Duo Li1, Prakash Ranjitkar2, Yifei Zhao1, Hui Yi1, Soroush Rashidi3. 1. a Highway School, Chang'an University , Xi'an , Shaanxi , China. 2. b Department of Civil & Environmental Engineering , University of Auckland , Auckland , New Zealand. 3. c Opus International Consultants Ltd. , The West Haven , Auckland , New Zealand.
Abstract
OBJECTIVE: Pedestrians are the most vulnerable road users due to the lack of mass, speed, and protection compared to other types of road users. Adverse weather conditions may reduce road friction and visibility and thus increase crash risk. There is limited evidence and considerable discrepancy with regard to impacts of weather conditions on injury severity in the literature. This article investigated factors affecting pedestrian injury severity level under different weather conditions based on a publicly available accident database in Great Britain. METHOD: Accident data from Great Britain that are publicly available through the STATS19 database were analyzed. Factors associated with pedestrian, driver, and environment were investigated using a novel approach that combines a classification and regression tree with random forest approach. RESULTS: Significant severity predictors under fine weather conditions from the models included speed limits, pedestrian age, light conditions, and vehicle maneuver. Under adverse weather conditions, the significant predictors were pedestrian age, vehicle maneuver, and speed limit. CONCLUSIONS: Elderly pedestrians are associated with higher pedestrian injury severities. Higher speed limits increase pedestrian injury severity. Based on the research findings, recommendations are provided to improve pedestrian safety.
OBJECTIVE: Pedestrians are the most vulnerable road users due to the lack of mass, speed, and protection compared to other types of road users. Adverse weather conditions may reduce road friction and visibility and thus increase crash risk. There is limited evidence and considerable discrepancy with regard to impacts of weather conditions on injury severity in the literature. This article investigated factors affecting pedestrian injury severity level under different weather conditions based on a publicly available accident database in Great Britain. METHOD: Accident data from Great Britain that are publicly available through the STATS19 database were analyzed. Factors associated with pedestrian, driver, and environment were investigated using a novel approach that combines a classification and regression tree with random forest approach. RESULTS: Significant severity predictors under fine weather conditions from the models included speed limits, pedestrian age, light conditions, and vehicle maneuver. Under adverse weather conditions, the significant predictors were pedestrian age, vehicle maneuver, and speed limit. CONCLUSIONS: Elderly pedestrians are associated with higher pedestrian injury severities. Higher speed limits increase pedestrian injury severity. Based on the research findings, recommendations are provided to improve pedestrian safety.
Entities:
Keywords:
Injury severity; classification and regression trees (CART); random forests; weather