Prediction of Road Accident Risk for Vehicle Fleet Based on Statistically Processed Tire Wear Model

  • Robertas Pečeliūnas Faculty of Transport Engineering, Vilnius Gediminas Technical University
  • Vidas Žuraulis Faculty of Transport Engineering, Vilnius Gediminas Technical University
  • Paweł Droździel Faculty of Mechanical Engineering, Lublin University of Technology
  • Saugirdas Pukalskas Faculty of Transport Engineering, Vilnius Gediminas Technical University
Keywords: road accident, prediction, tread depth, distribution, accident rate, accident risk


The goal of the paper is to investigate the impact of tire tread depth on road accident risk and to develop an accident rate prediction model. The state of 4288 vehicle tires using tread depth gauge was inspected and processed statistically. The tread depth of the most worn tire from each vehicle was registered for further analysis. Based on the collected data, a statistical tire tread depth model for an insurance company vehicle fleet had been developed. The conformity of the gamma distribution to the data was verified upon applying the Pearson compatibility criterion. The paper provides the histograms of the frequencies of tire tread depths and the theoretical curves of the distribution density. The probability of the accident risk depending on the tire tread depth (adaptive risk index) was calculated applying the formed distributions and risk index dependence on the tire tread depth for the inspected vehicle fleet. According to the developed prediction model, an upgrade of the regulation for the minimum allowed tire tread depth by 2 mm (up to 3.6 mm) could reduce road accident risk (caused by poor adhesion to road surface) to 19.3% for the chosen vehicle fleet. Such models are useful for road safety experts, insurance companies and accident cost evaluation specialists by predicting expenses related to insurance events.


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How to Cite
Pečeliūnas R, Žuraulis V, Droździel P, Pukalskas S. Prediction of Road Accident Risk for Vehicle Fleet Based on Statistically Processed Tire Wear Model. Promet [Internet]. 2022Jul.12 [cited 2024May23];34(4):619-30. Available from: