Investigating the factors affecting secondary crash frequency caused by one primary crash using zero-inflated ordered probit regression

Abstract

The contributing factors to secondary crashes have been investigated by a number of studies in recent years. However, previous studies generally considered only the first secondary crash after the primary crash. Existing studies have seldom considered the multiple secondary crashes caused by one primary crash and the effects of real-time traffic flow conditions. This paper aimed to investigate the effects of real-time traffic flow conditions on the frequency of secondary crashes caused by one primary crash on freeways. The zero-inflated ordered probit (ZIOP) regression model was developed to link the probability of multiple secondary crashes after the occurrence of one primary crash with real-time traffic flow, geometric, weather and primary crash characteristics. The ZIOP regression model analyzed the probability of secondary crash frequency after one primary crash by separating it into two states. One is a secondary-crash-free state that determines whether the occurrence of a crash will lead to one or more secondary crashes, and the other is a secondary-crash-prone state that determines the secondary crash frequency caused by one primary crash. The average speed, average traffic volume, and the difference between the numbers of on-ramp and off-ramp are the significant variables in the secondary-crash-free state. In the secondary-crash-prone state, the significant variables affecting the probability of multiple secondary crashes include average detector occupancy, rainy weather, primary crash severity, and hit-and-run primary crash. The ROC curves were used to test predictive performance of the ZIOP model. The test results suggested that the ZIOP model provide reasonably good predictive accuracy of multiple secondary crashes caused by one primary crash.