Driving risk assessment using near-crash database through data mining of tree-based model

Accident; Analysis and Prevention
Jianqiang WangKeqiang Li

Abstract

This paper considers a comprehensive naturalistic driving experiment to collect driving data under potential threats on actual Chinese roads. Using acquired real-world naturalistic driving data, a near-crash database is built, which contains vehicle status, potential crash objects, driving environment and road types, weather condition, and driver information and actions. The aims of this study are summarized into two aspects: (1) to cluster different driving-risk levels involved in near-crashes, and (2) to unveil the factors that greatly influence the driving-risk level. A novel method to quantify the driving-risk level of a near-crash scenario is proposed by clustering the braking process characteristics, namely maximum deceleration, average deceleration, and percentage reduction in vehicle kinetic energy. A classification and regression tree (CART) is employed to unveil the relationship among driving risk, driver/vehicle characteristics, and road environment. The results indicate that the velocity when braking, triggering factors, potential object type, and potential crash type exerted the greatest influence on the driving-risk levels in near-crashes.

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Citations

Nov 5, 2016·Accident; Analysis and Prevention·A ChevalierL Keay
Feb 15, 2021·Accident; Analysis and Prevention·Ashutosh ArunTarek Sayed
Apr 6, 2021·Accident; Analysis and Prevention·Byeongjoon Noh, Hwasoo Yeo
May 14, 2021·Accident; Analysis and Prevention·Xiaoqiang KongYunlong Zhang
Feb 8, 2020·Accident; Analysis and Prevention·Shifeng Niu, Satish V Ukkusuri

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