Citation

Omranian E, Sharif H, Dessouky S, Weissmann J. Accid. Anal. Prev. 2018; 117: 10-20.

Affiliation

Department of Civil and Environmental Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, United States. Electronic address: jweissmann@utsa.edu.

Copyright

(Copyright © 2018, Elsevier Publishing)

DOI

10.1016/j.aap.2018.03.030

PMID

29626543

Abstract

Interaction between adverse weather conditions and motor vehicle crashes is an important topic for traffic engineers and hydrometeorologists. With the recent availability of high resolution precipitation products (hourly, 4 × 4 km), it is possible to evaluate crash risk during rainfall events more accurately. Texas, the second largest state in the U.S., with a relatively high population density in its eastern part that receives significant rainfall from tropical events, experiences many hazardous traffic conditions every year. This study investigates temporal and spatial variability of the Relative Accident Risk (RAR) due to rainy conditions across Texas during the year 2015 using a Crash-Based Matched Pairs Analysis (CB-MPA) approach for every 4 × 4 km grid using an hourly time scale. The overall findings show that rainfall increases crash risk across the state by about 57%, while seasonal-based analysis confirms the role of precipitation patterns on crash rates. Although eastern and central counties (wetter and more urbanized) have remarkably higher rates of crash occurrence, the western counties (mainly rural and dry) show higher RAR values. Moreover, higher rainfall intensity can increase RAR up to three-fold while directly having an adverse effect on crash injury type. There is a relatively high correlation between rainfall intensity and RAR values (R² = 0.76). The analysis also shows higher RAR values on high-speed interstate highways and tollways compared to urban local streets. RAR values also vary according to the gender and age of drivers. The study findings shed light on future paths toward more detailed applications of high-resolution environmental data in crash risk analysis.

Copyright © 2018 Elsevier Ltd. All rights reserved.

Language: en

Keywords

Accident risk; Crash-based matched pair analysis; Injury severity; Radar rainfall; Road safety; Weather effect