In order to accurately analyse the influencing factors of road traffic accident severity, two statistical models, the generalized ordered logit (GOL) model and the generalized ordered probit (GOP) model, are introduced. The model with the best fit is selected based on the goodness of fit test, and subsequently used to study the influence of human factors, vehicles, roads and environment on the severity of road traffic accidents. The results of the analysis of 1445 road traffic accident samples from 2011 to 2020 in a city in Shaanxi Province indicate that both the GOL model and the GOP model are suitable for analysing the influencing factors of road traffic accident severity. The GOL model demonstrates a superior fit to the city’s data compared to the GOP model. A total of 15 variables, including age and driving experience, are found to be significantly correlated with the severity of road traffic accidents. Furthermore, elasticity analysis reveals that eight factors, such as agricultural transport vehicles and the absence of physical separation facilities, are identified as significant contributors to the probability of fatal accidents.


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    Title :

    Analysis of influencing factors of road traffic accident severity based on generalized ordered model


    Contributors:

    Conference:

    International Conference on Smart Transportation and City Engineering (STCE 2024) ; 2024 ; Chongqing, China


    Published in:

    Proc. SPIE ; 13575


    Publication date :

    2025-04-28





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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