Optimizing path planning for reduced congestion using reinforcement learning

Overpopulation in urbanized areas has been causing traffic density to increase and push the boundaries of the existing infrastructure units. Especially during peak hours, many people suffer from the congestion they encounter in their everyday commute. Aside from the time being spent on the road, traffic also causes air pollution, increased gas consumption, and even negative psychological impacts. Expanding existing roads with new lines, or promoting public transportation do not balance out the increasing congestion, and there is a need for an alternative approach to path planning. Autonomous driving technology, driving assistance software, increased IoT installations in road infrastructure, and the accessibility of the internet make modern vehicles smarter each day and revolutionize the transportation industry. Today, many drivers make their route planning based on path suggestion applications using GPS. With the help of these applications, drivers can see the least congested and fastest routes possible based on their destination locations. However, these applications provide individual-optimum solutions for the driver and may fail to estimate congestion correctly due to the lack of other vehicles' path information. Following the literature in the multi-agent path-finding domain, a new solution is proposed for the congestion problem. The solution is based on prioritizing system optimality and increasing the utilization of existing road networks. By using a reinforcement learning algorithm and a scalable orchestration architecture it is shown that overall congestion is reduced and less time is spent on the roads compared with individual-oriented solutions.