IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v124y2019icp611-626.html
   My bibliography  Save this article

Spatial prediction of traffic accidents with critical driving events – Insights from a nationwide field study

Author

Listed:
  • Ryder, Benjamin
  • Dahlinger, Andre
  • Gahr, Bernhard
  • Zundritsch, Peter
  • Wortmann, Felix
  • Fleisch, Elgar

Abstract

Despite the fact that semi-autonomous vehicles will become more and more prevalent in the coming decades, recent studies have highlighted that traffic accidents will persist as a core issue for road users, insurers, and policy makers alike. Researchers and industry players see potential in the technology embedded in semi-autonomous vehicles to combat this challenge by reliably predicting locations with a high likelihood of traffic accidents. This technology can be leveraged to detect accidents and ‘near miss incidents’, such as heavy braking and evasive manoeuvres, otherwise known as Critical Driving Events (CDEs). The locations of CDEs could identify areas of high accident exposure, offering automotive insurers a unique opportunity to reduce traffic accidents through the adoption of active loss prevention business models, such as providing safe-routing services and in-vehicle warnings. To date, there is limited empirical evidence on whether the Crash Frequency and Crash Rate of locations can be accurately identified through CDEs. To address this research gap, an 18-week naturalistic driving field study of 72 vehicles was conducted in Switzerland, covering over 690,000 km. Data collected from the CAN Bus of these vehicles indicate that there is a proportional relationship between the CDEs of the fleet, and the Crash Frequency and Crash Rate of a location. Furthermore, a nationwide spatial regression analysis was applied to determine Crash Frequency across the majority of the Swiss road network. We identify the relationship between Crash Frequency, and the CDEs and Trip Frequency of the fleet, along with additional explanatory variables for urban and highway locations. These insights provide first evidence that insurance companies and other industry players with access to a nationwide semi-autonomous fleet can determine existing and emerging locations of high accident probability, enabling more proactive business models and safety focused services.

Suggested Citation

  • Ryder, Benjamin & Dahlinger, Andre & Gahr, Bernhard & Zundritsch, Peter & Wortmann, Felix & Fleisch, Elgar, 2019. "Spatial prediction of traffic accidents with critical driving events – Insights from a nationwide field study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 611-626.
  • Handle: RePEc:eee:transa:v:124:y:2019:i:c:p:611-626
    DOI: 10.1016/j.tra.2018.05.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856417310145
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tra.2018.05.007?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fagnant, Daniel J. & Kockelman, Kara, 2015. "Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 167-181.
    2. Lord, Dominique & Mannering, Fred, 2010. "The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 291-305, June.
    3. Bansal, Prateek & Kockelman, Kara M., 2017. "Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 95(C), pages 49-63.
    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. Gu, Shuang & Li, Keping & Feng, Tao & Yan, Dongyang & Liu, Yanyan, 2022. "The prediction of potential risk path in railway traffic events," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    2. Federico Orsini & Mariaelena Tagliabue & Giulia De Cet & Massimiliano Gastaldi & Riccardo Rossi, 2021. "Highway Deceleration Lane Safety: Effects of Real-Time Coaching Programs on Driving Behavior," Sustainability, MDPI, vol. 13(16), pages 1-16, August.
    3. Tibor Sipos & Anteneh Afework Mekonnen & Zsombor Szabó, 2021. "Spatial Econometric Analysis of Road Traffic Crashes," Sustainability, MDPI, vol. 13(5), pages 1-16, February.
    4. Amini, Mostafa & Bagheri, Ali & Delen, Dursun, 2022. "Discovering injury severity risk factors in automobile crashes: A hybrid explainable AI framework for decision support," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    5. Marjana Čubranić-Dobrodolac & Libor Švadlenka & Svetlana Čičević & Aleksandar Trifunović & Momčilo Dobrodolac, 2020. "Using the Interval Type-2 Fuzzy Inference Systems to Compare the Impact of Speed and Space Perception on the Occurrence of Road Traffic Accidents," Mathematics, MDPI, vol. 8(9), pages 1-19, September.

