Abstract
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.
Highlights
According to the World Health Organization (WHO) report in 2018 on road safety, Iran has a higher rate of road traffic fatalities per person than the global average [1]
Accident risk was mapped with bagged decision trees (BDTs), extra trees (ETs), and random forest (RF) algorithms on Iranian roads in different circadian alertness situations
The performance of created risk models was cross-validated by different metrics
Summary
According to the World Health Organization (WHO) report in 2018 on road safety, Iran has a higher rate of road traffic fatalities per person than the global average [1]. Road traffic injuries have always been one of the leading causes of Iranians’ deaths [2]. In 2020, from March 2019 to March 2020, 159,735 road traffic accidents (RTA) occurred in. The high rate of RTA in Iran, along with significant economic damage and loss of life [4,5], necessitates research into the problem. Because RTA causes significant financial damage to countries and leads to many people’s death every year [6], its modeling has always been a hot topic.
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