Traffic Signal Control: a Double Q-learning Approach

• Autorzy: Agafonov Anton , Myasnikov Vladislav

Identyfikatory

DOI

10.15439/2021F109

• Konferencja: Federated Conference on Computer Science and Information Systems (16 ; 02-05.09.2021 ; online)
• Języki publikacji: EN

Abstrakty

EN

The use of information and communication technologies for solving economic, social, transportation, and other problems in the urban environment is usually considered within the "smart city" concept. Optimal traffic management is one of the key components of smart cities. In this paper, we investigate the reinforcement learning approach to solve the traffic signal control problem. Both the initial data on the connected vehicles distribution and the aggregated characteristics of traffic flows are used to describe the state of the reinforcement learning agent. Experimental studies of the proposed model were carried out on synthetic and real data using the CityFlow simulator.

Słowa kluczowe

EN

computer simulation; control engineering; computing; road traffic control; traffic engineering

PL

symulacja komputerowa; inżynieria sterowania; informatyka; kontrola ruchu drogowego; inżynieria ruchu

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2021
• Tom: Vol. 25
• Strony: 365--369
• Opis fizyczny: Bibliogr. 31 poz., wz., wykr., rys.

Twórcy

autor

Agafonov Anton

• Samara National Research University, Samara, Russia

autor

Myasnikov Vladislav

• Samara National Research University, Samara, Russia

Bibliografia

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Uwagi

Track 3: Advances in Information Systems and Technology