A Dueling Deep Q-Network method for low-carbon traffic signal control

交叉口(航空) 计算机科学 信号(编程语言) 温室气体 人工神经网络 卷积神经网络 控制(管理) 实时计算 汽车工程 控制理论(社会学) 人工智能 工程类 运输工程 程序设计语言 生态学 生物
作者
Leilei Kang,Weike Lu,Hao Huang,Lan Liu
出处
期刊:Applied Soft Computing [Elsevier]
卷期号:141: 110304-110304 被引量:1
标识
DOI:10.1016/j.asoc.2023.110304
摘要

Improper traffic signal control will lead to long delays for vehicles and produce massive carbon emissions. The vast vehicle exhaust emissions will pollute the environment and exacerbate the earth’s greenhouse effect. Intersection signal optimization tends to start from the traditional view of improving traffic efficiency but ignores the perspective of reducing vehicle carbon emissions. Under the framework of a deep reinforcement learning strategy, this study proposes a novel signal control method to minimize the carbon emissions of vehicles at the intersection. To associate with carbon emissions and signal control plans, the method employs the negative value of vehicle’s carbon dioxide emissions as the reward and takes the feature vectors at different time points in the two decision action intervals as the state features. The fully connected neural network, convolutional neural network, and long short-term memory network are respectively adopted to extract the state features of the decision-making period and compare their Q-value estimation effects. Through the SUMO simulation platform, the proposed signal control method is comprehensively evaluated and compared with different baseline models. It has been proved that the proposed signal control approach can not only directly reduce vehicle carbon emissions but also improve the operational efficiency of the intersection.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
完美世界应助千里采纳,获得10
1秒前
情怀应助朴素的书琴采纳,获得10
2秒前
蹦蹦发布了新的文献求助10
3秒前
夕瑶完成签到,获得积分10
3秒前
4秒前
李健的小迷弟应助jack采纳,获得10
4秒前
慕青应助微光采纳,获得20
4秒前
凩飒发布了新的文献求助100
5秒前
7秒前
wzhang完成签到,获得积分10
7秒前
归尘发布了新的文献求助20
7秒前
Cloud发布了新的文献求助10
9秒前
小绵羊的酸奶盖完成签到,获得积分10
10秒前
11秒前
迟雾发布了新的文献求助10
11秒前
11秒前
13秒前
甜甜圈完成签到 ,获得积分10
13秒前
13秒前
14秒前
14秒前
15秒前
结实涑发布了新的文献求助10
16秒前
今后应助斯文又槐采纳,获得10
16秒前
大狗砸发布了新的文献求助10
16秒前
17秒前
朝圣者发布了新的文献求助30
17秒前
guo完成签到,获得积分10
18秒前
李洛华哥发布了新的文献求助20
18秒前
充电宝应助笑忘书采纳,获得10
18秒前
就下载个文献完成签到,获得积分20
18秒前
18秒前
CipherSage应助汤谷栽扶桑采纳,获得30
18秒前
上官若男应助奋斗秋采纳,获得10
20秒前
Cloud完成签到 ,获得积分10
20秒前
千里发布了新的文献求助10
21秒前
Forty完成签到,获得积分20
21秒前
21秒前
脑洞疼应助结实涑采纳,获得10
22秒前
22秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Mechanistic Modeling of Gas-Liquid Two-Phase Flow in Pipes 2500
Structural Load Modelling and Combination for Performance and Safety Evaluation 800
Conference Record, IAS Annual Meeting 1977 610
Interest Rate Modeling. Volume 3: Products and Risk Management 600
Interest Rate Modeling. Volume 2: Term Structure Models 600
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3555252
求助须知:如何正确求助?哪些是违规求助? 3130871
关于积分的说明 9389097
捐赠科研通 2830384
什么是DOI,文献DOI怎么找? 1555991
邀请新用户注册赠送积分活动 726370
科研通“疑难数据库(出版商)”最低求助积分说明 715737