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Exploring spatial distribution patterns and associate factors of urban traffic chaos using GPS trajectory and onboard image of vehicles

Author

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  • Li, Bozhao
  • Ge, Yijun
  • Liu, Xuanyu
  • Cai, Zhongliang
  • Su, Shiliang
  • Kang, Mengjun

Abstract

Urban transportation is crucial for sustainable urban development. Among the challenges associated with urban transportation, traffic chaos stands out as a significant issue, disrupting smooth traffic flow, posing risks to traffic safety, hampering traffic efficiency and causing traffic congestion. Nowadays, extensive research has been conducted on traffic congestion and flow prediction, but there is still a lack of effective measurement indicators and monitoring methods for traffic chaos. To address these issues, this paper first proposes a traffic chaos index based on information entropy theory to evaluate the degree of chaos in urban traffic. A calculation pipeline is then developed to enable large-scale monitoring of urban traffic chaos using GPS trajectories and onboard images from vehicles. Additionally, key built environment factors contributing to traffic chaos are selected, and an interpretable machine learning method is employed to preliminarily explore their impacts on traffic chaos. Results reveal that road design indicators such as road level and non-motorized lane types have the greatest impact on traffic chaos, followed by the density of points of interest (POIs) related to catering and shopping services. Based on these findings, we propose several practical recommendations for road planning and management to mitigate urban traffic chaos. In the future, we will further integrate the proposed traffic chaos index and calculation pipeline into dashcams and road surveillance cameras to achieve large-scale, long-term and all-weather dynamic monitoring of traffic chaos. This will provide data support for comprehensive analyses and predictions of traffic chaos across multiple cities.

Suggested Citation

  • Li, Bozhao & Ge, Yijun & Liu, Xuanyu & Cai, Zhongliang & Su, Shiliang & Kang, Mengjun, 2026. "Exploring spatial distribution patterns and associate factors of urban traffic chaos using GPS trajectory and onboard image of vehicles," Transport Policy, Elsevier, vol. 176(C).
    • Handle: RePEc:eee:trapol:v:176:y:2026:i:c:s0967070x25004457
    DOI: 10.1016/j.tranpol.2025.103902
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    References listed on IDEAS

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