IoT-Based Model in Smart Urban Traffic Control: Graph theory and Genetic Algorithm

Abstract

Environmental pollution and urban dissatisfaction with traffic are the biggest challenges in metropolitan cities. Nevertheless, the inevitability of allocating roads for temporary and sometimes long periods to specific issues such as meetings, conferences, accidents, etc. could cause traffic on the surrounding roads. One of the solutions to reduce traffic on these roads is to make some roads one-way or two-way depending on their properties, where urban communication is not disrupted. In this paper, we presented an approach to employ the Internet of Things (IoT) to detect traffic information and create weighted dependency graphs to minimize the amount of free road traffic. To model the urban roads, we consider a directed graph in which each edge represents the stream. Then the optimal directed graph obtained using the genetic algorithm represented the traffic model of the vehicles. According to the car’s location and destination, the optimal path was announced to the driver via the car internet. This method improved the average waiting time and queue length.