IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i11p3937-d366345.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/11/3937/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/11/3937/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nguyen-Phuoc, Duy Quy & Nguyen, Ha Anh & De Gruyter, Chris & Su, Diep Ngoc & Nguyen, Vinh Hoang, 2019. "Exploring the prevalence and factors associated with self-reported traffic crashes among app-based motorcycle taxis in Vietnam," Transport Policy, Elsevier, vol. 81(C), pages 68-74.
    2. Javadreza Vahedi & Afshin Shariat Mohaymany & Zahra Tabibi & Milad Mehdizadeh, 2018. "Aberrant Driving Behaviour, Risk Involvement, and Their Related Factors Among Taxi Drivers," IJERPH, MDPI, vol. 15(8), pages 1-17, August.
    3. Hassan M. Al-Ahmadi & Arshad Jamal & Imran Reza & Khaled J. Assi & Syed Anees Ahmed, 2019. "Using Microscopic Simulation-Based Analysis to Model Driving Behavior: A Case Study of Khobar-Dammam in Saudi Arabia," Sustainability, MDPI, vol. 11(11), pages 1-18, May.
    4. Danish Farooq & Sarbast Moslem & Rana Faisal Tufail & Omid Ghorbanzadeh & Szabolcs Duleba & Ahsen Maqsoom & Thomas Blaschke, 2020. "Analyzing the Importance of Driver Behavior Criteria Related to Road Safety for Different Driving Cultures," IJERPH, MDPI, vol. 17(6), pages 1-14, March.
    5. Chen Wang & Lin Liu & Chengcheng Xu & Weitao Lv, 2019. "Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework," IJERPH, MDPI, vol. 16(3), pages 1-18, January.
    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.
    8. Norman Johnson & Paul Sorlie & Eric Backlund, 1999. "The impact of specific occupation on mortality in the U.S. National Longitudinal Mortality Study," Demography, Springer;Population Association of America (PAA), vol. 36(3), pages 355-367, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mohammed Saleh Alfawzan & Ahmad Aftab, 2022. "Efficiency Assessment of New Signal Timing in Saudi Arabia Implementing Flashing Green Interval Complimented with Law Enforcement Cameras," Sustainability, MDPI, vol. 14(22), pages 1-15, November.
    2. Vagioula Tsoutsi & Maria Papadakaki & George Yannis & Dimosthenis Pavlou & Maria Basta & Joannes Chliaoutakis & Dimitris Dikeos, 2023. "Driving Behaviour in Depression Based on Subjective Evaluation and Data from a Driving Simulator," IJERPH, MDPI, vol. 20(8), pages 1-15, April.
    3. Arshad Jamal & Waleed Umer, 2020. "Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network," IJERPH, MDPI, vol. 17(20), pages 1-22, October.
    4. Tufail Ahmed & Ali Pirdavani & Davy Janssens & Geert Wets, 2023. "Utilizing Intelligent Portable Bicycle Lights to Assess Urban Bicycle Infrastructure Surfaces," Sustainability, MDPI, vol. 15(5), pages 1-22, March.
    5. Nattawut Pumpugsri & Wanchai Rattanawong & Varin Vongmanee, 2023. "Development of a Safety Heavy-Duty Vehicle Model Considering Unsafe Acts, Unsafe Conditions and Near-Miss Events Using Structural Equation Model," Sustainability, MDPI, vol. 15(16), pages 1-20, August.
    6. Danish Farooq & Sarbast Moslem & Arshad Jamal & Farhan Muhammad Butt & Yahya Almarhabi & Rana Faisal Tufail & Meshal Almoshaogeh, 2021. "Assessment of Significant Factors Affecting Frequent Lane-Changing Related to Road Safety: An Integrated Approach of the AHP–BWM Model," IJERPH, MDPI, vol. 18(20), pages 1-17, October.
    7. Muhammad Zahid & Yangzhou Chen & Arshad Jamal & Khalaf A. Al-Ofi & Hassan M. Al-Ahmadi, 2020. "Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study," IJERPH, MDPI, vol. 17(14), pages 1-15, July.
    8. Muhammad Ijaz & Lan Liu & Yahya Almarhabi & Arshad Jamal & Sheikh Muhammad Usman & Muhammad Zahid, 2022. "Temporal Instability of Factors Affecting Injury Severity in Helmet-Wearing and Non-Helmet-Wearing Motorcycle Crashes: A Random Parameter Approach with Heterogeneity in Means and Variances," IJERPH, MDPI, vol. 19(17), pages 1-24, August.
    9. Yaqi Liu & Xiaoyuan Wang, 2020. "Differences in Driving Intention Transitions Caused by Driver’s Emotion Evolutions," IJERPH, MDPI, vol. 17(19), pages 1-22, September.
    10. Moneim Massar & Imran Reza & Syed Masiur Rahman & Sheikh Muhammad Habib Abdullah & Arshad Jamal & Fahad Saleh Al-Ismail, 2021. "Impacts of Autonomous Vehicles on Greenhouse Gas Emissions—Positive or Negative?," IJERPH, MDPI, vol. 18(11), pages 1-23, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Quy Nguyen-Phuoc, Duy & An Ngoc Nguyen, Nguyen & Nguyen, Minh Hieu & Ngoc Thi Nguyen, Ly & Oviedo-Trespalacios, Oscar, 2022. "Factors influencing road safety compliance among food delivery riders: An extension of the job demands-resources (JD-R) model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 541-556.
