An Efficient Adaptive Traffic Light Control System for Urban Road Traffic Congestion Reduction in Smart Cities

Abstract

Traffic lights have been used for decades to control and manage traffic flows crossing road intersections to increase traffic efficiency and road safety. However, relying on fixed time cycles may not be ideal in dealing with the increasing congestion level in cities. Therefore, we propose a new Adaptive Traffic Light Control System (ATLCS) to assist traffic management authorities in efficiently dealing with traffic congestion in cities. The main idea of our ATLCS consists in synchronizing a number of traffic lights controlling consecutive junctions by creating a delay between the times at which each of them switches to green in a given direction. Such a delay is dynamically updated based on the number of vehicles waiting at each junction, thereby allowing vehicles leaving the city centre to travel a long distance without stopping (i.e., minimizing the number of occurrences of the ‘stop and go’ phenomenon), which in turn reduces their travel time as well. The performance evaluation of our ATLCS has shown that the average travel time of vehicles traveling in the synchronized direction has been significantly reduced (by up to 39%) compared to non-synchronized fixed time Traffic Light Control Systems. Moreover, the overall achieved improvement across the simulated road network was 17%.

Distributed Finite-Time Consensus Control of Second-Order Multiagent Systems Subject to Communication Time Delay

“…Equation ( 8) represents the relative state information between agent i and its neighbor agent j. In equation (7), sig(x) k � |x| k sign(x), where sign(x) is a symbolic function.…”

“…Consider the leaderless system with equation (2). Suppose G has a directed spanning tree and that control protocol (7) can make the MAS achieve finite-time consensus in the time-delay case.…”

“…In recent years, the consensus control [1][2][3][4] of the multiagent system has attracted great attention, which has been widely applied in the fields of UAV formation [5], intelligent transportation system [6,7], satellite attitude calibration [8,9], aerospace, and other fields. e development of the multiagent system comes from people's research on the overall consensus behavior of biological clusters in nature, such as ants moving, birds flying in clusters, and so on.…”

PALM: Platoons Based Adaptive Traffic Light Control System for Mixed Vehicular Traffic

“…Multi-agent reinforcement learning algorithms are exploited in [18]- [21], where the reactions by local and nearby intersections are considered to adjust the traffic lights timing. Moreover, Aleko et al synchronize multiple traffic lights at consecutive junctions to reduce traffic congestion based on the dynamic updates of information obtained from vehicles waiting at each intersection [22].…”

SCARE: A Novel Switching and Collision Avoidance pRocEss for Connected Vehicles Using Virtualization and Edge Computing Paradigm

“…Here, the MEC architecture is able to reduce the amount of data to be sent to the cloud, processing critical data, sensitive to latency, at the origin point, via smart devices, or sending it to an intermediate server, located nearby; the less ’time-sensitive’ data can instead be transmitted to the cloud infrastructure or data center, to allow more complex processing, such as big data analysis. In this context, particular attention has to be addressed to congestion reduction (e.g., [ 4 , 5 ]), routing (e.g., [ 6 ]) and mobility prediction (e.g., [ 7 ]).…”