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Prediction of Crash Severity as a Way of Road Safety Improvement: The Case of Saint Petersburg, Russia

Author

Listed:
  • Maria Rodionova

    (Graduate School of Industrial Economics, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia)

  • Angi Skhvediani

    (Graduate School of Industrial Economics, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia)

  • Tatiana Kudryavtseva

    (Graduate School of Industrial Economics, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia)

Abstract

This article investigates factors that explain road crash severity levels in Saint Petersburg, Russia, during the 2015–2021 period. The research takes into account factors such as lighting conditions, weather conditions, infrastructure factors, human factors, accident types, and vehicle category and color to assess their influence on crash severity. The most influential accident type is run-off-road crashes, which are associated with an 11.2% increase in fatal accidents. The biggest reason for the increase in fatal accidents due to road infrastructure conditions is road barrier shortcomings (2.8%). Road infrastructure conditions, such as a lack of road lighting, have a significant effect on fatal outcomes, increasing them by 12.6%, and this is the most influential factor in the analysis. The obtained results may serve as a basis for Saint Petersburg authorities to develop new road safety policies.

Suggested Citation

  • Maria Rodionova & Angi Skhvediani & Tatiana Kudryavtseva, 2022. "Prediction of Crash Severity as a Way of Road Safety Improvement: The Case of Saint Petersburg, Russia," Sustainability, MDPI, vol. 14(16), pages 1-20, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:9840-:d:883968
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    References listed on IDEAS

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    1. 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.
    2. Aziemah Azhar & Noratiqah Mohd Ariff & Mohd Aftar Abu Bakar & Azzuhana Roslan, 2022. "Classification of Driver Injury Severity for Accidents Involving Heavy Vehicles with Decision Tree and Random Forest," Sustainability, MDPI, vol. 14(7), pages 1-19, March.
    3. Khondoker Billah & Hatim O. Sharif & Samer Dessouky, 2021. "Analysis of Pedestrian–Motor Vehicle Crashes in San Antonio, Texas," Sustainability, MDPI, vol. 13(12), pages 1-23, June.
    4. 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.
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