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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25510%2F19%3A39915616" target="_blank" >RIV/00216275:25510/19:39915616 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.tandfonline.com/doi/abs/10.1080/17457300.2019.1690002?journalCode=nics20" target="_blank" >https://www.tandfonline.com/doi/abs/10.1080/17457300.2019.1690002?journalCode=nics20</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/17457300.2019.1690002" target="_blank" >10.1080/17457300.2019.1690002</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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&apos;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 utilized. Based on the obtained results, it was determined which combination of the considered psychological tests provides the best prediction of a driver&apos;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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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&apos;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 utilized. Based on the obtained results, it was determined which combination of the considered psychological tests provides the best prediction of a driver&apos;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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

50703 - Transport planning and social aspects of transport (transport engineering to be 2.1)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

International Journal of Injury Control and Safety Promotion

ISSN

1745-7300

e-ISSN

—

Svazek periodika

2019

Číslo periodika v rámci svazku

listopad

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

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Kód UT WoS článku

000496108600001

EID výsledku v databázi Scopus

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