Abstract
The present study utilized a random parameter logit (RPL) model to explore the nonlinear relationship between explanatory variables and the likelihood of expressway crash severity. The potential unobserved heterogeneity of data brought by China’s road traffic characteristics was fully considered. A total of 1154 crashes happened on Hang-Jin-Qu Expressway from 2013 to 2018 were analyzed. In addition to the conventional impact factors considered in the past, variables related to road geometry were also introduced, which contributed to expressway accidents significantly. The overall stability of the model estimation was examined by likelihood ratio test. Then, the average elastic coefficient of the significant factors at each severity level was also calculated. Several factors that significantly increase the fatal crash probability were highlighted: rainy/snowy/cloudy weather condition, low visibility (100– m), night without light, wet-skid road surface, being female, aged 41+ years, collision with a rigid barrier and some other obstacles, radius and length of horizontal curve, and longitudinal gradient. The parameters of four factors were random and obeyed normal distribution: night without light, being female, driving experience with 10 + years and with large vehicle responsible. These findings provide insights for better understanding of expressway crash severity. Some countermeasures were proposed about driver education, traffic law enforcement, vehicle and road design, environmental improvement, and so on.
Highlights
The high mortality rate caused by road traffic accidents has imposed heavy economic and emotional burdens to the family and society
According to the testing results for multicollinearity with Variance Inflation Factors (VIF), the remaining variables were all treated as independent variables
To test the fitness of the random parameter logit (RPL) model, a fixed parameter MNL model was constructed for comparison
Summary
The high mortality rate caused by road traffic accidents has imposed heavy economic and emotional burdens to the family and society. Injury severity conditional on crash occurrence can depend on numerous factors all of which are most certainly not observed in crash databases. These unobserved factors can moderate the influence of other observed covariates in the model leading to variation in the parameter effects across different observations. These unobserved variations are referred to as ‘‘unobserved heterogeneity,’’ which is of considerable importance in injury severity analysis. The current study intents to optimize the traditional discrete choice models with fixed parameters into the RPL model with some random parameters for quantitative analysis of traffic accidents by considering the unobserved heterogeneity among predictor variables. Some corresponding improvement strategies are proposed based on the findings
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