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A fog-based Traffic Light Management Strategy (TLMS) based on fuzzy inference engine


Urban mobility is one of the critical challenges in modern cities that should be tackled carefully. The exponential growth of cars number badly impacts the transportation system (TS) on which most cities are living on. Traffic control is one of the most critical issues in TS, which depends on a set of cooperative traffic lights. Smart Traffic lights, which can receive and analyze traffic data, can solve traffic problems by efficiently predict the accurate waiting time for each traffic lane at the intersections. This can improve the traffic flow and accordingly promotes the transportation system performance. This paper introduces a Fog-Based Traffic Light Management Strategy (TLMS) based on Fuzzy Inference Engine. TLMS can accurately calculate the optimal waiting time for each traffic lane at the intersections to decrease the average waiting time for the stopped vehicles. TLMS applies Vehicle to infrastructure protocol (V2I) that allows vehicles to interact directly with the infrastructure of the road such as GPS sensors and the traffic light signals. At each traffic intersection, the number of waiting vehicles, their locations relative to TLMS, and their sizes are detected and sent to TLMS in real time. Then, based on fuzzy inference, TLMS can calculate the optimal waiting time for each lane, which optimizes the traffic flow at the intersection by minimizing the waiting time for the vehicles. The performance of the proposed TLMS has been compared and tested against recently proposed techniques via simulation. The results of the experiment showed that TLMS outperforms recent technologies as it minimizes the average waiting time of vehicles around the intersections and accordingly maximizes the performance of the traffic system.

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Correspondence to Samah A. Gamel.

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Gamel, S.A., Saleh, A.I. & Ali, H.A. A fog-based Traffic Light Management Strategy (TLMS) based on fuzzy inference engine. Neural Comput & Applic 34, 2187–2205 (2022).

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