Evaluation of Tree-Based Machine Learning Algorithms for Accident Risk Mapping Caused by Driver Lack of Alertness at a National Scale

• Published in: Sustainability 2021-09, Vol.13 (18), p.10239
• Main Authors: Farhangi, Farbod, Sadeghi-Niaraki, Abolghasem, Razavi-Termeh, Seyed Vahid, Choi, Soo-Mi
• Format: Article
• Language: English
• Subjects: Accidents; Alertness; Algorithms; Big Data; Decision trees; Drowsiness; Emergency services; Fatalities; Geographic information systems; Land use; Learning algorithms; Machine learning; Mapping; Modelling; Risk; Roads; Roads & highways; Service stations; Sustainability; Time factors; Traffic accidents; Traffic accidents & safety; Traffic models; Traffic volume
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su131810239

Drivers’ lack of alertness is one of the main reasons for fatal road traffic accidents (RTA) in Iran. Accident-risk mapping with machine learning algorithms in the geographic information system (GIS) platform is a suitable approach for investigating the occurrence risk of these accidents by analyzing the role of effective factors. This approach helps to identify the high-risk areas even in unnoticed and remote places and prioritizes accident-prone locations. This paper aimed to evaluate tuned machine learning algorithms of bagged decision trees (BDTs), extra trees (ETs), and random forest (RF) in accident-risk mapping caused by drivers’ lack of alertness (due to drowsiness, fatigue, and reduced attention) at a national scale of Iran roads. Accident points and eight effective criteria, namely distance to the city, distance to the gas station, land use/cover, road structure, road type, time of day, traffic direction, and slope, were applied in modeling, using GIS. The time factor was utilized to represent drivers’ varied alertness levels. The accident dataset included 4399 RTA records from March 2017 to March 2019. The performance of all models was cross-validated with five-folds and tree metrics of mean absolute error, mean squared error, and area under the curve of the receiver operating characteristic (ROC-AUC). The results of cross-validation showed that BDT and RF performance with an AUC of 0.846 were slightly more accurate than ET with an AUC of 0.827. The importance of modeling features was assessed by using the Gini index, and the results revealed that the road type, distance to the city, distance to the gas station, slope, and time of day were the most important, while land use/cover, traffic direction, and road structure were the least important. The proposed approach can be improved by applying the traffic volume in modeling and helps decision-makers take necessary actions by identifying important factors on road safety.