Authors:
István Knáb
1
;
Bálint Pelenczei
1
;
Bálint Kővári
2
;
3
;
Tamás Bécsi
2
and
László Palkovics
1
;
4
Affiliations:
1
Systems and Control Laboratory, HUN-REN Institute for Computer Science and Control (SZTAKI), Budapest, Hungary
;
2
Department of Control for Transportation and Vehicle Systems, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Budapest, Hungary
;
3
Asura Technologies Ltd., Budapest, Hungary
;
4
Széchenyi István University, Győr, Hungary
Keyword(s):
Machine Learning, Reinforcement Learning, Deep Learning, Traffic Signal Control, Intelligent Transportation Systems.
Abstract:
During the development of modern cities, there is a strong demand articulated for the sustainability of progress. Since transportation is one of the main contributors to greenhouse gas emissions, the modernization and efficiency of transportation are key issues in the development of livable cities. Increasing the number of lanes does not always provide a solution and often is not feasible for various reasons. In such cases, Intelligent Transportation Systems are applied primarily in urban environments, mostly in the form of Traffic Signal Control. The majority of modern cities already employ adaptive traffic signals, but these largely utilize rule-based algorithms. Due to the stochastic nature of traffic, there arises a demand for cognitive decision-making that enables event-driven characteristics with the assistance of machine learning algorithms. While there are existing solutions utilizing Reinforcement Learning to address the problem, further advancements can be achieved in vario
us areas. This paper presents a solution that not only reduces emissions and enhances network throughput but also ensures universal applicability regardless of network size, owing to individually tailored state representation and rewards.
(More)