Traffic congestion causes important problems such as delays, increased fuel consumption and additional pollution. In this paper we propose a new method to optimize traffic flow, based on reinforcement learning. We show that a traffic flow optimization problem can be formulated as a Markov Decision Process. We use Q-learning to learn policies dictating the maximum driving speed that is allowed on a highway, such that traffic congestion is reduced. An important difference between our work and existing approaches is that we take traffic predictions into account. A series of simulation experiments shows that the resulting policies significantly reduce traffic congestion under high traffic demand, and that inclusion of traffic predictions improves the quality of the resulting policies. Additionally, the policies are sufficiently robust to deal with inaccurate speed and density measurements.
Original languageEnglish
Pages (from-to)203-212
Number of pages10
JournalEngineering Applications of Artificial Intelligence
Publication statusPublished - 2016

    Research areas

  • traffic flow optimization, traffic congestion, variable speed limits, Reinforcement learning, neural networks

ID: 10329914