Integrating Traffic Conflict Frequency and Severity Indicators to Estimate Pedestrian Safety Performance Functions for Signalized Intersections

Assessing intersection safety for pedestrians based on crashes can be challenging because of the paucity of crashes. This is why surrogate measures of safety, typically traffic conflicts, have been used as an alternative approach for such assessments. Insufficient sample size has also presented a challenge in developing conventional safety performance functions (SPFs) for estimating pedestrian crash frequency in a variety of Highway Safety Manual applications. This challenge can also be resolved with the surrogate measures approach, in that these measures can logically capture the effects of geometric and operational variables that influence safety, thereby providing a useful complementary approach for developing SPFs, especially where sample sizes are small. This paper develops SPFs for pedestrian crashes at signalized intersections based on traffic conflicts, and in the process, it advances the methodology for this approach. Machine learning was used to develop a data-driven safety index that integrates frequency and severity indicators. This index was developed to classify conflicts into groups using a database of pedestrian conflicts derived from video observations at 44 intersections in five Canadian cities. The frequencies of conflicts in these groups, and crashes at these intersections, were then utilized to estimate SPFs. The results are promising in that they demonstrate the potential of using machine learning to estimate SPFs using surrogate measures. The approach is especially important given the focus on pedestrian crashes in Vision Zero plans and the reality that crash samples are typically too small for estimating pedestrian SPFs that directly capture the effects of multiple variables.