Citation

Sun M, Zhou R, Jiao C. J. Traffic Transp. Eng. Engl. Ed. 2023; 10(1): 132-142.

Copyright

(Copyright © 2023, Periodical Offices of Chang'an University, Publisher Elsevier Publishing)

DOI

10.1016/j.jtte.2022.07.004

PMID

unavailable

Abstract

Professional drivers are more frequently exposed to longer driving distance and travel time, leading to a higher possibility of safety risk for distraction and fatigue. The widespread and common use of commercial driver monitoring systems (DMS) provides a potential for data collection. It increases the amount of data characterizing driver behavior that can be used for further safety research. This study utilized DMS warning-based data and applied an association rule mining approach to explore risk factors contributing to hazardous materials (HAZMAT) truck driver inattention. A total of 499 HAZMAT truck driver inattentive warning events were used to find rules that will predict the occurrence of driver's fatigue and distraction. First, Fisher's exact tests were performed to examine the association between the frequency of driver inattentive behavior warnings and risk factors. Second, support, confidence, and lift values were used as measurements to quantify the relative strength of the association rules generated by the Apriori algorithm.

RESULTS show that speed between 40 and 49 km/h, relatively longer travel time (3-6 h), freeway, tangent section, off-peak hour and clear weather condition are found to be highly associated with fatigue driving, while nighttime during 18:00 to 23:59, speed between 70 and 80 km/h, travel time between 1 and 3 h, freeways, acceleration less than 0.5 m/s2, visibility greater than 1000 m, and tangent roadway section are found to be highly associated with distracted driving. By focusing on the specific feature groups, these association rules would help in the development of mitigating distraction and fatigue driving countermeasures and enforcement approaches.

Language: en

Keywords

Association rule mining; Distracted driving; DMS warning-based data; Fatigue driving; HAZMAT truck driver; Traffic safety