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

Sivakumar, R., Angayakanni, S.A., Ramana Rao, Y.V. and Sadiq, A. ORCID: 0000-0002-5746-0257, 2022. Traffic flow forecasting using natural selection based hybrid Bald Eagle Search - Grey Wolf Optimization algorithm. PLoS ONE. ISSN 1932-6203

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Abstract

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 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 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.

Item Type:	Journal article
Publication Title:	PLoS ONE
Creators:	Sivakumar, R., Angayakanni, S.A., Ramana Rao, Y.V. and Sadiq, A.
Publisher:	Public Library of Science
Date:	26 September 2022
ISSN:	1932-6203
Identifiers:	Number Type 10.1371/journal.pone.0275104 DOI 1599828 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	21 Sep 2022 13:49
Last Modified:	25 Sep 2023 14:48
URI:	https://irep.ntu.ac.uk/id/eprint/47064

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