Proactive integrated traffic control to mitigate congestion at toll plazas

Zhou, Tong; Yangshan, Sun; Wang, Xu; Dai, Rongjian; Cao, Rong; Wu, Xiaopei

doi:10.1049/itr2.12348

“…Limited research has focused on proactive traffic control at freeway toll plazas. Zhou et al LSTM predicted traffic flow and implemented proactive control strategies, including variable speed limits (VSLs) and lane configuration before toll plazas [47]. Meanwhile, both Spiliopoulou et al and Papageorgiou et al proposed a comprehensive conceptual framework for real-time merging traffic using the local ALINEA RM strategy, improving toll plaza efficiency by reducing vehicle delay and queue length.…”

Section: Literature Reviewmentioning

confidence: 99%

Traffic Flow Optimization at Toll Plaza Using Proactive Deep Learning Strategies

Hashmi,

Ud-Din,

Khan

et al. 2024

Infrastructures

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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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“…The collaboration between cars, however, is not taken into consideration by this strategy. To direct cars, references [14,32] employed pricing strategy and a dynamic toll policy. The extended anti-Stackelberg game was used in reference [15] to help the traffic centre direct the motorist towards the system's ideal path.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Some researchers choose a selection of roads with comparable driving times based on past vehicle path data and propose them to driving cars based on probability [13] in order to spread vehicles on various paths to relieve traffic congestion. Alternatively, a charge dependent on the level of path congestion might direct the vehicle's choice of route [14] or by choosing the vehicle's route through the interactive game with the road network center [15].…”

Section: Introductionmentioning

confidence: 99%

An efficient algorithm for optimal route node sensing in smart tourism Urban traffic based on priority constraints

Ding,

Yao,

Khezri

2023

Wireless Netw

8

0

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The public transportation system is now dealing with a number of problems brought on by the sharp increase in automobile ownership in cities as well as the buildup of vehicles as a result of events and accidents. However, the city's limited road network capacity cannot keep up with the increasing traffic demand, which further worsens travel conditions and results in a waste of time and money. Given that it is challenging to enhance the capacity of the road network in practice, efficient vehicle travel and evacuation using algorithms has emerged as a recent study focus. It is crucial to learn how to manage urban traffic issues during emergencies and maintain smooth and safe traffic flow. The existing studies only considers the optimized route selection for individual vehicles, signal cycle of traffic lights and deploy historical data to disperse the vehicles on alternative routes. However, such works do not consider the conflict of routes between vehicles, the customized traffic demand of each vehicle and uncertain traffic conditions. Therefore, this paper proposes a novel approach to facilitate the user to select the optimal route with real-time traffic scenario. Furthermore, the Nash equilibrium is established by mutual information swapping and self-adaptive learning method. Simulation results show that the proposed algorithm has better route selection capability in real-time personalized road traffic as compared with existing algorithms.

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An Efficient Algorithm for Optimal Route Node Sensing in Smart Tourism Urban Traffic Based on Priority Constraints

Ding,

Yao,

Khezri

2023

Preprint

View full text Add to dashboard Cite

The public transportation system is now dealing with a number of problems brought on by the sharp increase in automobile ownership in cities as well as the buildup of vehicles as a result of events and accidents. However, the city's limited road network capacity cannot keep up with the increasing traffic demand, which further worsens travel conditions and results in a waste of time and money. Given that it is challenging to enhance the capacity of the road network in practice, efficient vehicle travel and evacuation using algorithms has emerged as a recent study focus. It is crucial to learn how to manage urban traffic issues during emergencies and maintain smooth and safe traffic flow. The existing studies only considers the optimized route selection for individual vehicles, signal cycle of traffic lights and deploy historical data to disperse the vehicles on alternative routes. However, such works do not consider the conflict of routes between vehicles, the customized traffic demand of each vehicle and uncertain traffic conditions. Therefore, this paper proposes a novel approach to facilitate the user to select the optimal route with real-time traffic scenario. Furthermore, the Nash equilibrium is established by mutual information swapping and self-adaptive learning method. Simulation results show that the proposed algorithm has better route selection capability in real-time personalized road traffic as compared with existing algorithms.

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Proactive integrated traffic control to mitigate congestion at toll plazas

Cited by 3 publications

References 31 publications

Traffic Flow Optimization at Toll Plaza Using Proactive Deep Learning Strategies

Traffic Flow Optimization at Toll Plaza Using Proactive Deep Learning Strategies

An efficient algorithm for optimal route node sensing in smart tourism Urban traffic based on priority constraints

An Efficient Algorithm for Optimal Route Node Sensing in Smart Tourism Urban Traffic Based on Priority Constraints

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