Traffic Flow Optimization at Toll Plaza Using Proactive Deep Learning Strategies

Abstract

Global urbanization and increasing traffic volume have intensified traffic congestion throughout transportation infrastructure, particularly at toll plazas, highlighting the critical need to implement proactive transportation infrastructure solutions. Traditional toll plaza management approaches, often relying on manual interventions, suffer from inefficiencies that fail to adapt to dynamic traffic flow and are unable to produce preemptive control strategies, resulting in prolonged queues, extended travel times, and adverse environmental effects. This study proposes a proactive traffic control strategy using advanced technologies to combat toll plaza congestion and optimize traffic management. The approach involves deep learning convolutional neural network models (YOLOv7–Deep SORT) for vehicle counting and an extended short-term memory model for short-term arrival rate prediction. When projected arrival rates exceed a threshold, the strategy proactively activates variable speed limits (VSLs) and ramp metering (RM) strategies during peak hours. The novelty of this study lies in its predictive and adaptive capabilities, ensuring efficient traffic flow management. Validated through a case study at Ravi Toll Plaza Lahore using PTV VISSIMv7, the proposed method reduces queue length by 57% and vehicle delays by 47% while cutting fuel consumption and pollutant emissions by 28.4% and 34%, respectively. Additionally, by identifying the limitations of conventional approaches, this study presents a novel framework alongside the proposed strategy to bridge the gap between theory and practice, making it easier for toll plaza operators and transportation authorities to adopt and benefit from advanced traffic management techniques. Ultimately, this study underscores the importance of integrated and proactive traffic control strategies in enhancing traffic management, minimizing congestion, and fostering a more sustainable transportation system.

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“…Bertini et al used computerized microscopic traffic simulation programs Paramics and VISSIM to analyze a diamond interchange in Portland, Oregon [41]. Simulation models allow designers to analyze non-existing situations, like adding a new lane or turn bay and studying the impact of geometric changes, or optimizing traffic flow at toll plaza [42,43]. Mosseri et al studied a multi-modal urban corridor with complex geometry and traffic control in VISSIM [44].…”

“…Hashmi et al optimized the traffic flow at toll plaza using neural network models and VISSIM v7 for a case study of Ravi toll plaza in Lahore. The simulation results showed that fuel consumption costs, vehicle delays, and pollutant emissions were reduced by 28.4%, 47%, and 34%, respectively by optimizing the traffic flow [43]. Karthik and Loui developed a congestion model for Kerala, India, which estimated the congestion cost to be almost 25% of the total expenditure on road infrastructure in the 2021 budget [52].…”