REDUCING A POSSIBILITY OF TRANSPORT CONGESTION ON FREEWAYS USING RAMP CONTROL MANAGEMENT

Lagerev, Roman; Kapski, Denis; Burinskienė, Marija; Barauskas, Andrius · 2017 · Crossref

DOI: 10.3846/16484142.2017.1336643

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Summary

This paper addresses the problem of transport congestion on freeways, specifically focusing on merge junctions where on-ramp vehicles create turbulence that reduces mainline capacity and triggers breakdowns. The authors argue that while Ramp-Metering (RM) is an effective control technique, it is often limited by the risk of ramp queues spilling over into adjacent infrastructure. The study aims to introduce a methodology for optimizing RM concepts to minimize ramp queue lengths and reduce the probability of congestion formation, thereby improving traffic flow quality and safety. The methodology employs a quadratic programming algorithm implemented in MATLAB to estimate optimal ramp flows. The algorithm divides the highway into segments and ramps, using traffic density and flow intensity as input criteria. It seeks to minimize the squared residuals of vehicle queues on adjacent ramps while ensuring that the total flow in any highway segment does not exceed its capacity. The model accounts for the share of ramp flows passing through specific highway sections and respects practical capacity limits for ramps (240–900 veh/h). The theoretical framework is validated through a case study of the Western bypass ramps in Vilnius, Lithuania. The simulation compares two access control strategies: metering one vehicle per cycle versus metering several vehicles per cycle, against a baseline scenario with no access restrictions. The results from the Vilnius case study demonstrate that unrestricted access leads to significant turbulence and potential congestion, particularly when ramp flow exceeds 26% of the mainline flow. The application of the proposed algorithm successfully identifies optimal flow dosing values that prevent highway segment overload. The comparison of the two metering strategies revealed that there is little difference in performance between metering one vehicle versus several vehicles per cycle, though the multi-vehicle strategy yielded slightly better results in terms of queue management. The algorithm effectively estimated the maximum sustainable ramp streams (e.g., 125 to 327 vehicles per 15 minutes for specific ramps) without causing congestion on the mainline. The significance of this work lies in providing a practical, adaptive tool for Intelligent Transportation Systems (ITS) to manage freeway access. By using objective traffic data to dynamically adjust ramp metering, the proposed method can delay the onset of congestion, accelerate its dissolution, and reduce the spatial extent of traffic jams. This approach not only optimizes throughput but also enhances road safety and reduces environmental impacts by minimizing fuel consumption and emissions associated with stop-and-go traffic. The study concludes that adaptive ramp control is a viable solution for managing recurrent congestion in urban highway networks.

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• traffic density