S-Edge: heterogeneity-aware, light-weighted, and edge computing integrated adaptive traffic light control framework

Sachan, Anuj; Kumar, Neetesh

doi:10.1007/s11227-023-05216-0

Cited by 3 publications

(1 citation statement)

References 45 publications

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“…Jianqi Liu et al [9] have considered only traffic density on the edge to schedule the traffic light whereas M. Bani Younes et al [25] have considered traffic speed and time required to cross intersection along with traffic density in RA. In [30], X. Liang et al have considered waiting time to allocate phase duration effectively whereas Anuj Sachan et al [31] have considered average queue length along with the waiting time. In [11], parameters like count of waiting vehicles, time required to cross an intersection and safety time are considered for deciding the cycle time of traffic light.…”

Section: Related Workmentioning

confidence: 99%

Zone Based Traffic Adaptive Mechanism for Traffic Light Control using VANET

Dave,

Panchal

2023

IJCDS

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Nowadays, the problem of traffic congestion becomes severe in almost all the countries of the world. Waste of time and money, loss of human life and negative impact on the environment are some adverse effects of traffic congestion. Currently, to minimize the impact of traffic congestion, different traffic light control mechanisms are deployed but these mechanisms are not capable of handling the problem of increased traffic efficiently. Researchers across the globe are working on an Intelligent Transport System which aims to provide hassle-free traffic movement. Many researchers have proposed dynamic solutions for controlling traffic light considering traffic density and traffic velocity within the ready or green area but almost all the researchers have taken a static value of this ready or green area and hence these solutions are performing well for specific traffic scenarios but not for all the scenarios. In this paper, a novel Zone based traffic adaptive traffic light control mechanism is proposed which efficiently calculates optimal green phase time and cycle time for a traffic light. In the proposed mechanism, for calculating the optimal value of green phase time and cycle time, ready or green area is divided into different zones. Each zone is allocated a specific multiplying factor (weight) and zone wise real-time traffic density is collected using VANET. For all the edges, priority is calculated based on the multiplying factor of the zone and traffic density within that zone. The edge with maximum priority will be allocated a green phase first. To evaluate the performance of the proposed system, a simulation testbed is prepared using SUMO traffic simulator. The simulation result shows that the proposed mechanism significantly minimizes the waiting time compared to the other dynamic mechanisms. To determine optimal value of number of zones, the performance comparison considering two, three and four zones is also presented.

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