A Decision Support Model for Transportation Companies to Examine Driver Behavior

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25510%2F21%3A39918136" target="_blank" >RIV/00216275:25510/21:39918136 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9524537" target="_blank" >https://ieeexplore.ieee.org/document/9524537</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TEM.2021.3102879" target="_blank" >10.1109/TEM.2021.3102879</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Decision Support Model for Transportation Companies to Examine Driver Behavior

Popis výsledku v původním jazyce

The entire society, and particularly the transportation companies, has an interest to improve traffic safety. Besides more than 3000 lost lives on the roads every day, there are significant financial consequences of road traffic accidents (RTAs). The purpose of this article is to design an efficient model for providing information about driver propensity for RTAs based on assessing their personality traits. This is achieved by creating a fuzzy inference system (FIS) where inputs are the scores from the implemented psychological instruments and output is the number of RTAs experienced by a driver. To adjust the functioning of FIS to the empirical data, a bee colony optimization (BCO) metaheuristic is applied. In this optimization procedure, we test three approaches for defining the variables of initial FIS and compare their performance. Simulation results show the differences between the considered approaches and, generally, very promising achievements of the proposed algorithm. The best-found FIS reached a 36% improvement of the objective function compared to the starting FIS. This FIS can be used, inter alia, as a decision-making tool in recruitment procedures for professional drivers to assess their propensity for RTAs, by that saving the lives of people and reducing the costs of the companies.

Název v anglickém jazyce

A Decision Support Model for Transportation Companies to Examine Driver Behavior

Popis výsledku anglicky

The entire society, and particularly the transportation companies, has an interest to improve traffic safety. Besides more than 3000 lost lives on the roads every day, there are significant financial consequences of road traffic accidents (RTAs). The purpose of this article is to design an efficient model for providing information about driver propensity for RTAs based on assessing their personality traits. This is achieved by creating a fuzzy inference system (FIS) where inputs are the scores from the implemented psychological instruments and output is the number of RTAs experienced by a driver. To adjust the functioning of FIS to the empirical data, a bee colony optimization (BCO) metaheuristic is applied. In this optimization procedure, we test three approaches for defining the variables of initial FIS and compare their performance. Simulation results show the differences between the considered approaches and, generally, very promising achievements of the proposed algorithm. The best-found FIS reached a 36% improvement of the objective function compared to the starting FIS. This FIS can be used, inter alia, as a decision-making tool in recruitment procedures for professional drivers to assess their propensity for RTAs, by that saving the lives of people and reducing the costs of the companies.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

50203 - Industrial relations

Projekt

—

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

IEEE Transactions on Engineering Management

ISSN

0018-9391

e-ISSN

—

Svazek periodika

Neuveden

Číslo periodika v rámci svazku

AUG 2021

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

1-13

Kód UT WoS článku

000732624100001

EID výsledku v databázi Scopus

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