@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00199959,
 author = {Maipradit, Arnan and Kawakami, Tomoya and Gao, Juntao and Ito, Minoru and Arnan, Maipradit and Tomoya, Kawakami and Juntao, Gao and Minoru, Ito},
 book = {第27回マルチメディア通信と分散処理ワークショップ論文集},
 month = {Nov},
 note = {Traffic congestion causes significant problems such as longer travel time, energy consumption, and air pollution. Currently, we have proposed an adaptive traffic control algorithm based on back-pressure and Q-learning to efficiently reduce congestion. In this paper, we evaluate our proposed method using the road network simulated by a real structure. The simulation results show that our algorithm significantly decreases average vehicle traveling time from 17% to 37% compared with the state-of-the-art algorithm., Traffic congestion causes significant problems such as longer travel time, energy consumption, and air pollution. Currently, we have proposed an adaptive traffic control algorithm based on back-pressure and Q-learning to efficiently reduce congestion. In this paper, we evaluate our proposed method using the road network simulated by a real structure. The simulation results show that our algorithm significantly decreases average vehicle traveling time from 17% to 37% compared with the state-of-the-art algorithm.},
 pages = {274--276},
 publisher = {情報処理学会},
 title = {Evaluation of an Adaptive Traffic Control Algorithm Based on Back-Pressure and Q-Learning},
 year = {2019}
}