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Potentialities of Vehicle Trajectory Big Data for Monitoring Potentially Fatigued Drivers and Explaining Vehicle Crashes on Motorway Sections

Author

Listed:
  • Hyunho Chang

    (Urban Science Institute, Incheon National University, Incheon 22012, Korea)

  • Dongjoo Park

    (Department of Transportation Engineering, University of Seoul, Seoul 02540, Korea)

Abstract

Task-related fatigue, caused by prolonged driving, is a major cause of vehicle crashes. Despite noticeable academic achievements, monitoring drivers’ fatigue on road sections is still an ongoing challenge which must be met to prevent and reduce traffic accidents. Fortunately, individual instances of vehicle trajectory big data collected through advanced vehicle-GPS systems offer a strong opportunity to trace driving durations. We propose a new approach by which to monitor task-related fatigued drivers by directly using the ratio of potentially fatigued drivers (RFD) to all drivers for each road section. The method used to compute the RFD index was developed based on two inputs: the distribution of the driving duration (extracted from vehicle trajectory data), and the boundary condition of the driving duration between fatigued and non-fatigued states. We demonstrate the potentialities of the method using vehicle trajectory big data and real-life traffic accident data. Results showed that the measured RFD has a strong explanatory power with regard to the traffic accident rate, with a statistical correlation of 0.86 at least, for regional motorway sections. Therefore, it is expected that the proposed approach is a feasible means of successfully monitoring fatigued drivers in the present and near future era of smart-mobility big data.

Suggested Citation

  • Hyunho Chang & Dongjoo Park, 2020. "Potentialities of Vehicle Trajectory Big Data for Monitoring Potentially Fatigued Drivers and Explaining Vehicle Crashes on Motorway Sections," Sustainability, MDPI, vol. 12(15), pages 1-16, July.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:15:p:5877-:d:387816
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    References listed on IDEAS

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    1. Hyun-ho Chang & Seung-hoon Cheon, 2019. "The potential use of big vehicle GPS data for estimations of annual average daily traffic for unmeasured road segments," Transportation, Springer, vol. 46(3), pages 1011-1032, June.
    2. Mahdi Pour-Rouholamin & Mohammad Jalayer & Huaguo Zhou, 2017. "Modelling single-vehicle, single-rider motorcycle crash injury severity: an ordinal logistic regression approach," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 21(3), pages 344-363, September.
    3. Dongkwan Lee & Jean-Michel Guldmann & Burkhard von Rabenau, 2018. "Interactions between the built and socio-economic environment and driver demographics: spatial econometric models of car crashes in the Columbus Metropolitan Area," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 22(1), pages 17-37, January.
    4. Do-Gyeong Kim & Yuhwa Lee, 2017. "Identifying the influences of demographic characteristics and personality of inveterate drunk drivers on the likelihood of driving under the influence of alcohol (DUIA) recurrence," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 21(3), pages 300-311, September.
    5. Jungyeol Hong & Reuben Tamakloe & Dongjoo Park, 2019. "A Comprehensive Analysis of Multi-Vehicle Crashes on Expressways: A Double Hurdle Approach," Sustainability, MDPI, vol. 11(10), pages 1-22, May.
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