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Using the Interval Type-2 Fuzzy Inference Systems to Compare the Impact of Speed and Space Perception on the Occurrence of Road Traffic Accidents

Author

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  • Marjana Čubranić-Dobrodolac

    (Faculty of Transport Engineering, University of Pardubice, Studentská 95, 532 10 Pardubice, Czech Republic
    Faculty of Transport and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, 11000 Belgrade, Serbia)

  • Libor Švadlenka

    (Faculty of Transport Engineering, University of Pardubice, Studentská 95, 532 10 Pardubice, Czech Republic)

  • Svetlana Čičević

    (Faculty of Transport and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, 11000 Belgrade, Serbia)

  • Aleksandar Trifunović

    (Faculty of Transport and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, 11000 Belgrade, Serbia)

  • Momčilo Dobrodolac

    (Faculty of Transport and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, 11000 Belgrade, Serbia)

Abstract

A constantly increasing number of deaths on roads forces analysts to search for models that predict the driver’s propensity for road traffic accidents (RTAs). This paper aims to examine a relationship between the speed and space assessment capabilities of drivers in terms of their association with the occurrence of RTAs. The method used for this purpose is based on the implementation of the interval Type-2 Fuzzy Inference System (T2FIS). The inputs to the first T2FIS relate to the speed assessment capabilities of drivers. These capabilities were measured in the experiment with 178 young drivers, with test speeds of 30, 50, and 70 km/h. The participants assessed the aforementioned speed values from four different observation positions in the driving simulator. On the other hand, the inputs of the second T2FIS are space assessment capabilities. The same group of drivers took two types of space assessment tests—2D and 3D. The third considered T2FIS sublimates of all previously mentioned inputs in one model. The output in all three T2FIS structures is the number of RTAs experienced by a driver. By testing three proposed T2FISs on the empirical data, the result of the research indicates that the space assessment characteristics better explain participation in RTAs compared to the speed assessment capabilities. The results obtained are further confirmed by implementing a multiple regression analysis.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:9:p:1548-:d:411370
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    References listed on IDEAS

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    1. Wang, Chao & Quddus, Mohammed & Ison, Stephen, 2009. "The effects of area-wide road speed and curvature on traffic casualties in England," Journal of Transport Geography, Elsevier, vol. 17(5), pages 385-395.
    2. Stefan Jovčić & Petr Průša & Momčilo Dobrodolac & Libor Švadlenka, 2019. "A Proposal for a Decision-Making Tool in Third-Party Logistics (3PL) Provider Selection Based on Multi-Criteria Analysis and the Fuzzy Approach," Sustainability, MDPI, vol. 11(15), pages 1-23, August.
    3. Chen, Tzu-Ying & Jou, Rong-Chang, 2019. "Using HLM to investigate the relationship between traffic accident risk of private vehicles and public transportation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 119(C), pages 148-161.
    4. Pingping Gao & Yabin Gao, 2019. "Quadrilateral Interval Type-2 Fuzzy Regression Analysis for Data Outlier Detection," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-9, August.
    5. Shafaei Bajestani, Narges & Vahidian Kamyad, Ali & Nasli Esfahani, Ensieh & Zare, Assef, 2018. "Prediction of retinopathy in diabetic patients using type-2 fuzzy regression model," European Journal of Operational Research, Elsevier, vol. 264(3), pages 859-869.
    6. 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.
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    Cited by:

    1. Marjana Čubranić-Dobrodolac & Stefan Jovčić & Sara Bošković & Darko Babić, 2023. "A Decision-Making Model for Professional Drivers Selection: A Hybridized Fuzzy–AROMAN–Fuller Approach," Mathematics, MDPI, vol. 11(13), pages 1-24, June.
    2. Ke Zhang & Yaming Guo, 2023. "Attention-Based Residual Dilated Network for Traffic Accident Prediction," Mathematics, MDPI, vol. 11(9), pages 1-15, April.

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