title: Predicting crash likelihood and severity on freeways with real-time loop detector data.
abstract: Real-time crash risk prediction using traffic data collected from loop detector stations is useful in dynamic safety management systems aimed at improving traffic safety through application of proactive safety countermeasures. The major drawback of most of the existing studies is that they focus on the crash risk without consideration of crash severity. This paper presents an effort to develop a model that predicts the crash likelihood at different levels of severity with a particular focus on severe crashes. The crash data and traffic data used in this study were collected on the I-880 freeway in California, United States. This study considers three levels of crash severity: fatal/incapacitating injury crashes (KA), non-incapacitating/possible injury crashes (BC), and property-damage-only crashes (PDO). The sequential logit model was used to link the likelihood of crash occurrences at different severity levels to various traffic flow characteristics derived from detector data. The elasticity analysis was conducted to evaluate the effect of the traffic flow variables on the likelihood of crash and its severity.The results show that the traffic flow characteristics contributing to crash likelihood were quite different at different levels of severity. The PDO crashes were more likely to occur under congested traffic flow conditions with highly variable speed and frequent lane changes, while the KA and BC crashes were more likely to occur under less congested traffic flow conditions. High speed, coupled with a large speed difference between adjacent lanes under uncongested traffic conditions, was found to increase the likelihood of severe crashes (KA). This study applied the 20-fold cross-validation method to estimate the prediction performance of the developed models. The validation results show that the model's crash prediction performance at each severity level was satisfactory. The findings of this study can be used to predict the probabilities of crash at different severity levels, which is valuable knowledge in the pursuit of reducing the risk of severe crashes through the use of dynamic safety management systems on freeways.
authors: School of Transportation, Southeast University, Si Pai Lou #2, Nanjing 210096, China. iamxcc1@gmail.com
authors: Chengcheng
authors: C
authors: Xu
authors: Andrew P
authors: AP
authors: Tarko
authors: Wei
authors: W
authors: Wang
authors: Pan
authors: P
authors: Liu
mesh_terms: D000063:Accidents, Traffic; D001334:Automobile Driving / Q000706:statistics & numerical data; D003199:Computer Systems; D004779:Environment Design; D006801:Humans; D016015:Logistic Models; D018570:Risk Assessment; D017751:Safety Management; D012680:Sensitivity and Specificity