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

Rapid increase in the private and public vehicles fleet causes urban centers heavily populated with limited transport road infrastructure. To overcome this, in real-time scenarios, queue length-based traffic light controllers are being designed utilizing light-weighted S-Edge devices. This system suffers from starvation problems if a road lane at the intersection continuously receives vehicles during peak hours. With this, higher green phase duration can be allocated to the same-lane multiple times despite vehicles on the other lanes' longer waiting time. To tackle this problem, an efficient and smart edge computing (S-Edge)-driven traffic light controller is proposed by accounting the real-time heterogeneous vehicular dynamics at the fog computing node. The fog node executes the proposed fuzzy inference system to generate phase-cycle duration. Further, to allocate the phase duration effectively, a method for estimating the lane pressure is proposed for the edge controller utilizing average queue length and waiting time. S-Edge is a light-weighted actuated traffic light controller that generates traffic light control cycle duration and phase (red/yellow/green) duration. To validate the S-Edge controller, a prototype is developed on an Indian city OpenStreetMap utilizing the low-computing power IoT devices, i.e., Raspberry Pi, and a well-known open-source simulator, i.e., Simulation of Urban MObility. [ABSTRACT FROM AUTHOR]