An IoT Enabled Traffic Light Controllers Synchronization Method for Road Traffic Congestion Mitigation

Aleko, Dex R.; Djahel, Soufiene

doi:10.1109/isc246665.2019.9071667

Cited by 8 publications

(5 citation statements)

References 6 publications

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“…An IoT-based traffic signal synchronization method was proposed in [74] to reduce the traffic congestion at road intersections. This method allows the vehicles to travel a long distance by minimizing the number of STOP and GO occurrences by adapting the A self-synchronization mechanism for aerial vehicles and air traffic control (ATC) systems was introduced in [70] for avoiding collision.…”

Section: Development Status Of Synchronization Of Traffic Lightsmentioning

confidence: 99%

State-of-Art Review of Traffic Light Synchronization for Intelligent Vehicles: Current Status, Challenges, and Emerging Trends

2022

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The effective control and management of traffic at intersections is a challenging issue in the transportation system. Various traffic signal management systems have been developed to improve the real-time traffic flow at junctions, but none of them have resulted in a smooth and continuous traffic flow for dealing with congestion at road intersections. Notwithstanding, the procedure of synchronizing traffic signals at nearby intersections is complicated due to numerous borders. In traditional systems, the direction of movement of vehicles, the variation in automobile traffic over time, accidents, the passing of emergency vehicles, and pedestrian crossings are not considered. Therefore, synchronizing the signals over the specific route cannot be addressed. This article explores the key role of real-time traffic signal control (TSC) technology in managing congestion at road junctions within smart cities. In addition, this article provides an insightful discussion on several traffic light synchronization research papers to highlight the practicability of networking of traffic signals of an area. It examines the benefits of synchronizing the traffic signals on various busy routes for the smooth flow of traffic at intersections.

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Section: Development Status Of Synchronization Of Traffic Lightsmentioning

confidence: 99%

State-of-Art Review of Traffic Light Synchronization for Intelligent Vehicles: Current Status, Challenges, and Emerging Trends

2022

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“…To determine the average traffic density, it is necessary to monitor the number of vehicles passing the highway [15] Monitoring is usually carried out by observers. The observers sometime will generate errors in the calculation process due to vehicle congestion, environmental influences or internal investigators themselves [17] [18]. In addition, human calculations require separate costs which indicate the implementation becomes less efficient [8].…”

Section: Traffic Monitoringmentioning

confidence: 99%

“…In addition, human calculations require separate costs which indicate the implementation becomes less efficient [8]. Therefore, this study will also design and calculate the number of vehicles on the branch, using cameras and C ++ programming with the OpenCV library (18). OpenCV is used as its own subprogram or library which can be combined to be multiple functions in the programming which relates to digital image.…”

Section: Traffic Monitoringmentioning

confidence: 99%

Mitigating Traffic Congestion at Road Junction using Fuzzy Logic

Pohan¹,

Latiff²,

Dziyauddin³

et al. 2021

IJACSA

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The timing of traffic lights at intersections is determined by the Local Authority. It is based on the peak hour statistics and the timing is maintained even during an off-peak hour. This one standard green time will be used every time in a day regardless of the number of vehicles and the road width. This approach will have a long green traffic light even though the number of vehicles is only a few and hence, will cause a long waiting time at the intersections. Therefore, the aim of this study is to vary the timing of traffic lights at junctions according to the number of vehicles. This paper will also consider road width variable which have not been considered so far. Fuzzy logic rules will be used to classify the number of vehicles and road width and time taken for vehicles to move at the intersections which was proposed in a previous work. The new timing will commensurate with the number of vehicles and road width. Field test data were gathered from Sala Benda and Semplak intersections which are amongst the busiest intersections in Bogor, Indonesia. Comparisons were made and show that the green light timing obtained is appropriate to the two factors considered. Also the waiting time for vehicles in each traffic cycle was also reduced. This study have formulated an optimal green lights timing in each intersection and will be used by local authorities to determine the timing of green traffic lights at the intersection and hence, can implement traffic control and an appropriate waiting time.

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“…The Internet of Vehicles (IoV) in a smart city is already beginning to be practically implemented through the introduction of V2X technologies [3]. In addition to questions about what data to collect and how to organize the communication for building an Internet of vehicles, the role of tasks related to how to use this data to improve the efficiency of each autonomous vehicle working in connection with others is increasing [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Policy Learning for Multi-Agent Pathfinding

et al. 2021

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In this work we study the behavior of groups of autonomous vehicles, which are the part of the Internet of Vehicles systems. One of the challenging modes of operation of such systems is the case when the observability of each vehicle is limited and the global/local communication is unstable, e.g. in the crowded parking lots. In such scenarios the vehicles have to rely on the local observations and exhibit cooperative behavior to ensure safe and efficient trips. This type of problems can be abstracted to the socalled multi-agent pathfinding when a group of agents, confined to a graph, have to find collision-free paths to their goals (ideally, minimizing an objective function e.g. travel time). Widely used algorithms for solving this problem rely on the assumption that a central controller exists for which the full state of the environment (i.e. the agents current positions, their targets, configuration of the static obstacles etc.) is known and they can not be straightforwardly be adapted to the partially-observable setups. To this end, we suggest a novel approach which is based on the decomposition of the problem into the two sub-tasks: reaching the goal and avoiding the collisions. To accomplish each of this task we utilize reinforcement learning methods such as Deep Monte Carlo Tree Search, Q-mixing networks, and policy gradients methods to design the policies that map the agents' observations to actions. Next, we introduce the policy-mixing mechanism to end up with a single hybrid policy that allows each agent to exhibit both types of behavior -the individual one (reaching the goal) and the cooperative one (avoiding the collisions with other agents). We conduct an extensive empirical evaluation that shows that the suggested hybrid-policy outperforms standalone stat-ofthe-art reinforcement learning methods for this kind of problems by a notable margin.

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An IoT Enabled Traffic Light Controllers Synchronization Method for Road Traffic Congestion Mitigation

Cited by 8 publications

References 6 publications

State-of-Art Review of Traffic Light Synchronization for Intelligent Vehicles: Current Status, Challenges, and Emerging Trends

State-of-Art Review of Traffic Light Synchronization for Intelligent Vehicles: Current Status, Challenges, and Emerging Trends

Mitigating Traffic Congestion at Road Junction using Fuzzy Logic

Hybrid Policy Learning for Multi-Agent Pathfinding

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