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

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  • 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
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    Cited by:

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    2. 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.
    3. 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.
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    5. Meng, Yihao & Zou, Yuan & Ji, Chengda & Zhai, Jianyang & Zhang, Xudong & Zhang, Zhaolong, 2024. "Data-driven electric vehicle usage and charging analysis of logistics vehicle in Shenzhen, China," Energy, Elsevier, vol. 307(C).
    6. Alexandre B. Gonçalves, 2021. "Spatial Analysis and Geographic Information Systems as Tools for Sustainability Research," Sustainability, MDPI, vol. 13(2), pages 1-3, January.

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