Citation

Mahmud A, Sengupta A, Gayah VV. Transp. Lett. 2024; 16(3): 207-217.

Copyright

(Copyright © 2024, Maney Publishing, Publisher Informa - Taylor and Francis Group)

DOI

10.1080/19427867.2023.2175419

PMID

unavailable

Abstract

Traffic crashes vary in the manner in which the collision occurs (collision type), and countermeasures to reduce crashes might vary significantly based on this collision type. The inherent complexity in their mechanism has motivated this study to identify significant factors influencing collision types, with the goal of better countermeasure deployment. The objective of this work is to compare the performances of statistical and machine learning (ML) models in classifying crashes based on collision type, and assess their generalizability and interpretability. Discrete choice models, Bayesian classifiers, tree-based algorithms, and support vector machines are among the data-driven methods considered for comparison.

RESULTS indicate that tree-based algorithms perform consistently well and offer a higher interpretability, with out-of-distribution robustness. However, while ML models provide a flexible framework for modeling large data volumes, statistical models provide additional interpretability on the effect of critical variables on crash mechanisms - which is relevant from a safety management standpoint.

Language: en

Keywords

crash classification; crash mechanism; machine learning; Statistical model; transferability