层次聚类在深度强化学习控制交通网络中的应用

Fady Taher, A. Elmahalawy, A. Shouman, A. El-Sayed
{"title":"层次聚类在深度强化学习控制交通网络中的应用","authors":"Fady Taher, A. Elmahalawy, A. Shouman, A. El-Sayed","doi":"10.21608/mjeer.2020.22756.1003","DOIUrl":null,"url":null,"abstract":"Traffic congestions is a crucial problem affectingcities around the globe and they are only getting worse as thenumber of vehicles tends to increase significantly. Traffic signalcontrollers are considered as the most important mechanism tocontrol traffic, specifically at intersections, the field of MachineLearning introduces advanced techniques which can be appliedto provide more flexibility and adaptiveness to traffic controltechniques. Efficient traffic controllers can be designed using areinforcement learning (RL) approach but major problems offollowing RL approach are, exponential growth in the state andaction spaces and the need for coordination. We use real trafficdata of 65 intersection of the city of Ottawa to build oursimulations and show that, clustering the network usinghierarchal techniques has a great potential in reducing the stateactionpair significantly and enhance overall trafficperformance.","PeriodicalId":218019,"journal":{"name":"Menoufia Journal of Electronic Engineering Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Applying Hierarchal Clusters on Deep Reinforcement Learning Controlled Traffic Network\",\"authors\":\"Fady Taher, A. Elmahalawy, A. Shouman, A. El-Sayed\",\"doi\":\"10.21608/mjeer.2020.22756.1003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traffic congestions is a crucial problem affectingcities around the globe and they are only getting worse as thenumber of vehicles tends to increase significantly. Traffic signalcontrollers are considered as the most important mechanism tocontrol traffic, specifically at intersections, the field of MachineLearning introduces advanced techniques which can be appliedto provide more flexibility and adaptiveness to traffic controltechniques. Efficient traffic controllers can be designed using areinforcement learning (RL) approach but major problems offollowing RL approach are, exponential growth in the state andaction spaces and the need for coordination. We use real trafficdata of 65 intersection of the city of Ottawa to build oursimulations and show that, clustering the network usinghierarchal techniques has a great potential in reducing the stateactionpair significantly and enhance overall trafficperformance.\",\"PeriodicalId\":218019,\"journal\":{\"name\":\"Menoufia Journal of Electronic Engineering Research\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Menoufia Journal of Electronic Engineering Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21608/mjeer.2020.22756.1003\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Menoufia Journal of Electronic Engineering Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21608/mjeer.2020.22756.1003","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

交通拥堵是影响全球城市的一个关键问题,随着车辆数量的显著增加,交通拥堵只会变得越来越严重。交通信号控制器被认为是控制交通的最重要的机制,特别是在十字路口,机器学习领域引入了先进的技术,可以为交通控制技术提供更多的灵活性和适应性。有效的交通控制器可以使用强化学习(RL)方法来设计,但RL方法的主要问题是,状态和行动空间的指数增长以及对协调的需求。我们使用渥太华市65个十字路口的真实交通数据来构建我们的模拟,并表明,使用分层技术聚类网络在显著减少状态对和提高整体交通性能方面具有很大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Applying Hierarchal Clusters on Deep Reinforcement Learning Controlled Traffic Network
Traffic congestions is a crucial problem affectingcities around the globe and they are only getting worse as thenumber of vehicles tends to increase significantly. Traffic signalcontrollers are considered as the most important mechanism tocontrol traffic, specifically at intersections, the field of MachineLearning introduces advanced techniques which can be appliedto provide more flexibility and adaptiveness to traffic controltechniques. Efficient traffic controllers can be designed using areinforcement learning (RL) approach but major problems offollowing RL approach are, exponential growth in the state andaction spaces and the need for coordination. We use real trafficdata of 65 intersection of the city of Ottawa to build oursimulations and show that, clustering the network usinghierarchal techniques has a great potential in reducing the stateactionpair significantly and enhance overall trafficperformance.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Classification of Brain Neuroimaging for Alzheimer's Disease Employing Principal Component Analysis DICOM Medical Image Security with DNA- Non-Uniform Cellular Automata and JSMP Map Based Encryption Technique Photonic Crystal Fiber Sensors, Literature Review, Challenges, and Some Novel Trends Cascading ensemble machine learning algorithms for maize yield level prediction Vibration Control of Horizontally Supported Jeffcott-Rotor System Utilizing PIRC-controller
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1