IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i21p6152-d283444.html
   My bibliography  Save this article

Exploring the Weekly Travel Patterns of Private Vehicles Using Automatic Vehicle Identification Data: A Case Study of Wuhan, China

Author

Listed:
  • Yuhui Zhao

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China)

  • Xinyan Zhu

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China)

  • Wei Guo

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China)

  • Bing She

    (Institute for Social Research, University of Michigan, Ann Arbor, MI 48109, USA)

  • Han Yue

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China)

  • Ming Li

    (Institute of Space Science and Technology, Nanchang University, Nanchang 330031, China)

Abstract

Automatic vehicle identification (AVI) systems collect 24 h vehicle travel data for the efficient management of traffic flows. The automatic vehicle identification data collected by an overhead traffic monitoring system provides a means for understanding urban traffic flows and human mobility. This article explores the weekly travel patterns of private vehicles based on AVI data in Wuhan, a megacity in Central China. We extracted origin–destination information and applied the K-Means clustering algorithm to classify spatial traffic hot spots by camera locations. Subsequently, the Latent Dirichlet Allocation algorithm was used to mine the temporal travel patterns of individual vehicles. The cluster results are summarized in nine travel probability matrixes. The effectiveness of this approach is illustrated by a case study using a large set of AVI data collected from 19 to 24 November 2018, in Wuhan, China. The results revealed six variations of the travel demand on weekdays and weekends—the commuting behaviors of private drivers triggered a tidal change in traffic flows. This study also exposed nine weekly travel patterns for private cars, reflecting temporal similarities of human mobility patterns. We identified four types of commuters. These results can help city managers understand daily changes in urban travel demands.

Suggested Citation

  • Yuhui Zhao & Xinyan Zhu & Wei Guo & Bing She & Han Yue & Ming Li, 2019. "Exploring the Weekly Travel Patterns of Private Vehicles Using Automatic Vehicle Identification Data: A Case Study of Wuhan, China," Sustainability, MDPI, vol. 11(21), pages 1-17, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:21:p:6152-:d:283444
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/21/6152/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/21/6152/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Luca Pappalardo & Filippo Simini & Salvatore Rinzivillo & Dino Pedreschi & Fosca Giannotti & Albert-László Barabási, 2015. "Returners and explorers dichotomy in human mobility," Nature Communications, Nature, vol. 6(1), pages 1-8, November.
    2. Zhang, Shen & Tang, Jinjun & Wang, Haixiao & Wang, Yinhai & An, Shi, 2017. "Revealing intra-urban travel patterns and service ranges from taxi trajectories," Journal of Transport Geography, Elsevier, vol. 61(C), pages 72-86.
    3. Yu Liu & Xi Liu & Song Gao & Li Gong & Chaogui Kang & Ye Zhi & Guanghua Chi & Li Shi, 2015. "Social Sensing: A New Approach to Understanding Our Socioeconomic Environments," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 105(3), pages 512-530, May.
    4. Han Yue & Xinyan Zhu, 2019. "Exploring the Relationship between Urban Vitality and Street Centrality Based on Social Network Review Data in Wuhan, China," Sustainability, MDPI, vol. 11(16), pages 1-19, August.
    5. Amaya, Margarita & Cruzat, Ramón & Munizaga, Marcela A., 2018. "Estimating the residence zone of frequent public transport users to make travel pattern and time use analysis," Journal of Transport Geography, Elsevier, vol. 66(C), pages 330-339.
    6. Carlos Lamsfus & David Martín & Aurkene Alzua-Sorzabal & Emilio Torres-Manzanera, 2015. "Smart Tourism Destinations: An Extended Conception of Smart Cities Focusing on Human Mobility," Springer Books, in: Iis Tussyadiah & Alessandro Inversini (ed.), Information and Communication Technologies in Tourism 2015, edition 127, pages 363-375, Springer.
    7. Liu, Xi & Gong, Li & Gong, Yongxi & Liu, Yu, 2015. "Revealing travel patterns and city structure with taxi trip data," Journal of Transport Geography, Elsevier, vol. 43(C), pages 78-90.
    8. 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.
    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. Guofa Li & Weijian Lai & Xingda Qu, 2020. "Association between Crash Attributes and Drivers’ Crash Involvement: A Study Based on Police-Reported Crash Data," IJERPH, MDPI, vol. 17(23), pages 1-16, December.
    2. Han Su & Qian Zhang & Wanying Wang & Xiaoan Tang, 2021. "A Driving Behavior Distribution Fitting Method Based on Two-Stage Hybrid User Classification," Sustainability, MDPI, vol. 13(13), pages 1-24, June.
    3. Alexandre B. Gonçalves, 2021. "Spatial Analysis and Geographic Information Systems as Tools for Sustainability Research," Sustainability, MDPI, vol. 13(2), pages 1-3, January.
    4. Chang-Jin Ma & Gong-Unn Kang, 2020. "Air Quality Variation in Wuhan, Daegu, and Tokyo during the Explosive Outbreak of COVID-19 and Its Health Effects," IJERPH, MDPI, vol. 17(11), pages 1-12, June.

