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Travel Characteristics Identification Method for Expressway Passenger Cars Based on Electronic Toll Collection Data

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  • Xiaoyu Cai

    (College of Smart City, Chongqing Jiaotong University, Chongqing 400074, China
    Chongqing Key Laboratory of Mountain City Traffic System and Safety, Chongqing 400074, China)

  • Yihan Zhang

    (College of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China)

  • Xin Zhang

    (Cmcu Engineering Co., Ltd., Chongqing 400039, China)

  • Bo Peng

    (College of Smart City, Chongqing Jiaotong University, Chongqing 400074, China
    Chongqing Key Laboratory of Mountain City Traffic System and Safety, Chongqing 400074, China)

Abstract

Passenger cars have emerged as a substantial segment of the vehicles traversing expressways, generating extensive traffic data on a daily basis. Accurately identifying individual vehicles and their travel patterns and characteristics is crucial in addressing the issues that impede the sustainable development of expressways, including traffic accidents, congestion, environmental pollution, and losses of both personnel and property. Regrettably, the utilization of electronic toll collection (ETC) data on expressways is currently not adequate, and data analysis and feature mining methods are underdeveloped, leading to the undervaluation of data potential. Focusing on ETC data from expressways, this study deeply analyzes the spatiotemporal characteristics of travel by passenger car users. Here, we propose an advanced user classification model by combining the traditional clustering algorithm with the feature grouping recognition model based on a back propagation neural network (BPNN) algorithm. Real-world data on expressway vehicle travel are used to validate our models. The results show a significant improvement in iteration efficiency of over 26.4% and a 23.17% accuracy improvement compared to traditional algorithms. The travel feature grouping recognition model yielded an accuracy of 95.23%. Furthermore, among the identified groups, such as “Public and commercial affairs” and “Commuting”, there is a notable characteristic of high travel frequency and concentrated travel periods. This indicates that these groups have placed significant pressure on the construction of a safe, efficient, and sustainable urban transportation system.

Suggested Citation

  • Xiaoyu Cai & Yihan Zhang & Xin Zhang & Bo Peng, 2023. "Travel Characteristics Identification Method for Expressway Passenger Cars Based on Electronic Toll Collection Data," Sustainability, MDPI, vol. 15(15), pages 1-28, July.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:11619-:d:1204110
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    References listed on IDEAS

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    1. Zhiqiang Xu & Mahdi Aghaabbasi & Mujahid Ali & Elżbieta Macioszek, 2022. "Targeting Sustainable Transportation Development: The Support Vector Machine and the Bayesian Optimization Algorithm for Classifying Household Vehicle Ownership," Sustainability, MDPI, vol. 14(17), pages 1-17, September.
    2. Zhenbo Lu & Zhen Long & Jingxin Xia & Chengchuan An, 2019. "A Random Forest Model for Travel Mode Identification Based on Mobile Phone Signaling Data," Sustainability, MDPI, vol. 11(21), pages 1-21, October.
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