IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i15p5936-d388547.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/15/5936/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/15/5936/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    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. 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.

    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. Roy Cerqueti & Claudio Lupi, 2023. "Severe testing of Benford’s law," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(2), pages 677-694, June.
    2. Malik Muneeb Abid & Shehar Bano & Ashok Kumar & Muhammad Iqbal & Muhammad Laiq Ur Rahman Shahid & Ahsan Javed & Muhammad Atiq Ur Rehman Tariq, 2022. "Trend towards Helmet Usage and the Behavior of Riders While Wearing Helmets," Sustainability, MDPI, vol. 14(22), pages 1-17, November.
    3. Sujanie Peiris & Janneke Berecki-Gisolf & Stuart Newstead & Bernard Chen & Brian Fildes, 2021. "Development of a Methodology for Estimating the Availability of ADAS-Dependent Road Infrastructure," Sustainability, MDPI, vol. 13(17), pages 1-18, August.
    4. Tölö, Eero & Jokivuolle, Esa & Virén, Matti, 2017. "Do banks’ overnight borrowing rates lead their CDS price? Evidence from the Eurosystem," Journal of Financial Intermediation, Elsevier, vol. 31(C), pages 93-106.
    5. I. Koetsier & J.A. Bikker, 2017. "Herding behaviour of Dutch pension funds in sovereign bond investments," Working Papers 17-15, Utrecht School of Economics.
    6. Gonzalo, Jesús & Pitarakis, Jean-Yves, 2021. "Spurious relationships in high-dimensional systems with strong or mild persistence," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1480-1497.
    7. Tengilimoglu, Oguz & Carsten, Oliver & Wadud, Zia, 2023. "Implications of automated vehicles for physical road environment: A comprehensive review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
    8. Kézdi, Gábor & Mátyás, László & Balázsi, László & Divényi, János Károly, 2014. "A közgazdasági adatforradalom és a panelökonometria [The revolution in economic data and panel econometrics]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(11), pages 1319-1340.
    9. Andrei C. Holman & Simona A. Popușoi, 2020. "How You Deal with Your Emotions Is How You Drive. Emotion Regulation Strategies, Traffic Offenses, and the Mediating Role of Driving Styles," Sustainability, MDPI, vol. 12(12), pages 1-13, June.
    10. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2019. "Forecast density combinations with dynamic learning for large data sets in economics and finance," Working Paper 2019/7, Norges Bank.
    11. Matúš Šucha & Ralf Risser & Kristýna Honzíčková, 2021. "Advanced Driver Assistant Systems Focused on Pedestrians’ Safety: A User Experience Approach," Sustainability, MDPI, vol. 13(8), pages 1-17, April.
    12. Sarbast Moslem & Muhammet Gul & Danish Farooq & Erkan Celik & Omid Ghorbanzadeh & Thomas Blaschke, 2020. "An Integrated Approach of Best-Worst Method (BWM) and Triangular Fuzzy Sets for Evaluating Driver Behavior Factors Related to Road Safety," Mathematics, MDPI, vol. 8(3), pages 1-20, March.
    13. Boysen-Hogrefe, Jens & Gern, Klaus-Jürgen & Groll, Dominik & Jannsen, Nils & Kooths, Stefan & Plödt, Martin & van Roye, Björn & Scheide, Joachim & Schwarzmüller, Tim, 2015. "Das europäische Verfahren zur Vermeidung und Korrektur makroökonomischer Ungleichgewichte: Auswertung der bisherigen Erfahrung und mögliche Reformansätze," Kieler Beiträge zur Wirtschaftspolitik 7, Kiel Institute for the World Economy (IfW Kiel).
    14. Li, Xia & Xiao, Yuewen & Zhao, Xiaodong & Ma, Xinwei & Wang, Xintong, 2023. "Modeling mixed traffic flows of human-driving vehicles and connected and autonomous vehicles considering human drivers’ cognitive characteristics and driving behavior interaction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    15. Jinhua Tan & Li Gong & Xuqian Qin, 2019. "Global Optimality under Internet of Vehicles: Strategy to Improve Traffic Safety and Reduce Energy Dissipation," Sustainability, MDPI, vol. 11(17), pages 1-16, August.
    16. 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.
    17. Roy Cerqueti & Claudio Lupi, 2021. "Some New Tests of Conformity with Benford’s Law," Stats, MDPI, vol. 4(3), pages 1-17, September.
    18. 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.
    19. I. Koetsier & J.A. Bikker, 2017. "Herding behaviour of Dutch pension funds in sovereign bond investments," Working Papers 17-15, Utrecht School of Economics.
    20. Vladimir Pajković & Mirjana Grdinić-Rakonjac, 2021. "Evaluation of Road Safety Performance Based on Self-Reported Behaviour Data Set," Sustainability, MDPI, vol. 13(24), pages 1-18, December.

    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:jsusta:v:12:y:2020:i:15:p:5936-:d:388547. 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.