Smart traffic controller using fuzzy inference system(STCFIS)

Radhika Patil, Anuradha Srinivasaraghavan
{"title":"Smart traffic controller using fuzzy inference system(STCFIS)","authors":"Radhika Patil, Anuradha Srinivasaraghavan","doi":"10.1109/NGCT.2016.7877437","DOIUrl":null,"url":null,"abstract":"The heavy traffic congestion problem in major cities and towns is mainly attributed to the time spend across crowded traffic junctions especially during peak hours. The traffic across junctions is controlled by signaling patterns, where the signal duration is invariably static in nature. The traffic density across either sides of the road does not affect the duration of signal. For reducing the time taken in waiting across traffic junction, the proposed work aims at simulating a dynamic traffic signal using fuzzy logic which would change the timing of the green signal with respect to the intensity of traffic and feed this to an arduino microcontroller based fuzzy inference system which decides the duration of the green signal. The intensity of traffic is computed using two methods, feature detection and image subtraction methods. The feature detection method takes lot of processing time and image subtraction method along with fuzzy logic was found more suitable in dynamically controlling the duration of traffic signals.","PeriodicalId":326018,"journal":{"name":"2016 2nd International Conference on Next Generation Computing Technologies (NGCT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 2nd International Conference on Next Generation Computing Technologies (NGCT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NGCT.2016.7877437","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

Abstract

The heavy traffic congestion problem in major cities and towns is mainly attributed to the time spend across crowded traffic junctions especially during peak hours. The traffic across junctions is controlled by signaling patterns, where the signal duration is invariably static in nature. The traffic density across either sides of the road does not affect the duration of signal. For reducing the time taken in waiting across traffic junction, the proposed work aims at simulating a dynamic traffic signal using fuzzy logic which would change the timing of the green signal with respect to the intensity of traffic and feed this to an arduino microcontroller based fuzzy inference system which decides the duration of the green signal. The intensity of traffic is computed using two methods, feature detection and image subtraction methods. The feature detection method takes lot of processing time and image subtraction method along with fuzzy logic was found more suitable in dynamically controlling the duration of traffic signals.
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
基于模糊推理系统的智能交通控制器(STCFIS)
主要城市和城镇的严重交通拥堵问题主要是由于人们在拥挤的交通枢纽上花费的时间,尤其是在高峰时段。交叉路口的交通由信号模式控制,信号持续时间本质上总是静态的。道路两侧的交通密度不影响信号的持续时间。为了减少在交通路口等待所花费的时间,提出的工作旨在使用模糊逻辑模拟动态交通信号,该信号将根据交通强度改变绿色信号的时间,并将其馈送到基于arduino微控制器的模糊推理系统,该系统决定绿色信号的持续时间。采用特征检测和图像减法两种方法计算交通强度。特征检测方法需要大量的处理时间,图像减法结合模糊逻辑更适合于交通信号持续时间的动态控制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
SCADA security issues and FPGA implementation of AES — A review Real-time analysis and visualization of online social media dynamics An advanced clustering scheme for wireless sensor networks using particle swarm optimization Physical telepresence: Growth trends of Tangible User Interface and its future Capital market forecasting by using sentimental analysis
×
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