An Analysis of Risk Factors of Traffic Safety for Heavy Trucks Based on Model Group

To explore the risk factors and occurrence mechanisms behind the traffic accidents of heavy trucks in-depth, a model group, which comprises random forests (RF), Logistic regression (LR), geographically weighted Logistic regression (GWLR), and Bayesian network (BN), is established based on the data of heavy truck accidents in a certain province from 2016 to 2021. This model group allows for examining the impact magnitude, spatial het-erogeneity, as well as the causal pathways leading to accidents of the risk factors. The results reveal that: ①The driv-ing status of heavy trucks, collision patterns, and other eight factors have significant impacts on risks. The impact of rural traffic participants, as well as frontal and side collisions, varies slightly across different models, while the im-pact of rear-end collisions is higher in GWLR compared to that in BN.②When the heavy trucks are engaged in right turns, illegal behaviors or vulnerable road users, the risk of fatal accidents increases by 39.0%, 41.9% and 39.3%, respectively.③With the factor collision patterns serving as a mediator, the causal pathway analysis for the risk of fatal accidents indicates: the factors side collisions and vulnerable road users contribute to the increase of the risk of fatal accident by 64.4% compared to the impact of the factor scrapes involving other vehicles, therefore can be marked as the typical hazardous scenario. Furthermore, when the other participant of heavy truck accidents involves drivers not older than 30 years, the probability of rear-end collisions increases by 10.3% and 26.1% com-pared to those involving drivers aged 30-60 and above 60 respectively. ④Among the risk factors exhibiting spatial heterogeneity, the factor frontal collision exhibits the highest intensity, whereas the factor right turn shows the least. In conclusion, the analytical framework based on the model group can be used to identify the significant risk factors for traffic safety of heavy trucks, and to verify the differences of the impact across models and the notable spatial heterogeneity for these factors.