Grey Wolf Optimizer with Deep Learning based Short Term Traffic Forecasting in Smart City Environment

Intelligent Transportation System (ITS) is one of the revolutionary technologies in smart cities that aids in minimizing traffic congestion and improving traffic quality. ITS provides real-time analysis and very effective traffic management by utilizing big data and communication technology. Traffic Flow Prediction (TFP) becomes a dynamic component in smart city management and was utilized for predicting the future traffic conditions on transportation networks relevant to past data. Machine Learning (ML) and Neural Network (NN) techniques can be broadly used in resolving real-time problems as these techniques are capable of managing adaptive data for some time. Deep Learning (DL) is a sub-divison of ML methods which earns effective performance on prediction and data classification tasks. This article designs a Grey Wolf optimizer with Deep Learning Based Short Term Traffic Forecasting (GWODL-STTF) in smart city environment. The presented GWODL-STTF technique concentrates on the prediction of traffic flow in smart cities. The presented GWODL-STTF technique involves two major processes. At the initial stage, the GWODL-STTF technique employed gated recurrent unit-neural network (GRU-NN) model to forecast traffic flow. Next, in the second stage, the GWODLSTTF technique makes use of GWO algorithm as a hyperparameter optimizer. The simulation values of the GWODL-STTF method can be tested under several metrics and the outcomes show the significant performance of the GWODLSTTF method over recent approaches with minimum MSE of 105.627.