中圖分類號：U491.2
文獻(xiàn)標(biāo)識碼： A
DOI： 10.19358/j.issn.2096-5133.2022.06.016
引用格式： 倪茹. 基于交叉口多維狀態(tài)評估的信號配時優(yōu)化研究[J].信息技術(shù)與網(wǎng)絡(luò)安全，2022，41(6)：102-108.

Research on signal timing optimization based on intersection multi-dimensional state evaluation

Abstract： The accurate evaluation of intersection operation status can provide quantifiable intersection information for traffic management system and provide basis for optimizing intersection signal control. In view of the current intersection operation status evaluation focuses on driving experience and is not objective, this paper determines multi-dimensional evaluation index systems combined with the needs of motor vehicles and pedestrians, and uses the AHP variation coefficient double-layer integrated weighting model to determine the weight of each index, so as to obtain the intersection operation evaluation model. Based on the evaluation model, a NoisyNet deep Q-learning reinforcement learning algorithm is used to optimize the signal timing. Taking an intersection in Hefei as an example, based on traffic simulation software, it is proved that this method can effectively alleviate traffic congestion, improve pedestrian feeling, and has high application expansibility.

Key words : traffic evaluation; indicator system; AHP-coefficient of variation; deep reinforcement learning