A bee colony optimization (BCO) and type-2 fuzzy approach to measuring the impact of speed perception on motor vehicle crash involvement

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25510%2F21%3A39918152" target="_blank" >RIV/00216275:25510/21:39918152 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s00500-021-06516-4" target="_blank" >https://link.springer.com/article/10.1007/s00500-021-06516-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00500-021-06516-4" target="_blank" >10.1007/s00500-021-06516-4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A bee colony optimization (BCO) and type-2 fuzzy approach to measuring the impact of speed perception on motor vehicle crash involvement

Popis výsledku v původním jazyce

The major challenge of this paper is to examine how various forms of speed perception affect motor vehicle crash (MVC) involvement. To model this relationship, we use a type-2 fuzzy inference system (T2FIS). Another general challenge is to improve the performance of seven created T2FISs in a sense of compliance with the empirical data. This is achieved by a proposal of an algorithm based on the bee colony optimization (BCO) metaheuristic. The main novelty of this algorithm is the way how the testing points are selected in a type-2 fuzzy environment, which influences the execution efficiency. Data collection was carried out in twelve experiments. A total of 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 their own speed. At each position, three speed levels were assessed: 30, 50, and 70 km/h. As a result of the implemented methodology, a relationship between the various forms of speed perception and participation in MVCs can be quantified. The BCO-based algorithm 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. This can be useful in various educational and recruitment procedures.

Název v anglickém jazyce

A bee colony optimization (BCO) and type-2 fuzzy approach to measuring the impact of speed perception on motor vehicle crash involvement

Popis výsledku anglicky

The major challenge of this paper is to examine how various forms of speed perception affect motor vehicle crash (MVC) involvement. To model this relationship, we use a type-2 fuzzy inference system (T2FIS). Another general challenge is to improve the performance of seven created T2FISs in a sense of compliance with the empirical data. This is achieved by a proposal of an algorithm based on the bee colony optimization (BCO) metaheuristic. The main novelty of this algorithm is the way how the testing points are selected in a type-2 fuzzy environment, which influences the execution efficiency. Data collection was carried out in twelve experiments. A total of 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 their own speed. At each position, three speed levels were assessed: 30, 50, and 70 km/h. As a result of the implemented methodology, a relationship between the various forms of speed perception and participation in MVCs can be quantified. The BCO-based algorithm 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. This can be useful in various educational and recruitment procedures.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Soft Computing

ISSN

1432-7643

e-ISSN

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Svazek periodika

Neuveden

Číslo periodika v rámci svazku

NOV 2021

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

24

Strana od-do

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Kód UT WoS článku

000717919900003

EID výsledku v databázi Scopus

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