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Examining drivers’ socio-demographic variables and perceptions towards sanction mechanisms on speeding behaviour on highways: targeting appropriate prevention

Published online by Cambridge University Press:  16 March 2021

Fitri Trapsilawati*
Affiliation:
Department of Mechanical and Industrial Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia.
Nadhiya Ulhaq Priatna
Affiliation:
Department of Mechanical and Industrial Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia.
Titis Wijayanto
Affiliation:
Department of Mechanical and Industrial Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia.
Ari Widyanti
Affiliation:
Department of Industrial Engineering, Bandung Institute of Technology, Bandung, Indonesia.
Utami Dyah Syafitri
Affiliation:
Department of Statistics, IPB University, Bogor, Indonesia.
Nur Chamidah
Affiliation:
Department of Mathematics, Airlangga University, Surabaya, Indonesia
*
*Corresponding author. E-mail: fitri.trapsilawati@ugm.ac.id.

Abstract

Investigating the underlying predictors of speeding behaviour deserves the full attention of research. This study aims to examine the effects of demographic variables on the perceived deterrent mechanisms and to predict speeding behaviour to target appropriate prevention programmes. In this study, 212 randomly selected drivers having a valid car driving licence participated in an online survey. The results revealed that demographic variables influenced drivers’ perceptions towards social and legal sanctions as well as material loss. The model revealed that two sanction-related constructs, that is, legal sanction (b = −0⋅227, P = 0⋅007) and material loss (b = −0⋅218, P = 0⋅005), as well as lax perception towards traffic accident (b = −0⋅176, P = 0⋅025), were the significant predictors of speeding behaviour. These findings suggested that prevention programmes should prioritise young and single drivers. The most effective targeted prevention programmes are highlighted accordingly based on the study results.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2021

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