Journal of South China University of Technology(Natural Science) >

2025 , Vol. 53 >Issue 8: 50 - 60

DOI: https://doi.org/10.12141/j.issn.1000-565X.240437

Intelligent Transportation System

An Investigation into Expressway Merging Behavior and Safety Based on ExiD Data

  • WEN Huiying ,
  • HUANG Kunhuo ,
  • CHEN Zhe ,
  • ZHAO Sheng ,
  • HU Yuqing ,
  • HUANG Junda
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  • 1.South China University of Technology，Guangzhou 510640，Guangdong，China
    2.Guangdong E -Serve United Co. ，Ltd. ，Guangzhou 510640，Guangdong，China
温惠英（1965—），女，教授，博士生导师，主要从事交通规划、交通安全研究。E-mail： hywen@scut.edu.cn

Received date: 2024-08-29

  Online published: 2025-01-13

Supported by

the National Natural Science Foundation of China(52372329)

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Abstract

Expressway merging areas are characterized by frequent lane changes, complex driving environments, and intense traffic conflicts, making them high-risk zones for traffic accidents. Accurately understanding the relationship between vehicle operating status and traffic safety in these merging areas can provide a foundation for real-time accident risk prediction and the development of effective traffic safety control strategies. This study is based on vehicle trajectory data from the German ExiD dataset. By analyzing the changes in the relative positions of mainline outer-lane vehicles during the process from a vehicle entering the acceleration lane to merging into the mainline, the merging patterns in expressway merging areas were classified. To systematically describe the safety risks associated with merging, this study introduced the Time to Collision theory and developed a risk representation framework. This framework includes two levels of indicators: (1) a merging moment risk indicator based on two-dimensional TTC, which evaluates potential conflict at the moment of merging; and (2) a merging process risk indicator based on collision exposure time, which reflects the accumulated risk throughout the merging process. For model development, four machine learning algorithms—XGBoost, LightGBM, GBDT, and Random Forest—were used to build a classification model for merging risk. In addition, SHAP was applied to interpret the model and analyzed the key factors influencing merging risk. Experimental results show that the XGBoost-based risk identification model for expressway merging areas outperforms other models, achieving an overall accuracy of 95.52%. It also demonstrates superior performance in terms of accuracy, precision, recall, and F1-score. Furthermore, comparison among models indicates that incorporating merging duration and urgency significantly improves risk identification accuracy. SHAP analysis further reveals that merging risk is closely related to several factors, including the ave-rage and maximum speed differences with the leading vehicle on the mainline, the average distance to the leading vehicle, merging duration, the standard deviation of longitudinal acceleration during merging, and the speed of the merging vehicle.

Key words： traffic safety; expressway merging areas; traffic conflicts; risk identification; machine learning

Cite this article

WEN Huiying , HUANG Kunhuo , CHEN Zhe , ZHAO Sheng , HU Yuqing , HUANG Junda . An Investigation into Expressway Merging Behavior and Safety Based on ExiD Data[J]. Journal of South China University of Technology(Natural Science), 2025 , 53(8) : 50 -60 . DOI: 10.12141/j.issn.1000-565X.240437

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