Abstract
Road traffic safety is an essential component of public safety and a globally significant issue. Pedestrians, as crucial participants in traffic activities, have always been a primary focus with regard to traffic safety. In the context of the rapid advancement of intelligent transportation systems (ITS), it is crucial to explore effective strategies for preventing pedestrian fatalities in pedestrian-vehicle crashes. This paper aims to investigate the factors that influence pedestrian injury severity based on pedestrian-involved crash data collected from several sensor-based sources. To achieve this, a hybrid approach of a random parameters logit model and random forest based on the SHAP method is proposed. Specifically, the random parameters logit model is utilized to uncover significant factors and the random variability of parameters, while the random forest based on SHAP is employed to identify important influencing factors and feature contributions. The results indicate that the hybrid approach can not only verify itself but also complement more conclusions. Eight significant influencing factors were identified, with seven of the factors identified as important by the random forest analysis. However, it was found that the factors "Workday or not" (Not), "Signal control mode" (No signal and Other security facilities), and "Road safety attribute" (Normal Road) are not considered significant. It is important to note that focusing solely on either significant or important factors may lead to overlooking certain conclusions. The proposed strategies for ITS have the potential to significantly improve pedestrian safety levels.