Route Traffic Guidance Using Deep Reinforcement Learning

Master's Thesis, 2018

50 Pages, Grade: 100.00

Abstract or Introduction

The developments of taller buildings and their growth in reduced spaces, the vehicle traffic has been increasing drastically due to the movement of the people between closer spaces. Now days the space for moving between one building and another is narrower than before, and now the density of people in the buildings has increase in relationship with the area of the building, due to the build of higher living and working buildings and towers. This is a growing issue that here we will introduce the problem that this has been creating.

As the city changes the traffic changes as well and therefore people must take more time to move from one place to another and interconnecting with other paths it’s the reason why we have many traffic jams nowadays. The Modeling of the traffic has been characterized many times, but due to many factors the resulting of the modeling is always complex or somehow not entirely complete. So in this case we are forced to neglect some factors in order to make the model usable to our purposes.

As we can see the selection of the route traffic guidance is very important to avoid the traffic jams and congestions that end up in loosing time for everyone. The selection of the route won’t only to be the shortest path but to use the best ways to avoid the concurrent traffic jams.

The estimation of the route according to the traffic state can give us a faster way to avoid traffic jams and arrive at the destination in the shortest time, saving time for us and the other cars as well, therefore releasing the stress of the traffic jams in the roads.


Route Traffic Guidance Using Deep Reinforcement Learning
Catalog Number
ISBN (eBook)
Machine learning, deep learning, reinforcement learning, deep reinforcement learning, tensorflow, multi-agent, Route traffic guidance, traffic congestion release
Quote paper
Bruno Roberto Centeno Perez (Author), 2018, Route Traffic Guidance Using Deep Reinforcement Learning, Munich, GRIN Verlag,


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