Abstract
The random nature of traffic conditions on freeways can cause excessive congestion and irregularities in the traffic flow. Ramp metering is a proven effective method to maintain freeway efficiency under various traffic conditions. Creating a reliable and practical ramp metering algorithm that considers both critical traffic measures and historical data is still a challenging problem. In this study we use simple machine learning approaches to develop a novel real-time ramp metering algorithm. The proposed algorithm is computationally simple and has minimal data requirements, which makes it practical for real-world applications. We conduct a simulation study to evaluate and compare the proposed approach with an existing traffic-responsive ramp metering algorithm.
Highlights
Reducing traffic congestion and maintaining the flow within the freeway capacity is necessary for the safe and proper operation of any urban traffic system [1,2,3,4,5,6,7]
If the estimated volume is less than the threshold, the ramp signal is disabled by setting the green phase duration (TGreen) equal to the traffic cycle (TCycle)
The software had to be customized to accommodate the ramp metering scenario with upstream and downstream sensors allowing to capture real-time speed, flow, and occupancy data. Both ALINEA and the proposed ramp metering algorithms were implemented in the software
Summary
Reducing traffic congestion and maintaining the flow within the freeway capacity is necessary for the safe and proper operation of any urban traffic system [1,2,3,4,5,6,7]. Ramp metering (RM) is one of the most important strategies for traffic control to reduce freeway delay and improve safety [8,9,10,11,12,13,14]. RM is an effective way to reduce traffic congestion and maintain capacity flow on a freeway It regulates the access of ramp traffic to the mainline [8]. Fixed time methods, which are the simplest, consider historical traffic information to determine the metering rates and establish the rates on a time-of-day basis [17]. These systems do not perform effectively in the presence of severe traffic fluctuations. We focus on integrating regression and clustering, two effective machine learning approaches, to come up with a framework to control ramp signal in an accurate and efficient way
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