TY  - JOUR
PY  - 2017//
TI  - Analyzing traffic crash severity in work zones under different light conditions
JO  - Journal of advanced transportation
A1  - Wei, Xinxin
A1  - Shu, Xiang
A1  - Huang, Baoshan
A1  - Taylor, Edward L.
A1  - Chen, Huaxin
SP  - ID 5783696
EP  - ID 5783696
VL  - 2017
IS  - 
N2  - Previous studies have investigated various factors that contribute to the severity of work zone crashes. However, little has been done on the specific effects of light conditions. Using the data from the Enhanced Tennessee Roadway Information Management System (E-TRIMS), crashes that occurred in the Tennessee work zones during 2003-2015 are categorized into three light conditions: daylight, dark-lighted, and dark-not-lighted. One commonly used decision tree method--Classification and Regression Trees (CART)--is adopted to investigate the factors contributing to crash severity in highway work zones under these light conditions. The outcomes from the three decision trees with differing light conditions show significant differences in the ranking and importance of the factors considered in the study, thereby indicating the necessity of examining traffic crashes according to light conditions. By separately considering the crash characteristics under different light conditions, some new findings are obtained from this study. The study shows that an increase in the number of lanes increases the crash severity level in work zones during the day while decreasing the severity at night. Similarly, drugs and alcohol are found to increase the severity level significantly under the dark-not-lighted condition, while they have a limited influence under daylight and dark-lighted conditions.<p />  <p>Language: en</p>
LA  - en
SN  - 0197-6729
UR  - http://dx.doi.org/10.1155/2017/5783696
ID  - ref1
ER  -