Multi-agent reinforcement learning for route guidance system

Nowadays, multi-agent systems are used to create applications in a variety of areas, including economics, management, transportation, telecommunications, etc. Importantly, in many domains, the reinforcement learning agents try to learn a task by directly interacting with its environment. The main challenge in route guidance system is to direct vehicles to their destination in a dynamic traffic situation, with the aim of reducing travel times and ensuring efficient use of available road network capacity. This paper proposes a multi-agent reinforcement learning algorithm to find the best and shortest path between the origin and destination nodes. The shortest path such as the lowest cost is calculated using multi-agent reinforcement learning model and it will be suggested to the vehicle drivers in a route guidance system. The proposed algorithm has been evaluated based on Dijkstra's algorithm to find the optimal solution using Kuala Lumpur (KL) road network map. A number of route cases have been used to evaluate the proposed approach based on the road network problems. Finally, the experiment results demonstrate that the proposed approach is feasible and efficient

Route guidance system based on self adaptive multiagent algorithm

Nowadays, self-adaptive multi-agent systems are applied in a variety of areas, including transportation, telecommunications, etc. The main challenge in route guidance system is to direct vehicles to their destination in a dynamic traffic situation, with the aim of reducing the traveling time and to ensure and efficient use of available road networks capacity. In this paper we propose a self-adaptive multi-agent algorithm for managing the shortest path routes that will improve the acceptability of the costs between the origin and destination nodes. The proposed algorithms have been compared with Dijkstra algorithm in order to find the best and shortest paths using a sample of Tehran road network map. Two cases have been tested on the simulation using the proposed algorithm. The experimental results demonstrate that the proposed algorithm could reduce the cost of vehicle routing problem

Route guidance system based on self-adaptive algorithm

Self-adaptive systems are applied in a variety of ways, including transportation, telecommunications, etc. The main challenge in route guidance system is to direct vehicles to their destination in a dynamic traffic situation, with the aim of reducing the motoring time and to ensure an efficient use of available road resources. In this paper, we propose a self-adaptive algorithm for managing the shortest paths in route guidance system. This is poised to minimize costs between the origin and destination nodes. The proposed algorithm was compared with the Dijkstra algorithm in order to find the best and shortest paths using a sample simplified real sample of Kuala-Lumpur (KL) road network map. Four cases were tested to verify the efficiency of our approach through simulation using the proposed algorithm. The results show that the proposed algorithm could reduce the cost of vehicle routing and associated problems