Removing Traffic Congestion at Traffic Lights Using GPS Technology

被引:0
|
作者
Tanwar, Rajneesh [1 ]
Sidhu, Gursewak Singh [1 ]
Majumdar, Rana [1 ]
Srivastava, Abhishek [1 ]
机构
[1] Am Univ, Dept Informat Technol, Noida, India
来源
2016 6th International Conference - Cloud System and Big Data Engineering (Confluence) | 2016年
关键词
traffic congestion; dynamic traffic light; GPS; RTS;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In present-day the most common issues that concerns everyone is traffic congestion on road at crossings where traffic light poles are placed. The objective is to flow and maintain traffic system automatically but unfortunately jamming queue exists while everything is fine from technology point of view because of predefined static time duration. This predefined time slots fail to capture real time scenarios in terms of traffic load and that leads to congestion. To avoid such type of problem, this work contributes the concept of dynamic traffic light slots as a best possible solution. Dynamic traffic light is a concept of controlling the time duration of RED and GREEN light according to traffic flow or by sensing flow of traffic on road in movement. This can be achieved by using Global Positioning System which will convey the details of traffic flow and by considering that, time slots are provided to traffic lights i.e. whole controlling and managing will work like Real Time System.
引用
收藏
页码:575 / 579
页数:5
相关论文
共 50 条
  • [21] Measuring exposure and contribution of different types of activity travels to traffic congestion using GPS trajectory data
    Kan, Zihan
    Liu, Dong
    Yang, Xue
    Lee, Jinhyung
    JOURNAL OF TRANSPORT GEOGRAPHY, 2024, 117
  • [22] Monitoring road traffic congestion using a macroscopic traffic model and a statistical monitoring scheme
    Zerouala, Abdelhafid
    Harrou, Fouzi
    Sun, Ying
    Messai, Nadhir
    SUSTAINABLE CITIES AND SOCIETY, 2017, 35 : 494 - 510
  • [23] A Novel Algorithm for Urban Traffic Congestion Detection Based on GPS Data Compression
    Xu, Xiujuan
    Gao, Xiaobo
    Zhao, Xiaowei
    Xu, Zhenzhen
    Chang, Huajian
    PROCEEDINGS 2016 IEEE INTERNATIONAL CONFERENCE ON SERVICE OPERATIONS AND LOGISTICS, AND INFORMATICS (SOLI), 2016, : 107 - 112
  • [24] A probabilistic estimation of traffic congestion using Bayesian network
    Afrin, Tanzina
    Yodo, Nita
    MEASUREMENT, 2021, 174
  • [25] Traffic congestion monitoring using an improved kNN strategy
    Harrou, Fouzi
    Zeroual, Abdelhafid
    Sun, Ying
    MEASUREMENT, 2020, 156
  • [26] Reactive Traffic Congestion Control by Using a Hierarchical Graph
    Idwan, Sahar
    Zubairi, Junaid Ahmed
    Haider, Syed Ali
    Etaiwi, Wael
    JOURNAL OF UNIVERSAL COMPUTER SCIENCE, 2024, 30 (04) : 449 - 461
  • [27] MEASUREMENT OF TRAFFIC CONGESTION FOR INDORE
    Goliya, H. S.
    Meshram, Kundan
    Mahapatra, Suchetan
    CIVIL ENGINEERING JOURNAL-STAVEBNI OBZOR, 2020, 30 (04): : 500 - 506
  • [28] Management of traffic congestion in adaptive traffic signals using a novel classification-based approach
    Sadollah, Ali
    Gao, Kaizhou
    Zhang, Yicheng
    Zhang, Yi
    Su, Rong
    ENGINEERING OPTIMIZATION, 2019, 51 (09) : 1509 - 1528
  • [29] Evolutionary Minimization of Traffic Congestion
    Boether, Maximilian
    Schiller, Leon
    Fischbeck, Philipp
    Molitor, Louise
    Krejca, Martin S.
    Friedrich, Tobias
    IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2023, 27 (06) : 1809 - 1821
  • [30] Evolutionary Minimization of Traffic Congestion
    Boether, Maximilian
    Schiller, Leon
    Fischbeck, Philipp
    Molitor, Louise
    Krejca, Martin S.
    Friedrich, Tobias
    PROCEEDINGS OF THE 2021 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'21), 2021, : 937 - 945