UC Berkeley

The crashes that cause fatalities and serious injuries typically occur at high speeds. Although the Federal Highway Administration (FHWA) asserts a need to identify the most dangerous locations on a roadway system, FHWA promotes a screening practice that fails to reveal these high-speed (high-risk) locations. In fact, the current practice obscures the most dangerous locations by conflating high-speed crashes with low-speed (low-risk) ones. The current practice supported by FHWA has two problems. Firstly, it analyzes crashes in uncongested and congested conditions together, without distinction. Secondly, it uses Vehicle Miles Traveled (VMT) as the measure of traffic exposure which cannot capture the vehicles’ extra time spent traveling in slow-moving congestion. These problems arise when the current practice is applied to freeways that see their fair share of congestion: the current practice tends to mistakenly identify locations with large numbers of low-speed (low-risk) crashes in congestion as if they were the most dangerous locations. The reason is that crash rates inside congestion are much higher than those occurring in uncongested traffic conditions.

To remedy these problems with current practice, the present study analyzes crashes in a disaggregated fashion based upon the vehicle occupancies measured by the nearest freeway loop detector at the time of the crash. High-speed crashes in uncongested conditions are analyzed separately from low-speed crashes in congested conditions. Crash counts for both traffic conditions were normalized by Vehicle Hours Traveled (VHT), rather than the commonly used Vehicle Miles Traveled (VMT), to capture vehicles’ extra exposure inside congestion. The data used for this research came from a 6-month period in 2016 on a 10-mile stretch of the northbound Interstate 880 freeway in Alameda County, California. Crash records collected by state police and traffic data measured by the site's loop detectors were also used as inputs.

The results show that by using the proposed method, one can better identifies the type of crashes, (uncongested and/or congested), that make some locations stand out as problematic. From there analysts can narrow down the set of these outliers to be the ones driven predominantly by uncongested crashes and are therefore the ones of the greatest safety concern. Additionally, one can rank uncongested outliers based on their magnitudes and focus on the highest ranked ones. For the reasons mentioned above, the proposed approach allows traffic agencies to focus their resources on these most dangerous locations and help improve the overall safety on the transportation system.

The amount of data used for this study is limited. But the findings are very promising. Future research to advance the proposed method and to answer questions that are yet unresolved are discussed.