详情

原文传递 Bayesian network for red-light-running prediction at signalized intersections

题名：	Bayesian network for red-light-running prediction at signalized intersections
正文语种：	英文
作者：	Xiqun (Michael) Chen, Lingxiao Zhou; Li Li
作者单位：	College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China; Department of Automation, Tsinghua University, Beijing, China
关键词：	Bayesian network (BN); causality interpretation; probabilistic output; red-light-running (RLR) prediction
摘要：	Red-light-running (RLR) is an important reason for the large number of intersection-related fatalities, injuries, and other losses. The accurate RLR prediction can effectively reduce crashes caused by RLR behavior. The RLR prediction is usually composed of two parts: the vehicle’s stop-or-go behavior and the arrival time when the vehicle reaches the stop line. Previous stop-or-go prediction models are usually based on embedded traffic sensors using machine learning algorithms. While based on the continuous trajectories collected by radar sensors, RLR prediction can be conducted more effectively. In this paper, a probabilistic stop-or-go prediction model based on the Bayesian network (BN) is proposed for RLR prediction. We extend the deterministic output into the probabilistic output, which provides decision-makers with greater autonomy. The causality of BN improves the interpretability of the prediction model. The BN model is calibrated and tested by the continuous trajectories data measured by radar sensors installed at a signalized intersection. We not only consider the movement measurements of individual vehicles (e.g., speed and acceleration), but also take into account the car-following behavior. As a comparison, different machine learning models and the model based on the inductive loop detection (ILD) are adopted. The results show that the proposed BN model has a high prediction accuracy and performs better in the feature interpretation. This paper provides a new way for probabilistic RLR prediction based on continuous trajectories, which will significantly improve traffic safety of signalized intersections.
出版年：	2019
期刊名称：	Journal of Intelligent Transportation Systems Technology Planning and Operations
卷：	23
期：	2
页码：	120-132