An Intelligent Vehicular Traffic Flow Prediction Model Using Whale Optimization with Multiple Linear Regression

At present, vehicular traffic flow prediction is treated as a crucial issue in the intelligent transportation system. It mainly focuses on the estimation of vehicular traffic flow on roadways or stations in the subsequent time interval ahead of the future. Generally, traffic flow prediction comprises two major stages, namely feature learning and predictive modeling. In this view, this paper introduces an Intelligent Vehicular Traffic Flow Prediction (IVTFP) model to effectively predict the flow of traffic on the road. The proposed IVTFP model involves two main stages, namely feature selection (FS) and classification. At the first level, the whale optimization algorithm (WOA) is applied as a feature selector called WOA-FS to select the useful subset of features. Next, in the second level, the multiple linear regression (MLR) technique is utilized as a prediction model to forecast the traffic flow. The performance of the IVTFP model takes place on the benchmark Brazil dataset. The simulation outcome indicated the effective outcome of the IVTFP model, and it ensured that the application of the WOA-FS model helps attain improved classification outcomes.

Keywords: Classification, Computational Intelligence, Feature Selection, Predictive Modeling, Traffic Flow Prediction.