Identifying roadway departure crash patterns on rural two-lane highways under different lighting conditions: Association knowledge using data mining approach

Ahmed Hossain; Xiaoduan Sun; Shahrin Islam; Shah Alam; Md Mahmud Hossain

doi:10.1016/j.jsr.2023.01.006

Identifying roadway departure crash patterns on rural two-lane highways under different lighting conditions: Association knowledge using data mining approach

J Safety Res. 2023 Jun:85:52-65. doi: 10.1016/j.jsr.2023.01.006. Epub 2023 Jan 27.

Authors

Ahmed Hossain¹, Xiaoduan Sun², Shahrin Islam³, Shah Alam⁴, Md Mahmud Hossain⁵

Affiliations

¹ Department of Civil Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA. Electronic address: ahmed.hossain1@louisiana.edu.
² Department of Civil Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA.
³ Department of Civil Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka 1000, Bangladesh.
⁴ Department of Civil Engineering, Rajshahi Science and Technology University, Natore 6400, Rajshahi, Bangladesh.
⁵ Department of Civil and Environmental Engineering, Auburn University, Auburn 36849, AL, USA.

PMID: 37330901
DOI: 10.1016/j.jsr.2023.01.006

Abstract

Introduction: More than half of all fatalities on U.S. highways occur due to roadway departure (RwD) each year. Previous research has explored various risk factors that contribute to RwD crashes, however, a comprehensive investigation considering the effect of lighting conditions has been insufficiently addressed.

Data: Using the Louisiana Department of Transportation and Development crash database, fatal and injury RwD crashes occurring on rural two-lane (R2L) highways between 2008-2017 were analyzed based on daylight and dark (with and without streetlight).

Method: This research employed a safe system approach to explore meaningful complex interactions among multidimensional crash risk factors. To accomplish this, an unsupervised data mining algorithm association rules mining (ARM) was utilized.

Results and conclusions: Based on the generated rules, the findings reveal several interesting crash patterns in the daylight, dark-with-streetlight, and dark-no-streetlight, emphasizing the importance of investigating RwD crash patterns depending on the lighting conditions. In daylight condition, fatal RwD crashes are associated with cloudy weather conditions, distracted drivers, standing water on the roadway, no seat belt use, and construction zones. In dark lighting condition (with and without streetlight), the majority of the RwD crashes are associated with alcohol/drug involvement, young drivers (15-24 years), driver condition (e.g., inattentive, distracted, illness/fatigued/asleep), and colliding with animal(s).

Practical applications: The findings also reveal how certain driver behavior patterns are connected to RwD crashes, such as a strong association between alcohol/drug intoxication and no seat belt usage in the dark-no-streetlight condition. Based on the identified crash patterns and behavioral characteristics under different lighting conditions, the findings could aid researchers and safety specialists in developing the most effective RwD crash mitigation strategies.

Keywords: Construction zones; Dark; Daylight; Lighting conditions; No seat belt use.

Published by Elsevier Ltd.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Accidents, Traffic*
Alcoholic Intoxication*
Ethanol
Humans
Lighting / adverse effects
Risk Factors
Weather

Substances

Ethanol