TRAFFIC FLOW PREDICTION USING FLOW STRENGTH INDICATORS AND DEEP LSTM NETWORK

The deployment of the Intelligent Transportation Systems (ITS) depends on the sub-directions, like speed, flow and density of the traffic prediction. The rapid growth of the real-time traffic data in the ITS have posed a major challenge in the traffic prediction model as the traffic information grows exponentially with time. Prediction of traffic flow represents to forecast the traffic data by using current data and historical data. It is the important part in smart city. Weather information and accidents are also the important reasons for traffic collapsing. Deep learning concepts are provides the assurance to recognize the traffic with high level of accuracy. These methods are used for the critical investigation in traffic prediction using the enormous set of data. In this paper, a traffic flow prediction model is developed using flow strength indicators and Deep LSTM network. The flow strength indicators are extracted using the proposed Chronological Chaotic fruitflyDeepLSTM (CCF-DeepLSTM).