Citation

Das S, Dutta A, Jalayer M, Bibeka A, Wu L. Int. J. Transp. Sci. Technol. 2018; 7(2): 114-123.

Copyright

(Copyright © 2018, Elsevier Publishing)

DOI

10.1016/j.ijtst.2018.02.001

PMID

unavailable

Abstract

Wrong-way driving (WWD) has been a constant traffic safety problem in certain types of roads. These crashes are mostly associated with fatal or severe injuries. This study aims to determine associations between various factors in the WWD crashes. Past studies on WWD crashes used either descriptive statistics or logistic regression to identify the impact of key contributing factors on frequency and/or severity of crashes. Machine learning and data mining approaches are resourceful in determining interesting and non-trivial patterns from complex datasets. This study employed association rules 'Eclat' algorithm to determine the interactions between different factors that result in WWD crashes. This study analyzed five years (2010-2014) of Louisiana WWD crash data to perform the analysis. A broad definition of WWD crashes (both freeway exit ramp WWD crashes and median crossover WWD crashes on low speed roadways) was used in this study. The results of this study confirmed that WWD fatalities are more likely to be associated with head-on collisions. Additionally, fatal WWD crashes tend to be involved with male drivers and off-peak hours. Driver impairment was found as a critical factor among the top twenty rules. Despite several other studies identifying only the WWD contributing factors, this study determined several influencing patterns in WWD crashes. The findings can provide an excellent opportunity for state departments of transportation (DOTs) and local agencies to develop safety strategies and engineering solutions to tackle the issues associated with WWD crashes.

Language: en

Keywords

Association rules mining; Eclat algorithm; Traffic safety; Wrong-way driving crashes