IDEAS home Printed from https://ideas.repec.org/a/taf/rjusxx/v21y2017i0p19-42.html
   My bibliography  Save this article

Transport modelling in the age of big data

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/12265934.2017.1281150
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/12265934.2017.1281150?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. Han, Gain & Sohn, Keemin, 2016. "Activity imputation for trip-chains elicited from smart-card data using a continuous hidden Markov model," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 121-135.
    2. Sun, Lijun & Tirachini, Alejandro & Axhausen, Kay W. & Erath, Alexander & Lee, Der-Horng, 2014. "Models of bus boarding and alighting dynamics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 447-460.
    3. 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.
    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. Gleb V. Savin, 2021. "The smart city transport and logistics system: Theory, methodology and practice," Upravlenets, Ural State University of Economics, vol. 12(6), pages 67-86, October.
    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. Pizzol, Bruna & Strambi, Orlando & Giannotti, Mariana & Arbex, Renato Oliveira & Alves, Bianca Bianchi, 2021. "Activity behavior of residents of Paraisópolis slum: Analysis of multiday activity patterns using data collected with smartphones," Journal of choice modelling, Elsevier, vol. 39(C).
    4. Carolina Ajeng & Tae-Hyoung Tommy Gim, 2018. "Analyzing on-Street Parking Duration and Demand in a Metropolitan City of a Developing Country: A Case Study of Yogyakarta City, Indonesia," Sustainability, MDPI, vol. 10(3), pages 1-14, February.
    5. 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.
    6. María Vega-Gonzalo & Panayotis Christidis, 2022. "Fair Models for Impartial Policies: Controlling Algorithmic Bias in Transport Behavioural Modelling," Sustainability, MDPI, vol. 14(14), pages 1-23, July.
    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).

    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. Qingru Zou & Xiangming Yao & Peng Zhao & Heng Wei & Hui Ren, 2018. "Detecting home location and trip purposes for cardholders by mining smart card transaction data in Beijing subway," Transportation, Springer, vol. 45(3), pages 919-944, May.
    2. Sun, Lijun & Axhausen, Kay W., 2016. "Understanding urban mobility patterns with a probabilistic tensor factorization framework," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 511-524.
    3. Lian, Liping & Song, Weiguo & Yuen, Kwok Kit Richard & Telesca, Luciano, 2018. "Investigating the time evolution of some parameters describing inflow processes of pedestrians in a room," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 77-88.
    4. 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.
    5. Ji, Yanjie & Gao, Liangpeng & Chen, Dandan & Ma, Xinwei & Zhang, Ruochen, 2018. "How does a static measure influence passengers’ boarding behaviors and bus dwell time? Simulated evidence from Nanjing bus stations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 13-25.
    6. Fangye Du & Jiaoe Wang & Liang Mao & Jian Kang, 2024. "Daily rhythm of urban space usage: insights from the nexus of urban functions and human mobility," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    7. Mohammadi, Neda & Taylor, John E., 2017. "Urban energy flux: Spatiotemporal fluctuations of building energy consumption and human mobility-driven prediction," Applied Energy, Elsevier, vol. 195(C), pages 810-818.
    8. Cen Zhang & Jan-Dirk Schmöcker & Martin Trépanier, 2022. "Latent stage model for carsharing usage frequency estimation with Montréal case study," Transportation, Springer, vol. 49(1), pages 185-211, February.
    9. Sheng Wei & Lei Wang, 2020. "Examining the population flow network in China and its implications for epidemic control based on Baidu migration data," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-10, December.
    10. Cheng Shi & Yujia Zhai & Dongying Li, 2023. "Urban tourists’ spatial distribution and subgroup identification in a metropolis --the examination applying mobile signaling data and latent profile analysis," Information Technology & Tourism, Springer, vol. 25(3), pages 453-476, September.
    11. 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.
    12. Schmöcker, Jan-Dirk & Sun, Wenzhe & Fonzone, Achille & Liu, Ronghui, 2016. "Bus bunching along a corridor served by two lines," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 300-317.
    13. Márquez, Luis & Alfonso A, Julieth V. & Poveda, Juan C., 2019. "In-vehicle crowding: Integrating tangible attributes, attitudes, and perceptions in a choice context between BRT and metro," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 452-465.
    14. Jungmin Kim & Juyong Park & Wonjae Lee, 2018. "Why do people move? Enhancing human mobility prediction using local functions based on public records and SNS data," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-29, February.
    15. Mohammadi, Neda & Taylor, John E., 2017. "Urban infrastructure-mobility energy flux," Energy, Elsevier, vol. 140(P1), pages 716-728.
    16. Meead Saberi & Taha H. Rashidi & Milad Ghasri & Kenneth Ewe, 2018. "A Complex Network Methodology for Travel Demand Model Evaluation and Validation," Networks and Spatial Economics, Springer, vol. 18(4), pages 1051-1073, December.
    17. Bai, Qiaowen & Ong, Ghim Ping, 2023. "Similarity-based bus services assignment with capacity constraint for staggered bus stops," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    18. Liu, Xiaodong & Song, Weiguo & Fu, Libi & Fang, Zhiming, 2016. "Experimental study of pedestrian inflow in a room with a separate entrance and exit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 224-238.
    19. Arias, Mariz B. & Kim, Myungchin & Bae, Sungwoo, 2017. "Prediction of electric vehicle charging-power demand in realistic urban traffic networks," Applied Energy, Elsevier, vol. 195(C), pages 738-753.
    20. Krause, Cory M. & Zhang, Lei, 2019. "Short-term travel behavior prediction with GPS, land use, and point of interest data," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 349-361.

    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:taf:rjusxx:v:21:y:2017:i:0:p:19-42. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/rjus20 .

    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.