ABSTRACT
In this paper, the study area was divided into plains, mountainous areas and hilly areas, and the time was further divided into working days and rest days. The cause and severity of freeway traffic crash under different space-time conditions were studied, respectively. From the selection of vehicle, road and environment, three dimensions involved in vehicles, roadside protective facilities type, lighting conditions, a total of 13 variables for working day and rest day crash in plains, respectively applied sequential Logit model and the multinomial Logit model to construct the freeway traffic crash severity model, the calibration model using maximum likelihood estimation method, Likelihood ratio test, Pearson statistics, deviation statistics and information criterion statistics were used to test the fitting effect of the models, and the accuracy of prediction of the two models was compared. The results show that there are different risk factors for freeway traffic crash in different time conditions, and different models have their own advantages and disadvantages for analyzing crash in different time conditions and different severity levels. The results show that the ordered Logit model is more suitable for the analysis of the severity of traffic crashes on the plain freeway, and can reveal the differences in the causes and severity of the crash in different time conditions.
