Abstract:
Due to the rapid growth of the population as well as the quality of life, reliance
and the use of roads is increasing drastically which leads to an inevitable
increase in traffic. As a result, many traffic control systems have been
in operation to regulate traffic but still need improvements to be more suitable
especially in our country. Indeed, these systems are designed to work
on predefined patterns which do not always correspond to the real and particular
cases of our road traffic.
In order to tackle this challenge we propose in this work an upgrade approach
of an already existing traffic monitoring model based on Deep QLearning
to traffic lights control with an agent simulator using the framework
SUMO. Our main objective is to minimize the average waiting time of
the vehicles at the intersection. To this end we propose many improvements
to the algorithm, by adding some important layers to the convolutional neural
network and adapting the values of some crucial hyper parameters.
The obtained simulation results have shown that our approach performs better
than the existing algorithm in terms of mean waiting time.
