2026-03-09T12:52:11Z https://ipsj.ixsq.nii.ac.jp/oai

oai:ipsj.ixsq.nii.ac.jp:00199959 2025-01-19T21:29:41Z 6164:6165:7006:9954

Evaluation of an Adaptive Traffic Control Algorithm Based on Back-Pressure and Q-Learning Evaluation of an Adaptive Traffic Control Algorithm Based on Back-Pressure and Q-Learning Maipradit, Arnan Kawakami, Tomoya Gao, Juntao Ito, Minoru Arnan, Maipradit Tomoya, Kawakami Juntao, Gao Minoru, Ito 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. conference paper 情報処理学会 2019-11-04 application/pdf 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 274 276 https://ipsj.ixsq.nii.ac.jp/record/199959/files/IPSJ-DPSWS2019045.pdf eng