    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. Disheng Yi & Yusi Liu & Jiahui Qin & Jing Zhang, 2020. "Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of Beijing," Sustainability, MDPI, vol. 12(22), pages 1-20, November.
    2. He, Yifan & Zhao, Chen & Zeng, An, 2022. "Ranking locations in a city via the collective home-work relations in human mobility data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    3. Helai Huang & Jialing Wu & Fang Liu & Yiwei Wang, 2020. "Measuring Accessibility Based on Improved Impedance and Attractive Functions Using Taxi Trajectory Data," Sustainability, MDPI, vol. 13(1), pages 1-23, December.
    4. 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.
    5. Li, Xijing & Ma, Xinlin & Wilson, Bev, 2021. "Beyond absolute space: An exploration of relative and relational space in Shanghai using taxi trajectory data," Journal of Transport Geography, Elsevier, vol. 93(C).
    6. Yang, Xiping & Fang, Zhixiang & Xu, Yang & Yin, Ling & Li, Junyi & Lu, Shiwei, 2019. "Spatial heterogeneity in spatial interaction of human movements—Insights from large-scale mobile positioning data," Journal of Transport Geography, Elsevier, vol. 78(C), pages 29-40.
    7. Jing Yang & Disheng Yi & Jingjing Liu & Yusi Liu & Jing Zhang, 2019. "Spatiotemporal Change Characteristics of Nodes’ Heterogeneity in the Directed and Weighted Spatial Interaction Networks: Case Study within the Sixth Ring Road of Beijing, China," Sustainability, MDPI, vol. 11(22), pages 1-15, November.
    8. Jiao, Hongzan & Huang, Shibiao & Zhou, Yu, 2023. "Understanding the land use function of station areas based on spatiotemporal similarity in rail transit ridership: A case study in Shanghai, China," Journal of Transport Geography, Elsevier, vol. 109(C).
    9. Yanyan Chen & Zheng Zhang & Tianwen Liang, 2019. "Assessing Urban Travel Patterns: An Analysis of Traffic Analysis Zone-Based Mobility Patterns," Sustainability, MDPI, vol. 11(19), pages 1-15, October.
    10. Xiping Yang & Zhixiang Fang & Ling Yin & Junyi Li & Yang Zhou & Shiwei Lu, 2018. "Understanding the Spatial Structure of Urban Commuting Using Mobile Phone Location Data: A Case Study of Shenzhen, China," Sustainability, MDPI, vol. 10(5), pages 1-14, May.
    11. Rongxiang Su & Zhixiang Fang & Ningxin Luo & Jingwei Zhu, 2018. "Understanding the Dynamics of the Pick-Up and Drop-Off Locations of Taxicabs in the Context of a Subsidy War among E-Hailing Apps," Sustainability, MDPI, vol. 10(4), pages 1-24, April.
    12. Katarzyna Sila-Nowicka & A. Stewart Fotheringham & Urška Demšar, 2023. "Activity triangles: a new approach to measure activity spaces," Journal of Geographical Systems, Springer, vol. 25(4), pages 489-517, October.
    13. Omer Dogan & Jaewon Han & Sugie Lee, 2021. "Opening Gated Communities and Neighborhood Accessibility Benefits: The Case of Seoul, Korea," IJERPH, MDPI, vol. 18(8), pages 1-15, April.
    14. Matteo Böhm & Mirco Nanni & Luca Pappalardo, 2022. "Gross polluters and vehicle emissions reduction," Nature Sustainability, Nature, vol. 5(8), pages 699-707, August.
    15. Benjamin Davies & David C. Maré, 2020. "Delineating functional labour market areas with estimable classification stabilities," Working Papers 20_08, Motu Economic and Public Policy Research.
    16. Xu, Paiheng & Yin, Likang & Yue, Zhongtao & Zhou, Tao, 2019. "On predictability of time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 345-351.
    17. 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).
    18. Assumpció Huertas & Antonio Moreno & Jordi Pascual, 2021. "Place Branding for Smart Cities and Smart Tourism Destinations: Do They Communicate Their Smartness?," Sustainability, MDPI, vol. 13(19), pages 1-18, October.
    19. Kirtonia, Sajeeb & Sun, Yanshuo, 2022. "Evaluating rail transit's comparative advantages in travel cost and time over taxi with open data in two U.S. cities," Transport Policy, Elsevier, vol. 115(C), pages 75-87.
    20. Zhou, Xiaolu & Wang, Mingshu & Li, Dongying, 2019. "Bike-sharing or taxi? Modeling the choices of travel mode in Chicago using machine learning," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.

    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:gam:jsusta:v:11:y:2019:i:21:p:6152-:d:283444. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.