A new spatiotemporal convolutional neural network model for short-term crash prediction  

作　　者：Bowen CAI Léah CAMARCAT Wen-long SHANG Mohammed QUDDUS 

机构地区：[1]Department of Civil and Environmental Engineering,Imperial College London,London SW72AZ,UK

出　　处：《Frontiers of Engineering Management》2025年第1期86-98,共13页工程管理前沿(英文版)

摘　　要：Predicting short-term traffic crashes is chal-lenging due to an imbalanced data set characterized by excessive zeros in noncrash counts,random crash occur-rences,spatiotemporal correlation in crash counts,and inherent heterogeneity.Existing models struggle to effec-tively address these distinct characteristics in crash data.This paper proposes a new joint model by combining the time-series generalized regression neural network(TGRNN)model and the binomially weighted convolutional neural network(BWCNN)model.The joint model aims to capture all these characteristics in short-term crash predic-tion.The model was trained and tested using real-world,highly disaggregated traffic data collected with inductive loop detectors on the M1 motorway in the UK in 2019,along with crash data extracted from the UK National Accident Database for the same year.The short-term is defined as a 30-min interval,providing sufficient time for a traffic control center to implement interventions and mitigate potential hazards.The year was segmented into 30-min intervals,resulting in a highly imbalanced data set with over 99.99%noncrash samples.The joint model was applied to predict the probability of a crash occurrence by updating both the crash and traffic data every 30 min.The findings revealed that 75.3%of crashes and 81.6%of noncrash events were correctly predicted in the southbound direction.In the northbound direction,78.1%of crashes and 80.2%of noncrash events were accurately captured.Causal analysis and model-based interpretation were used to analyze the relative importance of explanatory variables regarding their contribution to crashes.The results reveal that speed variance and speed are the most influential factors contributing to crash occurrence.

关 键 词：safety management crash prediction generalized regression neural network binomial weighted CNN variable importance 

分 类 号：U49[交通运输工程—交通运输规划与管理]