Random Parameter-Enabled Discrete Choice Model Development to Identify the Impact and Unobserved Heterogeneity among Contributing Factors to Animal-vehicle Collisions
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2022
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Safety is one of the most important characteristics of transportation networks. It can be simply defined as arriving at the destination with no injuries, injuries and fatality. Analysis of the severity and the risk factors are used for influencing highway and vehicle designs, safety management on major roads of the transport network, and directing and implementing a wide variety of regulatory policies on injury prevention. Vehicle- animal collisions account for a large proportion of traffic accidents that occur every year. An estimated 1 million to 2 million crashes between motor vehicles and large animals such as deer occur every year in the U.S., causing approximately 200 human deaths, 26,000 injuries, and at least $8 billion in property damage and other costs. In rural states such as Wyoming, wildlife-vehicle crashes represent almost 20% of reported collisions. Therefore, the study of vehicle-animal accidents is of great significance to the analysis of traffic safety. There are many different models that we can use when analyzing traffic safety data. In this article, I used the multinomial logit (MNL) model for the analysis, and then I used the Mixed MNL method to get another result. The two results are analyzed and compared, and the final conclusion is drawn.
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Civil engineering
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62 pages
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