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Accid Anal Prev. 2018 Apr;113:149-158. doi: 10.1016/j.aap.2018.01.033. Epub 2018 Mar 7.

Assessing rear-end collision risk of cars and heavy vehicles on freeways using a surrogate safety measure.

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PARSONS, 625 Cochrane Drive, Suite 500, Markham, ON, L3R 9R9, Canada. Electronic address:
Department of Civil and Environmental Engineering, University of Windsor, ON, N9B 3P4, Canada. Electronic address:


This study analyzes rear-end collision risk of cars and heavy vehicles on freeways using a surrogate safety measure. The crash potential index (CPI) was modified to reflect driver's reaction time and estimated by types of lead and following vehicles (car or heavy vehicle). CPIs were estimated using the individual vehicle trajectory data from a segment of the US-101 freeway in Los Angeles, U.S.A. It was found that the CPI was generally higher for the following heavy vehicle than the following car due to heavy vehicle's lower braking capability. This study also validates the CPI using the simulated traffic data which replicate the observed traffic conditions a few minutes before the crash time upstream and downstream of the crash locations. The observed data were obtained from crash records and loop detectors on a section of the Gardiner Expressway in Toronto, Canada. The result shows that the values of CPI were consistently higher during the traffic conditions immediately before the crash time (crash case) than the normal traffic conditions (non-crash case). This demonstrates that the CPI can be used to capture rear-end collision risk during car-following maneuver on freeways. The result also shows that rear-end collision risk is lower for heavy vehicles than cars in the crash case due to their shorter reaction time and lower speed when spacing is shorter. Thus, it is important to reflect the differences in driver behavior and vehicle performance characteristics between cars and heavy vehicles in estimating surrogate safety measures. Lastly, it was found that the CPI-based crash prediction model can correctly identify the crash and non-crash cases at higher accuracy than the other crash prediction models based on detectors.


Car-following behavior; Collision risk; Crash potential index; Heavy vehicle; Rear-end collision; Surrogate safety measure

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