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Predicting Risky and Aggressive Driving Behavior among Taxi Drivers: Do Spatio-Temporal Attributes Matter?

Author

Listed:
  • Muhammad Zahid

    (College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

  • Yangzhou Chen

    (College of Artificial Intelligence and Automation, Beijing University of Technology, Beijing 100124, China)

  • Sikandar Khan

    (Department of Mechanical Engineering, King Fahd University of Petroleum & Minerals, KFUPM Box 5069, Dhahran 31261, Saudi Arabia)

  • Arshad Jamal

    (Department of Civil and Environmental Engineering, King Fahd University of Petroleum & Minerals, KFUPM Box 5055, Dhahran 31261, Saudi Arabia)

  • Muhammad Ijaz

    (School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China)

  • Tufail Ahmed

    (UHasselt, Transportation Research Institute (IMOB), Agoralaan, 3590 Diepenbeek, Belgium)

Abstract

Risky and aggressive driving maneuvers are considered a significant indicator for traffic accident occurrence as well as they aggravate their severity. Traffic violations caused by such uncivilized driving behavior is a global issue. Studies in existing literature have used statistical analysis methods to explore key contributing factors toward aggressive driving and traffic violations. However, such methods are unable to capture latent correlations among predictor variables, and they also suffer from low prediction accuracies. This study aimed to comprehensively investigate different traffic violations using spatial analysis and machine learning methods in the city of Luzhou, China. Violations committed by taxi drivers are the focus of the current study since they constitute a significant proportion of total violations reported in the city. Georeferenced violation data for the year 2016 was obtained from the traffic police department. Detailed descriptive analysis is presented to summarize key statistics about various violation types. Results revealed that over-speeding was the most prevalent violation type observed in the study area. Frequency-based nearest neighborhood cluster methods in Arc map Geographic Information System (GIS) were used to develop hotspot maps for different violation types that are vital for prioritizing and conducting treatment alternatives efficiently. Finally, different machine learning (ML) methods, including decision tree, AdaBoost with a base estimator decision tree, and stack model, were employed to predict and classify each violation type. The proposed methods were compared based on different evaluation metrics like accuracy, F-1 measure, specificity, and log loss. Prediction results demonstrated the adequacy and robustness of proposed machine learning (ML) methods. However, a detailed comparative analysis showed that the stack model outperformed other models in terms of proposed evaluation metrics.

Suggested Citation

  • Muhammad Zahid & Yangzhou Chen & Sikandar Khan & Arshad Jamal & Muhammad Ijaz & Tufail Ahmed, 2020. "Predicting Risky and Aggressive Driving Behavior among Taxi Drivers: Do Spatio-Temporal Attributes Matter?," IJERPH, MDPI, vol. 17(11), pages 1-21, June.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:11:p:3937-:d:366345
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    References listed on IDEAS

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    6. Muhammad Zahid & Yangzhou Chen & Arshad Jamal & Coulibaly Zie Mamadou, 2020. "Freeway Short-Term Travel Speed Prediction Based on Data Collection Time-Horizons: A Fast Forest Quantile Regression Approach," Sustainability, MDPI, vol. 12(2), pages 1-19, January.
    7. Muhammad Tauhidur Rahman & Kh. Md. Nahiduzzaman, 2019. "Examining the Walking Accessibility, Willingness, and Travel Conditions of Residents in Saudi Cities," IJERPH, MDPI, vol. 16(4), pages 1-16, February.
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