Performance analysis of the shortest path algorithms for intelligent transportation system

The transport or transportation sector is a legal resource that transports or carries it from place to place. Over time, we face many problems, such as transportation, high accident rates, road congestion, traffic and air pollutionetc. Because of this complexity, researchers integrate virtual technologies into transportation, known as the Intelligent Transport System.This thesis deals with the various parts of the Intelligent Transport System applications, technologies and different fields. It is assumed that the introduction of intelligent transport systems (ITS) in ground vehicles will significantly increase traffic safety and mobility. The traffic problem is one of the most important problems for people in big cities and is still going on. Too much time spent in traffic due to the intensity of arrival and exit times to work, school, etc. Due to the serious consequences of an increase in traffic jams in recent times, people are losing time in traffic jams and increasing costs. Algorithms for calculating routes are proposed to optimize vehicle routes based on real-time traffic flows or historical data. To find the shortest path, many algorithms are used, such as the Dijkstra algorithm, the A star algorithm, the Genetic algorithm, the Floyd algorithm, and the Ant colony algorithm. The purpose of this study is to find out which of the best routing algorithms are tools for achieving goals through simulation. The thesis will focus on two algorithms. Therefore, by comparing the performance analysis of the two types of algorithms, it is necessary to investigate which one reaches the least edges (ends) and reach the goal in the least cost way. It was performed using the SUMO visual simulation tool. The result shows that the A star algorithm faster than Dijkstra's algorithm.