Impact of Connected Vehicles on Learning based Adaptive Traffic Control Systems (CROSBI ID 727632)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Miletić, Mladen ; Čakija, Dino ; Vrbanić, Filip ; Ivanjko, Edouard
engleski
Impact of Connected Vehicles on Learning based Adaptive Traffic Control Systems
Adaptive Traffic Signal Control (ATSC) systems can be implemented to reduce travel times at urban intersections by changing the signal program according to real-time traffic situations. Modern approaches to ATSC are based on Reinforcement Learning (RL) which can allow the controller to learn the control policy independently. By including the concept of Connected Vehicles (CVs), the RL-based ATSC system can use data gathered from CVs instead of traditional traffic sensors. In this paper, the impact of varying CV penetration rate on RL-based ATSC is implemented and evaluated in a simulated environment. Obtained results show that with a sufficient CVs penetration rate the RL-based ATSC systems can significantly reduce the delay of all vehicles in the traffic network.
Intelligent Transportation Systems ; Reinforcement Learning ; Connected Vehicles ; Adaptive Traffic Signal Control
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Podaci o prilogu
3311-3316.
2022.
objavljeno
10.1109/smc53654.2022.9945071
Podaci o matičnoj publikaciji
Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Institute of Electrical and Electronics Engineers (IEEE)
978-166545258-8
1062-922X
2577-1655
Podaci o skupu
2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
predavanje
09.10.2022-12.10.2022
Prag, Češka Republika
Povezanost rada
Računarstvo, Tehnologija prometa i transport