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

Čubranić-Dobrodolac, Marjana; Švadlenka, Libor; Čičević, Svetlana; Trifunović, Aleksandar; Dobrodolac, Momčilo

doi:10.3390/math8091548

Cited by 18 publications

(9 citation statements)

References 55 publications

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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%

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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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Section: The Proposed Model Based On Testing the T2fis Structuresmentioning

confidence: 99%

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

Self Cite

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“…A systematic and data mining approach was developed to extract and generate high-risk precrash test scenarios from the data [ 23 ]. Speed and space perception also affects the occurrence of road traffic accidents [ 24 ]. Yu developed a binary logit regression model to differentiate the hazardous scenarios of autonomous vehicles [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of the Safety Effectiveness and Cost of Autonomous Vehicles Based on Multivariable Coupling

Tan

Zhao

Zhang

et al. 2023

Sensors

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There is a need for in-depth studies of autonomous vehicle safety that evaluate the effectiveness of safety functions and different “atomic” technology combinations for vehicles and roads. In this paper, we provide a crash avoidance effectiveness evaluation model for autonomous vehicles enabled with different sensor combinations based on multiple variables of 14 different “atomic” sensing technologies on the vehicle side and road side, 52 safety functions, and 14 accident types. Meanwhile, a cost-sharing model is developed based on the traveled distance during the life cycle of vehicles and based on the traffic flow over the life cycle of roads to evaluate the unit cost per km of different “atomic” technology combinations. The results clearly show that the cost increases with the addition of “atomic” sensing technologies on the vehicle side, while an increase in crash avoidance effectiveness decreases. It is necessary to switch to V2X and to introduce roadside “atomic” technology combinations to realize better safety effectiveness at a lower cost for vehicles. In addition, a map that covers the safety effectiveness and cost per kilometer of all “atomic” technology combinations is calculated for decision-makers to select combinations under the preconditions of cost and safety.

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“…Such a high number of fatalities in road traffic around the world is also a result of risk factors, such as speeding, not using the protection system (safety helmet, seat belt, etc.) and driving under the influence of alcohol ( 4 – 6 ).…”

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confidence: 99%

What Affects the Perception of a Drunk Driving Campaign?

Pešić

Trifunović

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Driving under the influence of alcohol has been recognized as a significant contributing factor to the occurrence of one out of four road traffic crashes. Young drivers are acknowledged as a particularly hazardous group of road users because of insufficient driving experience, but also because of unsafe behavior while behind the wheel, which encompasses driving under the influence of alcohol. The primary aim of this study was to examine the perception of a campaign about driving under the influence of alcohol, while the secondary goal is to present the self-reported behavior and attitudes of young drivers about driving under the influence of alcohol in the Republic of Serbia. Data were collected through a questionnaire created by a multidisciplinary team of experts in the fields of traffic engineering, psychology, and education. A total of 4,015 respondents participated in the study, aged from 16 to 30. The results show that younger respondents have a 1.89 times higher chance of remembering the road traffic safety campaign then older ones. In addition, respondents were 5.057 times more likely to perceive and remember campaign messages that have fewer words than campaign messages that have more words.

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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

Cited by 18 publications

References 55 publications

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

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

An Evaluation of the Safety Effectiveness and Cost of Autonomous Vehicles Based on Multivariable Coupling

What Affects the Perception of a Drunk Driving Campaign?

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