Dynamic Traffic Light Control Mechanism in VANET

Paul, Ananya; Mitra, Sulata

doi:10.1109/icacci.2018.8554820

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…Managing traffic signals dynamically for metropolitan areas is one of the most vital aspects of ITS. 3,4 Due to the significant developments in edge computing, computation-intensive and latency-critical applications can be potentially executed in ITS using Internet of Things (IoT) devices. 5 IoT allows for real-time information transfer between vehicles and edge nodes, making it a viable option for reducing traffic congestion in cities.…”

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning based cooperative control of traffic signal for multi‐intersection network in intelligent transportation system using edge computing

Paul

2022

Trans Emerging Tel Tech

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In the current era, the coordination of traffic flow is hindered by the discrepancy between road infrastructure and the number of vehicles which leads to traffic congestion. One of the widely used strategies to mitigate traffic congestion is to control traffic signals with the help of deep reinforcement learning (DRL) in edge computing based intelligent transportation system. This article provides a comprehensive analysis of the most recent DRL algorithms, advantage actor‐critic and proximal policy optimization in multiple deep neural networks (DNNs), including a state‐of‐the‐art transformer model for effective traffic signal management. Here, a single DRL agent is used, which obtains the spatio‐temporal information of the traffic to identify traffic patterns from complex intersection environments. The agent uses this information as the input to the DNNs and then applies the algorithms to retrieve the essential parameters of DNN to seek an optimal action selection policy to mitigate congestion. Different real‐time maps and small city networks are explored here to determine which DNN is best suited for traffic congestion management. The simulation study reveals that both the algorithms significantly outperform the baseline. The transformer model gives the best result when compared to other DNNs. The transformer model decreases average waiting time by 96.16%, implying that it has a higher capability of dealing with congested environments.

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Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning based cooperative control of traffic signal for multi‐intersection network in intelligent transportation system using edge computing

Paul

2022

Trans Emerging Tel Tech

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An Intelligent Traffic Signal Management Strategy to Reduce Vehicles CO2 Emissions in Fog Oriented VANET

Paul

Haricharan

2021

Wireless Pers Commun

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Traffic Congestion Cause Identification Method for Urban Main Roads

Xiaoxi,

Yanping,

Lei

2023

Transportation Research Record: Journal of the Transportation R

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Identifying the causes of congestion is the key to solving traffic congestion. To improve the efficiency of congestion control, this paper establishes a congestion cause identification method based on the three stages of pattern recognition, source tracing, and cause discrimination. The K-means algorithm was proposed to calculate the frequency threshold of recurrent congestion, trace the sources of congestion according to the rules of congestion propagation time sequences, build a congestion fault tree based on causal logic relationships, and determine the occurrence probability and importance of each cause by using the expert scoring method and cloud model. The test results showed that the method is promising and could provide support for scientific congestion control.

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Dynamic Traffic Light Control Mechanism in VANET

Cited by 4 publications

References 9 publications

Deep reinforcement learning based cooperative control of traffic signal for multi‐intersection network in intelligent transportation system using edge computing

Deep reinforcement learning based cooperative control of traffic signal for multi‐intersection network in intelligent transportation system using edge computing

An Intelligent Traffic Signal Management Strategy to Reduce Vehicles CO2 Emissions in Fog Oriented VANET

Traffic Congestion Cause Identification Method for Urban Main Roads

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