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Journal Article

Citation

Mustakim F, Aziz AA, Fujita M, Wisetjindawat W, Ahmad MN, Sukor NSA. Int. J. Road Saf. 2021; 2(1): 45-53.

Copyright

(Copyright © 2021, Malaysian Institute of Road Safety Research (MIROS))

DOI

unavailable

PMID

unavailable

Abstract

Road accidents are regarded as a leading cause of both human and financial losses in countries at all stages of development. In Malaysia, 548,598 accidents occurred in the year 2018 alone. This resulted in an average of 16 people killed every day due to traffic accidents. The key objective of this study is to recognize the causal factors such as dangerously moving vehicles that largely contribute to the occurrence of traffic accidents, critical injuries and deaths at points of access or in this case, the unsignalised intersection. The study focused on right-turning vehicles (RTVs) demonstrating gap acceptance and conflict analysis at three unsignalised intersections along Federal Route 50 (Ft 50). The selected intersections were located in Road Sections (RS) 10, 9, and 2. Logistic regression was used to develop analysis models of gap acceptance behaviour. In analysing gap acceptance, five gap patterns were suggested to incorporate each potential gap pattern displayed by RTVs in a range between large and small roads at a T-junction. The study findings were used to thoroughly examine how drivers behave with regard to gap acceptance and serious conflicts, utilizing the suggested gap patterns displayed by RTVs. The paper demonstrated how second vehicle variables influenced the acceptance of shorter gaps by RTVs. Such variables included serious nose-tail and angular conflicts, and also when motorbikes or passenger cars were the vehicles going past the RTVs. Meanwhile, a longer gap was accepted by RTVs encountering traffic lights and channelization. From the investigations conducted as part of the study, the main proposal is for average gap pattern 3 at unsignalised intersection to form the basis for developing feasible countermeasures for road accidents.

Copyright (c) 2021 International Journal of Road Safety


Language: en

Keywords

Gap pattern

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