Citation

Liu J, Boyle LN, Banerjee AG. Accid. Anal. Prev. 2018; 120: 211-218.

Affiliation

Industrial & Systems Engineering, University of Washington, Seattle, WA 98195, USA; Mechanical Engineering, University of Washington, Seattle, WA 98195, USA.

Copyright

(Copyright © 2018, Elsevier Publishing)

DOI

10.1016/j.aap.2018.06.005

PMID

30170295

Abstract

Ensuring safe operations of large commercial vehicles (motor carriers) remains an important challenge, particularly in the United States. While the federal regulatory agency has instituted a compliance review-based rating method to encourage carriers to improve their safety levels, concerns have been expressed regarding the effectiveness of the current ratings. In this paper, we consider a crash rate level (high, medium, and low) rather than a compliance review-based rating (satisfactory, conditional satisfactory, and unsatisfactory). We demonstrate an automated way of predicting the crash rate levels for each carrier using three different classification models (Artificial Neural Network, Classification and Regression Tree (CART), and Support Vector Machine) and three separate variable selection methods (Empirical Evidence, Multiple Factor Analysis, Garson's algorithm). The predicted crash rate levels (high, low) are compared to the assigned levels based on the current safety rating method. The results indicate the feasibility of crash rate level as an effective measure of carrier safety, with CART having the best performance.

Copyright © 2018 Elsevier Ltd. All rights reserved.

Language: en

Keywords

Classification and Regression Tree; Crash rate; Motor carrier safety; Variable selection