Citation

Guadamuz R, Aguero-Valverde J. J. Transp. Saf. Secur. 2021; 13(6): 680-693.

Copyright

(Copyright © 2021, Southeastern Transportation Center, and Beijing Jiaotong University, Publisher Informa - Taylor and Francis Group)

DOI

10.1080/19439962.2019.1667932

PMID

unavailable

Abstract

Injury severity is a key aspect in road safety, especially at railway crossings. Traditional statistical methods such as ordered probit or ordered logit models provide adequate approaches to these analyses when plenty of covariates are available. However, these traditional approaches can be combined with other techniques, for instance, spatial methods and Bayesian analyses to provide better estimations by including the effects of covariates that are not explicitly included in the models, among other benefits. Bayesian analyses are useful when low crash frequencies and small sample sizes are present in the data, which is common for railway crashes. A case from San José, Costa Rica is used to demonstrate the use of the proposed methodology. Spatial analyses were implemented through conditional autoregressive (CAR) models up to third-ordered neighbors. A Bayesian approach was used to obtain more accurate estimations for the model coefficients and parameters. The results show how this methodology provides better overall results than a non-spatial ordered probit model. The first-order CAR model was determined to be the best overall model and therefore is preferred.

Language: en

Keywords

Bayesian; injury severity; ordered probit; railway crossings; spatial methods