Analysis of Freeway Secondary Crashes With a Two-Step Method by Loop Detector Data

Abstract

A two-step identification method of secondary crashes (SCs) is developed in this paper, and the effects of traffic variables on the SC risks on freeways are captured. Crash-related data were obtained from the I-880 freeway in California, USA. Combined with the speed contour map, the SCs were identified by the real shock wave speed index and the SC identification index. The random effect logit regression was applied to analyze the significantly contributing factors and their mechanism on the SCs. The results showed that the significantly contributing factors are different between the initial SC model and SC models (Threshold = 20mile/h, 15mile/h, 10mile/h, 5mile/h), except for the variable of the standard deviation of 30-s vehicle speed during 5-10 min. In the SC models, the number of significant contributing factors increases with the increase in the threshold value. The results of the elasticity analysis showed that the elasticity values of the hit object crash, wet surface road, the average 30-s vehicle speed during 5-10 min, and the average 30-s vehicle count during 5-10 min are greater than 10%.

Keywords

• Freeways
• traffic flow
• secondary crash
• identification method
• random effect logit regression