Red-light running vehicles behaviour based on linear regression approach at traffic lights along Bakau Condong road, Batu Pahat, Johor
Red-light dilemma zone is widely known as an area on the high-speed intersection approach, where vehicles either safely stop before the stop line or proceed through the intersection during the red interval. Within such an area, a vehicle might be involved in a right angle crash or rear-end collision...
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Main Authors: | , , , , , , , |
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Format: | Article |
Language: | English |
Published: |
Malaysian Institute of Road Safety Research (MIROS)
2020
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Subjects: | |
Online Access: | http://eprints.uthm.edu.my/6354/1/AJ%202020%20%28299%29.pdf http://eprints.uthm.edu.my/6354/ |
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Institution: | Universiti Tun Hussein Onn Malaysia |
Language: | English |
Summary: | Red-light dilemma zone is widely known as an area on the high-speed intersection approach, where vehicles either safely stop before the stop line or proceed through the intersection during the red interval. Within such an area, a vehicle might be involved in a right angle crash or rear-end collision. The objective of this study is to develop a prediction model of red light running and determine red running distance and the dilemma zones. Data that have been collected by using a video camera and Traffic Data Collector (TDC) had been analyzed such as traffic flow, traffic speed that red-light running (RLR), the number of the vehicle that RLR and traffic light cycle time. The data was analyzed by using applications such as Microsoft Excel, Minitab and PETRAPro software. Based on the result of Multiple Linear Regression Analysis, it shows the parameters that involved in vehicles behaviours along the traffic lights has been proved by highly significant of the p-value that less or equal to 0.1 (p ≤ 0.1) with 90% confidence level. The parameters such as the traffic flow and average speed of RLR vehicles with the p-value of 0.051 and 0.034 respectively proved that all these parameters cause the increasing rate of RLR toward traffic light intersections. The values of R2 = 63.76 % and also show that the prediction model that was obtained is satisfied. |
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