Analysis and Prediction of Risky Driving Behaviors Using Fuzzy Analytical Hierarchy Process and Machine Learning Techniques

Waseem Alam, Haiyan Wang, Amjad Pervez (), Muhammad Safdar, Arshad Jamal (), Meshal Almoshaogeh and Hassan M. Al-Ahmadi
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Waseem Alam: School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
Haiyan Wang: School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
Amjad Pervez: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Muhammad Safdar: School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
Arshad Jamal: Department of Civil Engineering, College of Engineering, Qassim University, Buraydah 51452, Saudi Arabia
Meshal Almoshaogeh: Department of Civil Engineering, College of Engineering, Qassim University, Buraydah 51452, Saudi Arabia
Hassan M. Al-Ahmadi: Civil & Environmental Engineering Department Research Center for Smart Mobility & Logistics, King Fahd University of Petroleum & Mineral, Dhahran 31261, Saudi Arabia

Sustainability, 2024, vol. 16, issue 11, 1-27

Abstract: Driver behavior plays a pivotal role in ensuring road safety as it is a significant factor in preventing traffic crashes. Although extensive research has been conducted on this topic in developed countries, there is a notable gap in understanding driver behavior in developing countries, such as Pakistan. It is essential to recognize that the cultural nuances, law enforcement practices, and government investments in traffic safety in Pakistan are significantly different from those in other regions. Recognizing this disparity, this study aims to comprehensively understand risky driving behaviors in Peshawar, Pakistan. To achieve this goal, a Driver Behavior Questionnaire was designed, and responses were collected using Google Forms, resulting in 306 valid responses. The study employs a Fuzzy Analytical Hierarchy Process framework to evaluate driver behavior’s ranking criteria and weight factors. This framework assigns relative weights to different criteria and captures the uncertainty of driving thought patterns. Additionally, machine learning techniques, including support vector machine, decision tree, Naïve Bayes, Random Forest, and ensemble model, were used to predict driver behavior, enhancing the reliability and accuracy of the predictions. The results showed that the ensemble machine learning approach outperformed others with a prediction accuracy of 0.84. In addition, the findings revealed that the three most significant risky driving attributes were violations, errors, and lapses. Certain factors, such as clear road signage and driver attention, were identified as important factors in improving drivers’ risk perception. This study serves as a benchmark for policymakers, offering valuable insights to formulate effective policies for improving traffic safety.

Keywords: driver behavior; Fuzzy Analytical Hierarchy Process; survey; machine learning; traffic safety management system (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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