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Fuzzy Logic Model for Assessing Accident Proneness Based on Passenger Vehicle Speed in Real and Virtual Traffic Conditions

Author

Listed:
  • Nenad Marković

    (Faculty of Transport and Traffic Engineering, University of Belgrade, 11000 Belgrade, Serbia)

  • Tijana Ivanišević

    (Academy of Professional Studies Sumadija, 34000 Kragujevac, Serbia)

  • Svetlana Čičević

    (Faculty of Transport and Traffic Engineering, University of Belgrade, 11000 Belgrade, Serbia)

  • Aleksandar Trifunović

    (Faculty of Transport and Traffic Engineering, University of Belgrade, 11000 Belgrade, Serbia)

Abstract

Inappropriate or unsafe speed is one of the main factors that affects the number of road crashes as well as the severity of the consequences. Research shows that speed is an influential factor in the occurrence of road crashes in more than 30% of road crashes with fatal outcomes and in over 12% of all road crashes. With an increase in speed, the risk of road crashes increases as well as the severity of the consequences. The perception of the vehicle speed in the traffic lane is one of the basic prerequisites for the safe functioning of traffic, that is, for the successful and timely interaction of all road users. Therefore, the challenge of this paper is to examine how the assessment of the speed of a passenger vehicle in different environments affects the prediction of the respondent’s participation in road crashes. Bearing the above in mind, an experimental study was carried out, in real traffic conditions (RTC) as well as in a virtual environment using a driving simulator (DS), at different passenger vehicle speeds (30, 50 and 70 km/h), and at different perspectives of observing the oncoming vehicle (observing the vehicle from the front, from the back, from the side and from the driver’s seat) by the respondents. The respondents had the task of evaluating the passenger vehicle speed, in all tested conditions and at all tested speeds. Standard statistical models and fuzzy logic were used to analyze the obtained results. The results show statistically significant differences for all tested situations and all tested speeds as well as statistically significant differences depending on the gender of the respondents, the driver’s license category, the driver’s experience, frequency of driving and depending on whether respondents wear glasses. Bearing in mind the results of the developed model, by applying fuzzy logic, it can be concluded that the proposed model can be used to assess the propensity of respondents to participate in road crashes, based on perception of vehicle speeds in two tested environments.

Suggested Citation

  • Nenad Marković & Tijana Ivanišević & Svetlana Čičević & Aleksandar Trifunović, 2024. "Fuzzy Logic Model for Assessing Accident Proneness Based on Passenger Vehicle Speed in Real and Virtual Traffic Conditions," Mathematics, MDPI, vol. 12(3), pages 1-20, January.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:3:p:421-:d:1328018
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    References listed on IDEAS

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    1. Aleksandar Trifunović & Tijana Ivanišević & Svetlana Čičević & Sreten Simović & Vedran Vukšić & Živana Slović, 2023. "Do Statistics Show Differences between Distance Estimations of 3D Objects in the Traffic Environment Using Glances, Side View Mirrors, and Camera Display?," Mathematics, MDPI, vol. 11(5), pages 1-13, March.
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