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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
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    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. 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).
    4. Tibor Sipos & Anteneh Afework Mekonnen & Zsombor Szabó, 2021. "Spatial Econometric Analysis of Road Traffic Crashes," Sustainability, MDPI, vol. 13(5), pages 1-16, February.
    5. Jing Wang & Chenhao Zhao & Zhixia Liu, 2024. "Can Historical Accident Data Improve Sustainable Urban Traffic Safety? A Predictive Modeling Study," Sustainability, MDPI, vol. 16(22), pages 1-24, November.
    6. 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.

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