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Journal of Advanced Transportation
Volume 2017, Article ID 1208170, 11 pages
Research Article

Effects of Human-Centered Factors on Crash Injury Severities

1Alabama Transportation Institute, The University of Alabama, Tuscaloosa, AL, USA
2Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL, USA

Correspondence should be addressed to Emmanuel Kofi Adanu;

Received 2 June 2017; Revised 20 November 2017; Accepted 29 November 2017; Published 20 December 2017

Academic Editor: Zhi-Chun Li

Copyright © 2017 Emmanuel Kofi Adanu and Steven Jones. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Factors related to drivers and their driving habits dominate the causation of traffic crashes. An in-depth understanding of the human factors that influence risky driving could be of particular importance to facilitate the application of effective countermeasures. This paper sought to investigate effects of human-centered crash contributing factors on crash outcomes. To select the methodology that best accounts for unobserved heterogeneity between crash outcomes, latent class (LC) logit model and random parameters logit (RPL) model were developed. Model estimation results generally show that serious injury crashes were more likely to involve unemployed drivers, no seatbelt use, old drivers, fatigued driving, and drivers with no valid license. Comparison of model fit statistics shows that the LC logit model outperformed the RPL model, as an alternative to the traditional multinomial logit (MNL) model.