title: Modeling secondary accidents identified by traffic shock waves.
abstract: The high potential for occurrence and the negative consequences of secondary accidents make them an issue of great concern affecting freeway safety. Using accident records from a three-year period together with California interstate freeway loop data, a dynamic method for more accurate classification based on the traffic shock wave detecting method was used to identify secondary accidents. Spatio-temporal gaps between the primary and secondary accident were proven be fit via a mixture of Weibull and normal distribution. A logistic regression model was developed to investigate major factors contributing to secondary accident occurrence. Traffic shock wave speed and volume at the occurrence of a primary accident were explicitly considered in the model, as a secondary accident is defined as an accident that occurs within the spatio-temporal impact scope of the primary accident. Results show that the shock waves originating in the wake of a primary accident have a more significant impact on the likelihood of a secondary accident occurrence than the effects of traffic volume. Primary accidents with long durations can significantly increase the possibility of secondary accidents. Unsafe speed and weather are other factors contributing to secondary crash occurrence. It is strongly suggested that when police or rescue personnel arrive at the scene of an accident, they should not suddenly block, decrease, or unblock the traffic flow, but instead endeavor to control traffic in a smooth and controlled manner. Also it is important to reduce accident processing time to reduce the risk of secondary accident.
authors.affiliation: School of Transportation Engineering, Tongji University, Shanghai 201804, China.
authors.forename: Wang
authors.initials: W
authors.lastname: Junhua
authors.affiliation: School of Transportation Engineering, Tongji University, Shanghai 201804, China.
authors.forename: Liu
authors.initials: L
authors.lastname: Boya
authors.affiliation: School of Transportation Engineering, Tongji University, Shanghai 201804, China. Electronic address: zlftongji@163.com.
authors.forename: Zhang
authors.initials: Z
authors.lastname: Lanfang
authors.affiliation: Safe Transportation Research and Education Center, University of California, Berkeley, CA 94720, United States.
authors.forename: David R
authors.initials: DR
authors.lastname: Ragland
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D002140:California; D019220:High-Energy Shock Waves; D006801:Humans; D016015:Logistic Models; D011336:Probability; D018570:Risk Assessment / Q000706:statistics & numerical data; D014887:Weather
keywords: Incident management; Logistic regression; Secondary accident; Traffic shock wave