Inter-Vehicle Traffic Accident Severity Analysis Based on Random Parameter Logit Model

To compensate for the fixed-parameter model's tendency to lead to biased estimates and erroneous inferences of results, a sample of 2016 car-vehicle traffic accident data in France was used, with accident severity as the dependent variable and 12 factors such as people, roads, environment, and accident characteristics as independent variables, based on a random parameter logit model using a backward stepwise selection method, while using average marginal effects. The results showed that: ① the variables “Passenger ≥ 2”, “Curve road”, “One-way road”, “Two-way physically separated road, “No public lighting at night”, “Severe weather”, “Urban area”, “head-on collision “ as random variables; ② the random parameter Logit model fits better compared to the ordinary logit model, which can effectively capture the unobserved heterogeneity in accident data and is more suitable for traffic accident analysis.