phone Derived Presence and Demographic Data: A Case Study in the City of Rome, Italy," Data, MDPI, vol. 4(1), pages 1-25, January.
    2. Wang, Hwachyi & De Backer, Hans & Lauwers, Dirk & Chang, S.K.Jason, 2019. "A spatio-temporal mapping to assess bicycle collision risks on high-risk areas (Bridges) - A case study from Taipei (Taiwan)," Journal of Transport Geography, Elsevier, vol. 75(C), pages 94-109.
    3. D. Woods & A. Cunningham & C. E. Utazi & M. Bondarenko & L. Shengjie & G. E. Rogers & P. Koper & C. W. Ruktanonchai & E. zu Erbach-Schoenberg & A. J. Tatem & J. Steele & A. Sorichetta, 2022. "Exploring methods for mapping seasonal population changes using mobile phone data," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-17, December.
    4. Hwachyi Wang & S. K. Jason Chang & Hans De Backer & Dirk Lauwers & Philippe De Maeyer, 2019. "Integrating Spatial and Temporal Approaches for Explaining Bicycle Crashes in High-Risk Areas in Antwerp (Belgium)," Sustainability, MDPI, vol. 11(13), pages 1-28, July.
    5. Zhao, Pengxiang & Kwan, Mei-Po & Qin, Kun, 2017. "Uncovering the spatiotemporal patterns of CO2 emissions by taxis based on Individuals' daily travel," Journal of Transport Geography, Elsevier, vol. 62(C), pages 122-135.
    6. Zhao, Shuangming & Zhao, Pengxiang & Cui, Yunfan, 2017. "A network centrality measure framework for analyzing urban traffic flow: A case study of Wuhan, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 478(C), pages 143-157.
    7. Zhou, Jiangping & Sipe, Neil & Ma, Zhenliang & Mateo-Babiano, Derlie & Darchen, Sébastien, 2019. "Monitoring transit-served areas with smartcard data: A Brisbane case study," Journal of Transport Geography, Elsevier, vol. 76(C), pages 265-275.
    8. Jing Wu & Changlong Ling & Xinzhuo Li, 2019. "Study on the Accessibility and Recreational Development Potential of Lakeside Areas Based on Bike-Sharing Big Data Taking Wuhan City as an Example," Sustainability, MDPI, vol. 12(1), pages 1-20, December.
    9. Chen, Wendong & Chen, Xuewu & Cheng, Long & Liu, Xize & Chen, Jingxu, 2022. "Delineating borders of urban activity zones with free-floating bike sharing spatial interaction network," Journal of Transport Geography, Elsevier, vol. 104(C).
    10. Mengyao Ren & Yaoyu Lin & Meihan Jin & Zhongyuan Duan & Yongxi Gong & Yu Liu, 2020. "Examining the effect of land-use function complementarity on intra-urban spatial interactions using metro smart card records," Transportation, Springer, vol. 47(4), pages 1607-1629, August.
    11. John Östh & Ian Shuttleworth & Thomas Niedomysl, 2018. "Spatial and temporal patterns of economic segregation in Sweden’s metropolitan areas: A mobility approach," Environment and Planning A, , vol. 50(4), pages 809-825, June.
    12. Rui Ding & Norsidah Ujang & Hussain Bin Hamid & Mohd Shahrudin Abd Manan & Rong Li & Safwan Subhi Mousa Albadareen & Ashkan Nochian & Jianjun Wu, 2019. "Application of Complex Networks Theory in Urban Traffic Network Researches," Networks and Spatial Economics, Springer, vol. 19(4), pages 1281-1317, December.
    13. Kang Wu & Jingxian Tang & Ying Long, 2019. "Delineating the Regional Economic Geography of China by the Approach of Community Detection," Sustainability, MDPI, vol. 11(21), pages 1-18, October.
    14. Liping Ge & Malek Sarhani & Stefan Voß & Lin Xie, 2021. "Review of Transit Data Sources: Potentials, Challenges and Complementarity," Sustainability, MDPI, vol. 13(20), pages 1-37, October.
    15. Jiang, Shixiong & Guan, Wei & Zhang, Wenyi & Chen, Xu & Yang, Liu, 2017. "Human mobility in space from three modes of public transportation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 227-238.
    16. Coker, Ayodeji Ajibola Alexander, 2021. "Accuracy of in Medias RES and EX-Post Cost-Benefit Analyses: A Case of National Special Programme for Food Security, Nigeria," 2021 Conference, August 17-31, 2021, Virtual 314936, International Association of Agricultural Economists.
    17. Sarker, Rumana Islam & Kaplan, Sigal & Mailer, Markus & Timmermans, Harry J.P., 2019. "Applying affective event theory to explain transit users’ reactions to service disruptions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 593-605.
    18. Liu, Yan & Wang, Siqin & Xie, Bin, 2019. "Evaluating the effects of public transport fare policy change together with built and non-built environment features on ridership: The case in South East Queensland, Australia," Transport Policy, Elsevier, vol. 76(C), pages 78-89.
    19. Fabio Antonialli & Rodrigo Gandia & Joel Sugano & Isabelle Nicolaï & Arthur Neto, 2019. "Business Platforms For Autonomous Vehicles Within Urban Mobility," Post-Print halshs-03687640, HAL.
    20. Liu, Shan & Zhang, Ya & Wang, Zhengli & Gu, Shiyi, 2023. "AdaBoost-Bagging deep inverse reinforcement learning for autonomous taxi cruising route and speed planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).

    More about this item

    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:jotrge:v:76:y:2019:i:c:p:235-244. 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: https://www.journals.elsevier.com/journal-of-transport-geography .

    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.