    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. Mao, Wei & Shepherd, Simon & Harrison, Gillian & Xu, Meng, 2024. "Autonomous vehicle market development in Beijing: A system dynamics approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    2. Di, Yunran & Zhang, Weihua & Ding, Heng & Zheng, Xiaoyan & Ran, Bin, 2024. "Cooperative control of dynamic CAV dedicated lanes and vehicle active lane changing in expressway bottleneck areas," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    3. Badia, Hugo & Jenelius, Erik, 2021. "Design and operation of feeder systems in the era of automated and electric buses," Transportation Research Part A: Policy and Practice, Elsevier, vol. 152(C), pages 146-172.
    4. Nikitas, Alexandros & Parkinson, Simon & Vallati, Mauro, 2022. "The deceitful Connected and Autonomous Vehicle: Defining the concept, contextualising its dimensions and proposing mitigation policies," Transport Policy, Elsevier, vol. 122(C), pages 1-10.
    5. Guo, Yuntao & Souders, Dustin & Labi, Samuel & Peeta, Srinivas & Benedyk, Irina & Li, Yujie, 2021. "Paving the way for autonomous Vehicles: Understanding autonomous vehicle adoption and vehicle fuel choice under user heterogeneity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 364-398.
    6. Manivasakan, Hesavar & Kalra, Riddhi & O'Hern, Steve & Fang, Yihai & Xi, Yinfei & Zheng, Nan, 2021. "Infrastructure requirement for autonomous vehicle integration for future urban and suburban roads – Current practice and a case study of Melbourne, Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 152(C), pages 36-53.
    7. Simpson, Jesse R. & Mishra, Sabyasachee, 2021. "Developing a methodology to predict the adoption rate of Connected Autonomous Trucks in transportation organizations using peer effects," Research in Transportation Economics, Elsevier, vol. 90(C).
    8. Raj, Alok & Kumar, J. Ajith & Bansal, Prateek, 2020. "A multicriteria decision making approach to study barriers to the adoption of autonomous vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 122-137.
    9. Talebian, Ahmadreza & Mishra, Sabyasachee, 2022. "Unfolding the state of the adoption of connected autonomous trucks by the commercial fleet owner industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    10. Gurumurthy, Krishna Murthy & Kockelman, Kara M., 2020. "Modeling Americans’ autonomous vehicle preferences: A focus on dynamic ride-sharing, privacy & long-distance mode choices," Technological Forecasting and Social Change, Elsevier, vol. 150(C).
    11. Simpson, Jesse R. & Mishra, Sabyasachee & Talebian, Ahmadreza & Golias, Mihalis M., 2019. "An estimation of the future adoption rate of autonomous trucks by freight organizations," Research in Transportation Economics, Elsevier, vol. 76(C).
    12. McLeay, Fraser & Olya, Hossein & Liu, Hongfei & Jayawardhena, Chanaka & Dennis, Charles, 2022. "A multi-analytical approach to studying customers motivations to use innovative totally autonomous vehicles," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    13. Marletto, Gerardo, 2019. "Who will drive the transition to self-driving? A socio-technical analysis of the future impact of automated vehicles," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 221-234.
    14. Kassens-Noor, Eva & Dake, Dana & Decaminada, Travis & Kotval-K, Zeenat & Qu, Teresa & Wilson, Mark & Pentland, Brian, 2020. "Sociomobility of the 21st century: Autonomous vehicles, planning, and the future city," Transport Policy, Elsevier, vol. 99(C), pages 329-335.
    15. Shatanawi, Mohamad & Alatawneh, Anas & Mészáros, Ferenc, 2022. "Implications of static and dynamic road pricing strategies in the era of autonomous and shared autonomous vehicles using simulation-based dynamic traffic assignment: The case of Budapest," Research in Transportation Economics, Elsevier, vol. 95(C).
    16. Rejali, Sina & Aghabayk, Kayvan & Esmaeli, Saeed & Shiwakoti, Nirajan, 2023. "Comparison of technology acceptance model, theory of planned behavior, and unified theory of acceptance and use of technology to assess a priori acceptance of fully automated vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 168(C).
    17. Li, Shunxi & Sui, Pang-Chieh & Xiao, Jinsheng & Chahine, Richard, 2019. "Policy formulation for highly automated vehicles: Emerging importance, research frontiers and insights," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 573-586.
    18. Hussain, Qinaat & Alhajyaseen, Wael K.M. & Adnan, Muhammad & Almallah, Mustafa & Almukdad, Abdulkarim & Alqaradawi, Mohammed, 2021. "Autonomous vehicles between anticipation and apprehension: Investigations through safety and security perceptions," Transport Policy, Elsevier, vol. 110(C), pages 440-451.
    19. Debbaghi, Fatima-Zahra & Kroesen, Maarten & de Vries, Gerdien & Pudāne, Baiba, 2024. "Daily schedule changes in the automated vehicle era: Uncovering the heterogeneity behind the veil of low survey commitment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 182(C).
    20. Merfeld, Katrin & Wilhelms, Mark-Philipp & Henkel, Sven & Kreutzer, Karin, 2019. "Carsharing with shared autonomous vehicles: Uncovering drivers, barriers and future developments – A four-stage Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 66-81.

    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:eee:transa:v:124:y:2019:i:c:p:611-626. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    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.