AIoT Enabled Traffic Congestion Control System Using Deep Neural Network

Siddiqui, Shahan Yamin and Ahmad, Inzmam and Khan, Muhammad Adnan and Khan, Bilal Shoaib and Ali, Muhammad Nadeem and Naseer, Iftikhar and Parveen, Kausar and Usama, Hafiz Muhammad (2021) AIoT Enabled Traffic Congestion Control System Using Deep Neural Network. EAI Endorsed Transactions on Scalable Information Systems, 8 (33). e7. ISSN 2032-9407

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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.

Item Type: Article
Uncontrolled Keywords: Deep neural network (DNN), Traffic congestion control system, AIoT, Smart city, Machine Learning
Subjects: Q Science > Q Science (General)
Depositing User: EAI Editor IV
Date Deposited: 08 Nov 2021 07:20
Last Modified: 08 Nov 2021 07:20
URI: https://eprints.eudl.eu/id/eprint/8020

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