A Framework for Smart Traffic Controller by Improved Cooperative Multi-agent Learning Algorithms (ICMALA)

Abstract

Traffic jams are common as a result of the heavy traffic caused by the vast number of cars on the road. Despite the fact that traffic congestion is prevalent nowadays, enhancing the effectiveness of traffic signal control for effective traffic management is an important goal. The goals of a cooperative intelligent traffic manage scheme are to increase transportation movement and decrease the common wait time of every vehicle. Each signal aspires to make a more efficient journey motion. Throughout the course, signals build a cooperative strategy as well as a constraint for adjacent signals to maximize their particular benefits. However, although most current traffic management schemes rely on simple heuristics, a more effective traffic regulator can be researched using multi-agent reinforcement learning, where in each agent is in charge of only traffic light. The traffic controller model may be influenced by a number of variables. Learning the best feasible result is difficult. Agents in earlier methods chose only the most favorable actions that were close by without cooperating in their activities. Traffic light controllers are not trained to analyze previous data. Due to this, they are not capable to account for the unpredictable shift of traffic flow. A traffic controller model using reinforcement learning was used to obtain fine timing rules by appropriately describing real-time features of the real-world traffic scenario. This research broadens the scope of this technique to include clear cooperation between adjacent traffic lights. The proposed real-time traffic controller prototype can successfully follow traffic signal scheduling guidelines. The model learns and sets up the ideal actions by expanding the vehicle's traffic value, which includes delay time, the number of vehicles halted at a signal, and newly incoming vehicles. The experimentation results show a significant improvement in traffic management, proving that the projected prototype is smart enough for providing real-time dynamic traffic management.