Abstract

Background: The challenge arises from management of big data in transportation can be identified by ontological approach. Road accidents are regarded as a significant cause of both harm to humans and financial damage in many countries. In Malaysia, there were 567,516 accidents in 2020 alone. This translates into a daily average of 13 people killed due to traffic accidents in the country. Here, our work focuses on gap patterns, critical gap analysis, gap acceptance of right-turning motorists (RTMs) and serious conflict model on Malaysia Rural Roadways at three-leg unsignalized intersections. Methods: In early stage, traffic volume, motorist turning manoeuvre and speed study are implemented to identify the traffic behaviour at selected intersection. Three unsignalized intersection (UI) was involved namely (UI2), (UI 9), and (UI10). In the development of logistic regression models, five different datasets were used in this study: right-turning motorist (RTM) at unsignalized intersection UI2 (259 dataset), right-turning motorist at UI 9 (239 dataset), right turning motorist at UI 10 (314 dataset), right turning motorist combined model (812 dataset) and serious conflict lane change (351 dataset). Determination of critical gap was carried out at each unsignalized intersection 2, 9 and 10. Meanwhile gap-pattern analysis at each intersection used visualization spatial plot. In addition, this work investigated logistic regression method, artificial neuron network and structural equation modelling. Results: Gap pattern three was discovered to be a vulnerable gap pattern. Furthermore, this research reveals that the attributes of the gap three pattern, motorcycles rider, speed limit exceeding 50 kph and RTMs where motorcycles stop near passenger cars in minor roads encourage serious conflict. Moreover, this study proposes novelty tool for identifying hazardous unsignalized intersection by using visualization spatial plot between approach speed and gap.

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