The Impact of Road Characteristics Assessment on The Traffic Accidents Occurrence

ČibranićDobrodolac, Marjana; Molková, Tatiana; Švadlenka, Libor

doi:10.15308/sinteza-2019-26-31

Cited by 5 publications

(5 citation statements)

References 3 publications

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“…Finally, the defined T2FIS structures should be tested. This is accomplished based on Equation (2) [51,52]. Cumulative deviation (CD) is a parameter that describes how good a T2FIS describes the empirical data.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Resultsmentioning

confidence: 99%

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

et al. 2020

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“…( 2) is marked as Propensity(i). The same concept of calculating the performance of FIS structures can be found in other papers (see Cubranic-Dobrodolac, Molkova, and Svadlenka, 2019;Jovcic, Prusa, Dobrodolac, and Svadlenka, 2019).…”

Section: According Tomentioning

confidence: 91%

Modelling driver propensity for traffic accidents: a comparison of multiple regression analysis and fuzzy approach

Čubranić-Dobrodolac

Švadlenka

Čičević

et al. 2019

International Journal of Injury Control and Safety Promotion

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This research proposes an assessment and decision support model to use when a driver should be examined about their propensity for traffic accidents, based on an estimation of the driver's psychological traits. The proposed model was tested on a sample of 305 drivers. Each participant completed four psychological tests: the Barratt Impulsiveness Scale (BIS-11), the Aggressive Driving Behaviour Questionnaire (ADBQ), the Manchester Driver Attitude Questionnaire (DAQ), and the Questionnaire for Self-assessment of Driving Ability. In addition, participants completed an extensive demographic and driving survey. Various fuzzy inference systems were tested and each was defined using the well-known Wang-Mendel method for rule-base definition based on empirical data. For this purpose, a programming code was designed and utilised. Based on the obtained results, it was determined which combination of the considered psychological tests provides the best prediction of a driver's propensity for traffic accidents.The best of the considered fuzzy inference systems might be used as a decision support tool in various situations, such as in recruitment procedures for professional drivers. The validity of the proposed fuzzy approach was confirmed as its implementation provided better results than from statistics, in this case multiple regression analysis.

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“…Here, we applied the well-known method proposed by Wang and Mendel (1992). This approach is widely used in the literature (Chang, Hieh, and Liao,2005;Čubranić-Dobrodolac et al, 2019Čubranić-Dobrodolac et al, , 2020aČubranić-Dobrodolac et al, , 2020bČubranić-Dobrodolac et al, , 2020cJovčić et al, 2019).…”

Section: The Proposed Model Based On Testing the T2fis Structuresmentioning

confidence: 99%

“…Fuzzy logic is widely used as a tool for MVCs prediction. Some of the examples are the papers by Chai et al (2017), Čubranić-Dobrodolac et al (2019Čubranić-Dobrodolac et al ( , 2020aČubranić-Dobrodolac et al ( , 2020bČubranić-Dobrodolac et al ( , 2020c, Dimitriou and Vlahogianni (2015), Nemet et al (2019), Thakur (2014), Xiong et al (2019). To test the stability of the initially obtained conclusions, we performed the optimization of T2FIS in the sense of maximizing the compliance with the empirical data, by using the Bee Colony Optimization (BCO) metaheuristic.…”

Section: Introductionmentioning

confidence: 99%

A Bee Colony Optimization (BCO) and Type-2 Fuzzy Approach to Measuring the Impact of Speed Perception on Motor Vehicle Crash Involvement

Čubranić-Dobrodolac

Švadlenka

Čičević

et al. 2021

Preprint

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This paper examines how a driver’s perception of various speed levels, as well as driver’s speed perception from different positions, affect the propensity for motor vehicle crashes (MVCs). Data collection is performed in twelve experiments. 178 young drivers assessed the speed level from four positions; three of them relate to the speed perception of other vehicles on the road, while the remaining one represents the assessment of own speed. At each position, three speed levels were assessed: 30, 50, and 70 km/h. To process data, seven Type-2 fuzzy inference systems (T2FISs) are designed and tested in a sense of compliance with the empirical data. As a result, a relationship between the various forms of speed perception and participation in MVCs can be quantified. To examine the initial conclusions, the optimization of each of these T2FISs is performed by implementing the bee colony optimization (BCO) metaheuristic. The BCO based algorithm proposed in this paper achieved an average improvement of 21.17% in the performance of the initial T2FIS structures. The final results indicate that the drivers whose speed perception of the vehicle they are looking at from the rear side, as well as of the own vehicle, is poor have an elevated risk toward participation in MVCs compared to other forms of speed perception. The best-found T2FIS structures can be used as a decision-making tool that quantifies the driver propensity for MVCs, which can be useful in various educational and recruitment procedures in the field of transportation and traffic safety.

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The Impact of Road Characteristics Assessment on The Traffic Accidents Occurrence

Cited by 5 publications

References 3 publications

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

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

Modelling driver propensity for traffic accidents: a comparison of multiple regression analysis and fuzzy approach

A Bee Colony Optimization (BCO) and Type-2 Fuzzy Approach to Measuring the Impact of Speed Perception on Motor Vehicle Crash Involvement

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