IDEAS home Printed from https://ideas.repec.org/a/eee/jotrge/v115y2024ics0966692324000243.html
   My bibliography  Save this article

Telematics data for geospatial and temporal mapping of urban mobility: New insights into travel characteristics and vehicle specific power

Author

Listed:
  • Ghaffarpasand, Omid
  • Pope, Francis D.

Abstract

This paper describes a new approach for understanding urban mobility called geospatial and temporal (GeoST) mapping, which translates telematics (location) data into travel characteristics. The approach provides the speed-acceleration profile of transport flow at high spatial and temporal resolution. The speed-acceleration profiles can be converted to vehicle-specific power (VSP), which can be used to estimate vehicle emissions. The underlying data used in the model is retrieved from a large telematics dataset, which was collected from GPS-connected vehicles during their journeys over the UK's West Midlands region road network for the years 2016 and 2018. Single journey telematics data were geospatially aggregated and then distributed over GeoST-segments. In total, approximately 354,000 GeoST-segments, covering over 17,700 km of roads over 35 timeslots are parameterized. GeoST mapping of the average vehicle speed (traffic flow), and VSP over different road types are analysed. The role of road slope upon VSP is estimated for every GeoST-segment through knowledge of the elevation of the start and end points of the segments. Previously, road slope and its effect upon VSP have been typically ignored in transport and urban planning studies. A series of case studies are presented that highlight the power of the new approach over differing spatial and temporal scales. For example, results show that the total vehicle fleet moved faster by an average of 3% in 2016 compared to 2018. The studied roads at weekends are shown to be less safe, compared to weekdays, because of lower adherence to speed limits. By including road slope in VSP calculations, the annually averaged VSP results differ by +12.6%, +14.3%, and + 12.7% for motorways, primary roads, and secondary roads, respectively, when compared to calculations that ignore road slope.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:jotrge:v:115:y:2024:i:c:s0966692324000243
    DOI: 10.1016/j.jtrangeo.2024.103815
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0966692324000243
    Download Restriction: no

    File URL: https://libkey.io/10.1016/j.jtrangeo.2024.103815?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. Börjesson, Maria & Bastian, Anne & Eliasson, Jonas, 2021. "The economics of low emission zones," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 99-114.
    2. Zhai, Guocong & Xie, Kun & Yang, Di & Yang, Hong, 2022. "Assessing the safety effectiveness of citywide speed limit reduction: A causal inference approach integrating propensity score matching and spatial difference-in-differences," Transportation Research Part A: Policy and Practice, Elsevier, vol. 157(C), pages 94-106.
    3. Pestel, Nico & Wozny, Florian, 2021. "Health effects of Low Emission Zones: Evidence from German hospitals," Journal of Environmental Economics and Management, Elsevier, vol. 109(C).
    4. 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.
    5. José I. Huertas & Michael Giraldo & Luis F. Quirama & Jenny Díaz, 2018. "Driving Cycles Based on Fuel Consumption," Energies, MDPI, vol. 11(11), pages 1-13, November.
    6. Omid Ghaffarpasand & Mark Burke & Louisa K. Osei & Helen Ursell & Sam Chapman & Francis D. Pope, 2022. "Vehicle Telematics for Safer, Cleaner and More Sustainable Urban Transport: A Review," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    7. 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.
    8. Gao, Guangyuan & Meng, Shengwang & Wüthrich, Mario V., 2022. "What can we learn from telematics car driving data: A survey," Insurance: Mathematics and Economics, Elsevier, vol. 104(C), pages 185-199.
    Full references (including those not matched with items on IDEAS)

    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. Rawad Choubassi & Lamia Abdelfattah, 2020. "How Big Data is Transforming the Way We Plan Our Cities," Briefs, Fondazione Eni Enrico Mattei, December.
    2. Fabio Bothner & Annette Elisabeth Töller & Paul Philipp Schnase, 2022. "Do Lawsuits by ENGOs Improve Environmental Quality? Results from the Field of Air Pollution Policy in Germany," Sustainability, MDPI, vol. 14(11), pages 1-18, May.
    3. 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).
    4. 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.
    5. 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.
    6. Euchi, Jalel & Kallel, Ahmed, 2021. "Internalization of external congestion and CO2emissions costs related to road transport: The case of Tunisia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    7. Chaogui Kang & Dongwan Fan & Hongzan Jiao, 2021. "Validating activity, time, and space diversity as essential components of urban vitality," Environment and Planning B, , vol. 48(5), pages 1180-1197, June.
    8. Zhang, Shen & Liu, Xin & Tang, Jinjun & Cheng, Shaowu & Qi, Yong & Wang, Yinhai, 2018. "Spatio-temporal modeling of destination choice behavior through the Bayesian hierarchical approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 537-551.
    9. 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.
    10. Zvonimir Dabčević & Branimir Škugor & Jakov Topić & Joško Deur, 2022. "Synthesis of Driving Cycles Based on Low-Sampling-Rate Vehicle-Tracking Data and Markov Chain Methodology," Energies, MDPI, vol. 15(11), pages 1-21, June.
    11. Li Zhao & Kun Li & Wu Zhao & Han-Chen Ke & Zhen Wang, 2022. "A Sticky Sampling and Markov State Transition Matrix Based Driving Cycle Construction Method for EV," Energies, MDPI, vol. 15(3), pages 1-19, January.
    12. Yang, Zhuo & Franz, Mark L. & Zhu, Shanjiang & Mahmoudi, Jina & Nasri, Arefeh & Zhang, Lei, 2018. "Analysis of Washington, DC taxi demand using GPS and land-use data," Journal of Transport Geography, Elsevier, vol. 66(C), pages 35-44.
    13. 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).
    14. Tianming Gao & Vasilii Erokhin & Aleksandr Arskiy, 2019. "Dynamic Optimization of Fuel and Logistics Costs as a Tool in Pursuing Economic Sustainability of a Farm," Sustainability, MDPI, vol. 11(19), pages 1-16, October.
    15. Xia, Dawen & Jiang, Shunying & Yang, Nan & Hu, Yang & Li, Yantao & Li, Huaqing & Wang, Lin, 2021. "Discovering spatiotemporal characteristics of passenger travel with mobile trajectory big data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    16. 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.
    17. Tang, Jinjun & Zhang, Shen & Zhang, Wenhui & Liu, Fang & Zhang, Weibin & Wang, Yinhai, 2016. "Statistical properties of urban mobility from location-based travel networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 694-707.
    18. Lei Zhang & Guoxing Zhang & Zhizheng Liang & Ekene Frank Ozioko, 2018. "Multi-features taxi destination prediction with frequency domain processing," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-22, March.
    19. 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.
    20. Tang, Jinjun & Liang, Jian & Zhang, Shen & Huang, Helai & Liu, Fang, 2018. "Inferring driving trajectories based on probabilistic model from large scale taxi GPS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 566-577.

    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:115:y:2024:i:c:s0966692324000243. 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.