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Transport modelling in the age of big data

Author

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  • Cuauhtemoc Anda
  • Alexander Erath
  • Pieter Jacobus Fourie

Abstract

New Big Data sources such as mobile phone call data records, smart card data and geo-coded social media records allow to observe and understand mobility behaviour on an unprecedented level of detail. Despite the availability of such new Big Data sources, transport demand models used in planning practice still, almost exclusively, are based on conventional data such as travel diary surveys and population census. This literature review brings together recent advances in harnessing Big Data sources to understand travel behaviour and inform travel demand models that allow transport planners to compute what-if scenarios. From trip identification to activity inference, we review and analyse the existing data-mining methods that enable these opportunistically collected mobility traces inform transport demand models. We identify that future research should tap on the potential of probabilistic models and machine learning techniques as commonly used in data science. Those data-mining approaches are designed to handle the uncertainty of sparse and noisy data as it is the case for mobility traces derived from mobile phone data. In addition, they are suitable to integrate different related data sets in a data fusion scheme so as to enrich Big Data with information from travel diaries. In any case, we also acknowledge that sophisticated modelling knowledge has developed in the domain of transport planning and therefore we strongly advise that still, domain expert knowledge should build the fundament when applying data-driven approaches in transport planning. These new challenges call for a multidisciplinary collaboration between transport modellers and data scientists.

Suggested Citation

  • Cuauhtemoc Anda & Alexander Erath & Pieter Jacobus Fourie, 2017. "Transport modelling in the age of big data," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 21(0), pages 19-42, August.
    • Handle: RePEc:taf:rjusxx:v:21:y:2017:i:0:p:19-42
    DOI: 10.1080/12265934.2017.1281150
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    References listed on IDEAS

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    7. Ross-Perez, Antonio & Walton, Neil & Pinto, Nuno, 2022. "Identifying trip purpose from a dockless bike-sharing system in Manchester," Journal of Transport Geography, Elsevier, vol. 99(C).
    8. Chen, Ruoyu & Zhou, Jiangping, 2022. "Fare adjustment’s impacts on travel patterns and farebox revenue: An empirical study based on longitudinal smartcard data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 111-133.
    9. Kandt, Jens & Leak, Alistair, 2019. "Examining inclusive mobility through smartcard data: What shall we make of senior citizens' declining bus patronage in the West Midlands?," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.
    10. 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.
    11. Pieroni, Caio & Giannotti, Mariana & Alves, Bianca B. & Arbex, Renato, 2021. "Big data for big issues: Revealing travel patterns of low-income population based on smart card data mining in a global south unequal city," Journal of Transport Geography, Elsevier, vol. 96(C).
    12. Johannes Müller & Markus Straub & Gerald Richter & Christian Rudloff, 2021. "Integration of Different Mobility Behaviors and Intermodal Trips in MATSim," Sustainability, MDPI, vol. 14(1), pages 1-18, December.
    13. Yadi Zhu & Feng Chen & Ming Li & Zijia Wang, 2018. "Inferring the Economic Attributes of Urban Rail Transit Passengers Based on Individual Mobility Using Multisource Data," Sustainability, MDPI, vol. 10(11), pages 1-17, November.
    14. Benito Zaragozí & Sergio Trilles & Aaron Gutiérrez & Daniel Miravet, 2021. "Development of a Common Framework for Analysing Public Transport Smart Card Data," Energies, MDPI, vol. 14(19), pages 1-22, September.
    15. Yuan Liao & Sonia Yeh & Jorge Gil, 2022. "Feasibility of estimating travel demand using geolocations of social media data," Transportation, Springer, vol. 49(1), pages 137-161, February.
    16. Baochao Li & Xiaoshu Cao & Jianbin Xu & Wulin Wang & Shishu Ouyang & Dan Liu, 2021. "Spatial–Temporal Pattern and Influence Factors of Land Used for Transportation at the County Level since the Implementation of the Reform and Opening-Up Policy in China," Land, MDPI, vol. 10(8), pages 1-17, August.
    17. Tae-Hyoung Tommy Gim, 2018. "An Analysis of the Relationship between Land Use and Weekend Travel: Focusing on the Internal Capture of Trips," Sustainability, MDPI, vol. 10(2), pages 1-17, February.
    18. Tae-Hyoung Tommy Gim, 2018. "Tourist Satisfaction, Image, and Loyalty from an Interregional Perspective: An Analysis of Neighboring Areas with Distinct Characteristics," Sustainability, MDPI, vol. 10(4), pages 1-18, April.
    19. Michał Zawodny & Maciej Kruszyna, 2022. "Proposals for Using the Advanced Tools of Communication between Autonomous Vehicles and Infrastructure in Selected Cases," Energies, MDPI, vol. 15(18), pages 1-15, September.
    20. Bantis, Thanos & Haworth, James, 2020. "Assessing transport related social exclusion using a capabilities approach to accessibility framework: A dynamic Bayesian network approach," Journal of Transport Geography, Elsevier, vol. 84(C).

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