  Deep Reinforcement Learning (DRL) uses diverse, unstructured data and makes
RL capable of learning complex policies in high dimensional environments.
Intelligent Transportation System (ITS) based on Autonomous Vehicles (AVs)
offers an excellent playground for policy-based DRL. Deep learning
architectures solve computational challenges of traditional algorithms while
helping in real-world adoption and deployment of AVs. One of the main
challenges in AVs implementation is that it can worsen traffic congestion on
roads if not reliably and efficiently managed. Considering each vehicle's
holistic effect and using efficient and reliable techniques could genuinely
help optimise traffic flow management and congestion reduction. For this
purpose, we proposed a intelligent traffic control system that deals with
complex traffic congestion scenarios at intersections and behind the
intersections. We proposed a DRL-based signal control system that dynamically
adjusts traffic signals according to the current congestion situation on
intersections. To deal with the congestion on roads behind the intersection, we
used re-routing technique to load balance the vehicles on road networks. To
achieve the actual benefits of the proposed approach, we break down the data
silos and use all the data coming from sensors, detectors, vehicles and roads
in combination to achieve sustainable results. We used SUMO micro-simulator for
our simulations. The significance of our proposed approach is manifested from
the results.
