Evaluation of the Impacts of Speed Variation on Freeway Traffic Collisions in Various Traffic States

Abstract

Objective: To evaluate the impacts of speed variation on the likelihood of traffic collision in various types of traffic states in a freeway section.

Methods: The overall traffic state is divided into free flow (FF), congested traffic (CT), back of queue (BQ), and front of queue (FQ) according to the speed at up- and downstream detector locations. Based on 448 crash recordings from a freeway stretch in California, the logistic regression model is used to estimate the impacts of average speed (AS) and standard deviation of speed (SD) as well as coefficient of speed variation (CSV) for each traffic state separately.

Results: For the overall state, both the SD and CSV are significantly related to the traffic collisions. For the separate states, the model estimates vary in different states: in FF, the CSV is positively related to collisions, whereas the SD is not a good indicator for collisions; in CT and BQ, the SD and CSV have positive impacts on the collision likelihood (especially for rear-end collisions); in FQ, the speed variation indexes were not found to have significant impacts. The coefficients of SD and CSV were found to be different in distinct traffic states. The impact of AS was significant only in FF and BQ.

Conclusions: Considering the overall traffic as one state could hide important relationships between speed variation and collisions. The impacts of speed variation on collision likelihood are different in various traffic states. The SD and CSV are effective surrogate safety measures for traffic collisions in CT and BQ but may not be good measures in FF and FQ. The AS is an accident determinant in FF and BQ but not a contributing factor in other states.

Supplemental materials are available for this article. Go to the publisher's online edition of Traffic Injury Prevention to view the supplemental file.

Keywords:

• safety
• collision
• speed variation
• traffic state
• freeway

Acknowledgments

This research was supported by the National Key Basic Research Program (NKBRP) of China (No. 2012CB725402), the National High-tech R&D Program of China (863 Program) (No. 2012AA112304), the National Natural Science Foundation of China (No. 51008074), as well as the Scientific Research Foundation of Graduate School of Southeast university (No. YBPY1211).