Traffic flow forecasting using natural selection based hybrid Bald Eagle Search-Grey Wolf optimization algorithm.

Traffic flow forecasting using natural selection based hybrid Bald Eagle Search-Grey Wolf optimization algorithm.

Blog Article

In a fast-moving world, transportation consumes most of the time and resources.Traffic prediction has become a thrust application for machine learning algorithms to overcome the hurdles faced by congestion.Its accuracy determines the selection and existence of machine learning algorithms.

The accuracy of such an algorithm Vitamin B is improved better by the proper tuning of the parameters.Support Vector Regression (SVR) is a well-known prediction mechanism.This paper exploits the Hybrid Grey Wolf Optimization-Bald Eagle Search (GWO-BES) algorithm for tuning SVR parameters, wherein the GWO selection methods are of natural selection.

SVR-GWO-BES with natural selection has error performance increases by 48% in Mean Absolute Percentage Error and General Root Mean Square Error, with the help of Caltrans Performance Measurement System (PeMS) open-source data and Chennai city traffic data for traffic forecasting.It is also shown that the increasing population of search agents increases the performance.