A high-resolution trajectory data driven method for real-time evaluation of traffic safety

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

72 Scopus Citations
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Author(s)

  • Yuping Hu
  • Ye Li
  • Helai Huang
  • Jaeyoung Lee
  • Guoqing Zou

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number106503
Journal / PublicationAccident Analysis and Prevention
Volume165
Online published2 Dec 2021
Publication statusPublished - Feb 2022

Abstract

Real-time safety evaluation is essential for developing proactive safety management strategy and improving the overall traffic safety. This paper proposes a method for real-time evaluation of road safety, in which traffic states and conflicts are combined to explore the internal relationship based on high-resolution trajectory data. In order to assess the real-time traffic safety at a lane level, the trajectory data of the HighD dataset from Germany are utilized to collect lane-based dataset. A surrogate safety measure, time-to-collision (TTC) index, is used for the conflict identification. A binary logistic regression model is employed to quantify the relationship between traffic states and conflicts. Moreover, machine learning methods, including support vector machine, decision tree, random forest, and gradient boosting decision tree, are applied for real-time evaluation. A total of 24 models are trained using the selected four classifier algorithms, and random forest achieves the best performance with 0.85 of the overall accuracy. The results show that the conflict risk can be well estimated by the proposed method. The findings of this study contribute to the high-precision evaluation of real-time traffic safety and the development of proactive safety management.

Research Area(s)

  • Real-time evaluation, Road safety, Traffic conflict, Trajectory

Citation Format(s)

A high-resolution trajectory data driven method for real-time evaluation of traffic safety. / Hu, Yuping; Li, Ye; Huang, Helai et al.
In: Accident Analysis and Prevention, Vol. 165, 106503, 02.2022.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review