Ontologies for Supporting Traffic Behaviour, Critical Gap, and Conflict Models at Unsignalized Intersection Routes

F. Mustakim, Mohammad Nazir Ahmad, Azlan Abdul Aziz, S. Jamian, A. Mahmud, Rabiah Abdul Kadir, Riza Sulaiman
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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.
支持交通行为的本体,关键间隙,和冲突模型在无信号交叉口路线
背景:交通运输大数据管理的挑战可以通过本体论方法来识别。在许多国家,道路交通事故被认为是造成人身伤害和经济损失的一个重要原因。仅在2020年,马来西亚就发生了567,516起事故。这意味着该国平均每天有13人死于交通事故。在这里,我们的工作重点是差距模式,关键差距分析,右转驾驶者的差距接受(RTMs)和严重冲突模型在马来西亚农村公路三腿无信号交叉口。方法:在前期通过交通量、驾驶员转向行为和速度研究来识别选定交叉口的交通行为。涉及(UI2)、(ui9)、(UI10)三个无信号交叉口。在逻辑回归模型的开发中,本研究使用了5个不同的数据集:u2无信号交叉口右转驾驶员(259数据集)、u9右转驾驶员(239数据集)、u10右转驾驶员(314数据集)、右转驾驶员组合模型(812数据集)和严重冲突变道(351数据集)。在每个无信号交叉口2、9和10处进行临界间隙的确定。同时利用可视化空间图对各路口的间隙模式进行分析。此外,本文还研究了逻辑回归方法、人工神经元网络和结构方程建模。结果:发现缝隙型3为易受伤害的缝隙型。此外,本研究还发现,差距三模式、摩托车骑手、超过50公里时速的限速以及在次要道路上摩托车停在乘用车附近的RTMs的属性助长了严重的冲突。此外,本研究还提出了一种新颖的工具,利用接近速度与间隙之间的可视化空间图来识别危险无信号交叉口。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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