sis 18: e15

Research Article

AIoT Enabled Traffic Congestion Control System Using Deep Neural Network

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  • @ARTICLE{10.4108/eai.28-9-2021.171170,
        author={Shahan Yamin Siddiqui and Inzmam Ahmad and Muhammad Adnan Khan and Bilal Shoaib Khan and Muhammad Nadeem Ali and Iftikhar Naseer and Kausar Parveen and Hafiz Muhammad Usama},
        title={AIoT Enabled Traffic Congestion Control System Using  Deep Neural Network},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2021},
        month={9},
        keywords={Deep neural network (DNN), Traffic congestion control system, AIoT, Smart city, Machine Learning},
        doi={10.4108/eai.28-9-2021.171170}
    }
    
  • Shahan Yamin Siddiqui
    Inzmam Ahmad
    Muhammad Adnan Khan
    Bilal Shoaib Khan
    Muhammad Nadeem Ali
    Iftikhar Naseer
    Kausar Parveen
    Hafiz Muhammad Usama
    Year: 2021
    AIoT Enabled Traffic Congestion Control System Using Deep Neural Network
    SIS
    EAI
    DOI: 10.4108/eai.28-9-2021.171170
Shahan Yamin Siddiqui1,2, Inzmam Ahmad1, Muhammad Adnan Khan3, Bilal Shoaib Khan1, Muhammad Nadeem Ali4,*, Iftikhar Naseer5, Kausar Parveen6, Hafiz Muhammad Usama1
  • 1: School of Computer Science, Minhaj University, Lahore, Pakistan
  • 2: School of Computer Science, National College of Business Administration & Economics, Lahore, Pakistan
  • 3: Riphah School of Computing and Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore, Pakistan
  • 4: Department of Computer Science, Lahore Garrison University, Lahore, Pakistan
  • 5: Department of Computer Science and Information Technology, Superior University, Lahore, Pakistan
  • 6: Department of Computer Science, University of Engineering and Technology, Lahore 54890, Pakistan
*Contact email: Mnadeemali@lgu.edu.pk

Abstract

With rapid population growth in cities, to allow full use of modern technology, transportation networks need to be developed efficiently and sustainability. A significant problem in the traffic motion barrier is dynamic traffic flow. To manage traffic congestion problems, this paper provides a method for forecasting traffic congestion with the aid of a Deep neural network that minimizes blockage and plays a vital role in traffic smoothing. In the proposed model, data is collected and received by using smart Internet of things enabled devices. With the help of this model, data of the previous junction of signals will send to another junction and update after that next layer named as intelligence prediction for the congestion layer will receive data from sensors and the cloud which is used to find out the congestion point. The proposed TC2S- DNN model achieved the accuracy of 98.03 percent and miss rate of 1.97 percent which is better then previous published approaches.