To examine the difference in contributing factors of rear-end crashes of different injury severity involving different types of vehicles, this paper proposed random-parameters multinomial logit models with heterogeneity in means and variances. A three-year (2017–2019) rear-end crash data collected from Beijing-Shanghai Highways in China was used to calibrate the models. The rear-end crashes were classified as five types (Car-Car, Car-Truck, Truck-Truck, Truck-Car, Others). With two possible injury severity outcomes of medium/severe injury and light injury, a wide range of possible variables including crash, traffic, speed, geometric, and sight characteristics were considered in this study. Likelihood ratio tests revealed the rationality of adopting merged models using the data across three-year periods. Remarkably significant differences were shown in crashes involving different types of vehicles. The results accounting for the possible heterogeneity could be of value to roadway designers and traffic management departments seeking to promote highway safety and raise accurate safety countermeasures.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Analysis of injury severity in rear-end crashes on an expressway involving different types of vehicles using random-parameters logit models with heterogeneity in means and variances


    Contributors:

    Published in:

    Transportation Letters ; 15 , 7 ; 742-753


    Publication date :

    2023-08-09


    Size :

    12 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown







    Injury severity prediction of cyclist crashes using random forests and random parameters logit models

    Scarano, Antonella / Rella Riccardi, Maria / Mauriello, Filomena et al. | Elsevier | 2023