考虑时间不稳定性的夜间行人-机动车事故严重程度致因分析OA北大核心

Nighttime pedestrian-vehicle crashes exhibit significantly higher injury severity than daytime crashes due to visibility limitations and other factors.To accurately identify influencing factors,this study develops a hybrid model integrating a random parameters Logit model with heterogeneity in means and variances and a random forest(RF)algorithm based on SHapley Additive exPlanation(SHAP),i.e.RF-SHAP,using crash data from 2017 to 2022.The log-likelihood ratio test confirms temporal instability in the dataset,necessitating separate models for 2017-2019,2020,2021,and 2022 with calculated average marginal effects for significant variables.Results demonstrate that random effects exist for drinking pedestrians(2017-2019),ambulance required(2020),local street crashes(2021),and 48-56 km/h speed limits(2022),with their mean/variance influenced by traffic control and road classi-fication.Drinking pedestrians,pedestrians aged over 45 to 60 years,driver injuries,vehicle types(pickup trucks and trucks),divided roadways,speed limits(32-40,48-56,64-72 km/h),weekends,and winter conditions have be-gun to exhibit statistically significant effects on nighttime pedestrian-vehicle crashes in recent years.In addition,the RF-SHAP algorithm quantifies heterogeneous contributions of all sub-variables within four random parameters to crash severity.Policy implications highlight three priorities:addressing pedestrian drinking behavior,enhancing nighttime crash prevention on expressways and arterial routes,and establishing appropriate speed limits while avoid-ing excessively high or low values.