Affiliation:
1. School of Traffic and Transportation Engineering, Central South University , Changsha, Hunan 410075 , P. R. China
2. Department of Computer Science, City University of Hong Kong , Kowloon, Hong Kong 999077 , P. R. China
Abstract
Abstract
This study aims to develop a reliable safety evaluation model for diverging vehicles and investigates the impact of the surrounding traffic environment on freeway diverging risks. High-resolution trajectory data from three sites in the Netherlands (Delft, Ter-Heide and Zonzeel) were employed for the risk analysis. Linear regression (LR), support vector machine (SVM), random forest (RF), extreme randomize trees (ET), adaptive boosting (Adaboost), extreme gradient boosting (XGboost) and multilayer perceptron (MLP), were developed for safety evaluation. The result showed that MLP outperforms the other models for diverging risk prediction over all the indicators, conflict thresholds and locations. Pairwise matrix, shapely addictive explanation (SHAP), and LR algorithms were further adopted to interpret the influence of the surrounding environment. It indicates that an increase in traffic density, subject vehicle (SV) lateral speed, the distance of SV from ramp nose and SV length would increase the diverging risk. At the same time, an increase in leading vehicle speed and space headway would decrease diverging risk. Finally, spatial analysis was also conducted to explore the stability of identified traffic features regarding the impact on the diverging risk across the sites.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Hunan Province
Publisher
Oxford University Press (OUP)
Subject
Engineering (miscellaneous),Safety, Risk, Reliability and Quality,Control and Systems Engineering