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