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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
02.12.2022
DOI
https://doi.org/10.7307/ptt.v34i6.4154
ISSUE
2022 (Vol 34), Issue 6
LICENSE
Copyright (c) 2024 Xiaoxia Xiong, Yu He, Xiang Gao, Yeling Zhao
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

A Multi-Level Risk Framework for Driving Safety Assessment Based on Vehicle Trajectory

Authors:Xiaoxia Xiong, Yu He, Xiang Gao, Yeling Zhao

Abstract

Few existing research studies have explored the relationship of road section level, local area level and vehicle level risks within the highway traffic safety system, which can be important to the formation of an effective risk event prediction. This paper proposes a framework of multi-level risks described by a set of carefully selected or designed indicators. The interrelationship among these latent multi-level risks and their observable indicators are explored based on vehicle trajectory data using the structural equation model (SEM). The results show that there exists significant positive correlation between the latent risk constructs that each have adequate convergent validity, and it is difficult to completely separate the local traffic level risk from both the road section level risk and vehicle level risk. The local and road level indicators are also found to be of more importance when risk prediction time gets earlier based on feature importance scoring of the LightGBM. The proposed conceptual multi-level indicator based latent risk framework generally fits with the observed results and emphasises the importance of including multi-level indicators for risk event prediction in the future.

Keywords:traffic safety, multi-level risk, safety indictor, SEM, vehicle trajectory

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