Drivers’ Behavior and Traffic Accident Analysis Using Decision Tree Method

Pires Abdullah () and Tibor Sipos
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Pires Abdullah: Department of Transport Technology and Economics, BME Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, 1111 Budapest, Hungary
Tibor Sipos: Department of Transport Technology and Economics, BME Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, 1111 Budapest, Hungary

Sustainability, 2022, vol. 14, issue 18, 1-11

Abstract: This study was carried out to examine the severity level of crashes by analyzing traffic accidents. The study’s goal is to identify the major contributing factors to traffic accidents in connection to driver behavior and socioeconomic characteristics. In order to find the most probable causes in accordance with the major target variable, which is the level of severity of the crash, the study set out to identify the main attributes induced by the decision tree method (DT). The local people received a semi-structured questionnaire interview with closed-ended questions. The survey asked questions about drivers’ attitude and behavior, as well as other contributing factors such as time of accidents and road type. The attributes were analyzed using the machine-learning method using DT with Python programming language. This method was able to determine the relationship between severe and non-severe crashes and other significant influencing elements. The Duhok city people participated in the survey, which was conducted in the Kurdistan area of northern Iraq. The results of the study demonstrate that the number of lanes, time of the accident, and human attitudes, represented by their adherence to the speed limit, are the primary causes of accidents with victims.

Keywords: machine learning; decision tree classifier; severe accident analysis; social factors; road safety (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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