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Classification of Inter-Urban Highway Drivers’ Resting Behavior for Advanced Driver-Assistance System Technologies using Vehicle Trajectory Data from Car Navigation Systems

Author

Listed:
  • Jaeheon Choi

    (Department of Transportation Engineering, Myong Ji University, Gyeonggi-do 17058, Korea)

  • Kyuil Lee

    (Department of Transportation Engineering, Myong Ji University, Gyeonggi-do 17058, Korea)

  • Hyunmyung Kim

    (Department of Transportation Engineering, Myong Ji University, Gyeonggi-do 17058, Korea)

  • Sunghi An

    (Institute of Transportation Studies, University of California, Irvine, CA 92697, USA)

  • Daisik Nam

    (Institute of Transportation Studies, University of California, Irvine, CA 92697, USA)

Abstract

Fatigue-related crashes, which are mainly caused by drowsy or distracted driving, account for a significant portion of fatal accidents on highways. Smart vehicle technologies can address this issue of road safety to improve the sustainability of transportation systems. Advanced driver-assistance system (ADAS) can aid drowsy drivers by recommending and guiding them to rest locations. Past research shows a significant correlation between driving distance and driver fatigue, which has been actively studied in the analysis of resting behavior. Previous research efforts have mainly relied on survey methods at specific locations, such as rest areas or toll booths. However, such traditional methods, like field surveys, are expensive and often produce biased results, based on sample location and time. This research develops methods to better estimate travel resting behavior by utilizing a large-scale dataset obtained from car navigation systems, which contain 591,103 vehicle trajectories collected over a period of four months in 2014. We propose an algorithm to statistically categorize drivers according to driving distances and their number of rests. The main algorithm combines a statistical hypothesis test and a random sampling method based on the renowned Monte-Carlo simulation technique. We were able to verify that cumulative travel distance shares a significant relationship with one’s resting decisions. Furthermore, this research identifies the resting behavior pattern of drivers based upon their travel distances. Our methodology can be used by sustainable traffic safety operators to their driver guiding strategies criterion using their own data. Not only will our methodology be able to aid sustainable traffic safety operators in constructing their driver guidance strategies criterion using their own data, but it could also be implemented in actual car navigation systems as a mid-term solution. We expect that ADAS combined with the proposed algorithm will contribute to improving traffic safety and to assisting the sustainability of road systems.

Suggested Citation

  • Jaeheon Choi & Kyuil Lee & Hyunmyung Kim & Sunghi An & Daisik Nam, 2020. "Classification of Inter-Urban Highway Drivers’ Resting Behavior for Advanced Driver-Assistance System Technologies using Vehicle Trajectory Data from Car Navigation Systems," Sustainability, MDPI, vol. 12(15), pages 1-20, July.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:15:p:5936-:d:388547
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    References listed on IDEAS

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    1. Danish Farooq & Sarbast Moslem & Szabolcs Duleba, 2019. "Evaluation of Driver Behavior Criteria for Evolution of Sustainable Traffic Safety," Sustainability, MDPI, vol. 11(11), pages 1-15, June.
    2. Dongkwan Lee & Jean-Michel Guldmann & Choongik Choi, 2019. "Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers," Sustainability, MDPI, vol. 11(23), pages 1-13, November.
    3. Giovanny Pillajo-Quijia & Blanca Arenas-Ramírez & Camino González-Fernández & Francisco Aparicio-Izquierdo, 2020. "Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods," Sustainability, MDPI, vol. 12(4), pages 1-28, February.
    4. C. W. J. Granger, 1998. "Extracting information from mega‐panels and high‐frequency data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 52(3), pages 258-272, November.
    5. Sujanie Peiris & Janneke Berecki-Gisolf & Bernard Chen & Brian Fildes, 2020. "Road Trauma in Regional and Remote Australia and New Zealand in Preparedness for ADAS Technologies and Autonomous Vehicles," Sustainability, MDPI, vol. 12(11), pages 1-26, May.
    6. C. W. J. Granger, 1998. "Extracting information from mega‐panels and high‐frequency data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 52(3), pages 258-272, November.
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    1. Qian Cheng & Xiaobei Jiang & Haodong Zhang & Wuhong Wang & Chunwen Sun, 2020. "Data-Driven Detection Methods on Driver’s Pedal Action Intensity Using Triboelectric Nano-Generators," Sustainability, MDPI, vol. 12(21), pages 1-17, October.
    2. Luid Pereira de Oliveira & Felipe Jiménez Alonso & Marcelino Aurélio Vieira da Silva & Breno Tostes de Gomes Garcia & Diana Mery Messias Lopes, 2020. "Analysis of the Influence of Training and Feedback Based on Event Data Recorder Information to Improve Safety, Operational and Economic Performance of Road Freight Transport in Brazil," Sustainability, MDPI, vol. 12(19), pages 1-22, October.
    3. Rong Cao & Xuehui Chen & Jianmin Jia & Hui Zhang, 2023. "Uncovering Equity and Travelers’ Behavior on the Expressway: A Case Study of Shandong, China," Sustainability, MDPI, vol. 15(11), pages 1-19, May.

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