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.