    2. Mansoor, Umer & Jamal, Arshad & Su, Junbiao & Sze, N.N. & Chen, Anthony, 2023. "Investigating the risk factors of motorcycle crash injury severity in Pakistan: Insights and policy recommendations," Transport Policy, Elsevier, vol. 139(C), pages 21-38.
    3. Meyer, S.C. & Künn-Nelen, A.C., 2014. "Do occupational demands explain the educational gradient in health?," Research Memorandum 016, Maastricht University, Graduate School of Business and Economics (GSBE).
    4. Nguyen-Phuoc, Duy Quy & Vo, Nguyen S. & Su, Diep Ngoc & Nguyen, Vinh Hoang & Oviedo-Trespalacios, Oscar, 2021. "What makes passengers continue using and talking positively about ride-hailing services? The role of the booking app and post-booking service quality," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 367-384.
    5. Loske, Dominic & Klumpp, Matthias, 2021. "Human-AI collaboration in route planning: An empirical efficiency-based analysis in retail logistics," International Journal of Production Economics, Elsevier, vol. 241(C).
    6. Xiao Lin & Zhengfeng Huang & Yun Ye & Jingxin Dong & Hongxiang Feng & Pengjun Zheng, 2023. "Effects of Aging on Taxi Service Performance: A Comparative Study Based on Different Age Groups," Sustainability, MDPI, vol. 15(22), pages 1-20, November.
    7. Seolyoung Lee & Jae Hun Kim & Jiwon Park & Cheol Oh & Gunwoo Lee, 2020. "Deep-Learning-Based Prediction of High-Risk Taxi Drivers Using Wellness Data," IJERPH, MDPI, vol. 17(24), pages 1-13, December.
    8. Quy Nguyen-Phuoc, Duy & Nguyen, Teron & Ngoc Su, Diep & Thi Le, Phuong & Oviedo-Trespalacios, Oscar, 2022. "How do social cues from other passengers affect word-of-mouth and intention to continue using bus services? A second-order SEM approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 302-320.
    9. Michael Geruso, 2012. "Black-White Disparities in Life Expectancy: How Much Can the Standard SES Variables Explain?," Demography, Springer;Population Association of America (PAA), vol. 49(2), pages 553-574, May.
    10. Mingyu Hou & Jianchuan Cheng, 2021. "The Role of Social Networks in Mobile Phone Use among Pedestrians: A Pilot Study in China," Sustainability, MDPI, vol. 13(1), pages 1-17, January.
    11. Corina Boar, 2017. "Dynastic Precautionary Savings," 2017 Meeting Papers 343, Society for Economic Dynamics.
    12. Itay Saporta-Eksten & Ity Shurtz & Sarit Weisburd, 2021. "Social Security, Labor Supply, and Health of Older Workers: Quasi-Experimental Evidence from a Large Reform [Identification and Estimation of Local Average Treatment Effects]," Journal of the European Economic Association, European Economic Association, vol. 19(4), pages 2168-2208.
    13. Nguyen-Phuoc, Duy Quy & Su, Diep Ngoc & Nguyen, Minh Hieu & Vo, Nguyen S. & Oviedo-Trespalacios, Oscar, 2022. "Factors influencing intention to use on-demand shared ride-hailing services in Vietnam: risk, cost or sustainability?," Journal of Transport Geography, Elsevier, vol. 99(C).
    14. Gonzalo Edwards & Raimundo Soto & Felipe Zurita, 2020. "Life Expectancy and Income Levels in Chile," Documentos de Trabajo 544, Instituto de Economia. Pontificia Universidad Católica de Chile..
    15. Chien-Hung Wei & Ying Lee & Yu-Wen Luo & Jyun-Jie Lu, 2021. "Incorporating Personality Traits to Assess the Risk Level of Aberrant Driving Behaviors for Truck Drivers," IJERPH, MDPI, vol. 18(9), pages 1-18, April.
    16. Quang Hoc Tran & Yao-Min Fang & Tien-Yin Chou & Thanh-Van Hoang & Chun-Tse Wang & Van Truong Vu & Thi Lan Huong Ho & Quang Le & Mei-Hsin Chen, 2022. "Short-Term Traffic Speed Forecasting Model for a Parallel Multi-Lane Arterial Road Using GPS-Monitored Data Based on Deep Learning Approach," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
    17. Scott Lynch, 2003. "Cohort and life-course patterns in the relationship between education and health: A hierarchical approach," Demography, Springer;Population Association of America (PAA), vol. 40(2), pages 309-331, May.
    18. Nguyen-Phuoc, Duy Quy & Su, Diep Ngoc & Tran, Phuong Thi Kim & Le, Diem-Trinh Thi & Johnson, Lester W., 2020. "Factors influencing customer's loyalty towards ride-hailing taxi services – A case study of Vietnam," Transportation Research Part A: Policy and Practice, Elsevier, vol. 134(C), pages 96-112.
    19. repec:pri:cheawb:racial_disparities_chw_geruso_april2010 is not listed on IDEAS
    20. Faris Tarlochan & Mohamed Izham Mohamed Ibrahim & Batool Gaben, 2022. "Understanding Traffic Accidents among Young Drivers in Qatar," IJERPH, MDPI, vol. 19(1), pages 1-13, January.
    21. Ross Stolzenberg & James Lindgren, 2010. "Retirement and death in office of U.S. Supreme Court justices," Demography, Springer;Population Association of America (PAA), vol. 47(2), pages 269-298, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:11:p:3937-:d:366345. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.