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

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 accu...

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Veröffentlicht in:PloS one Jg. 17; H. 9; S. e0275104
Hauptverfasser: R., Sivakumar, S. A., Angayarkanni, Y. V., Ramana Rao, Sadiq, Ali Safaa
Format: Journal Article
Sprache:Englisch
Veröffentlicht: San Francisco Public Library of Science 26.09.2022
Public Library of Science (PLoS)
Schlagworte:
Accuracy
Algorithms
Analysis
Biology and Life Sciences
Computer and Information Sciences
Data mining
Ecology and Environmental Sciences
Engineering and Technology
Errors
Forecasting
Forecasts and trends
Genetic algorithms
Haliaeetus leucocephalus
Learning algorithms
Literature reviews
Machine learning
Mathematical optimization
Methods
Natural selection
Neural networks
Optimization
Optimization algorithms
Optimization techniques
Parameters
Performance measurement
Physical Sciences
Population
Population growth
Research and Analysis Methods
Searching
Social Sciences
Support vector machines
Time series
Traffic
Traffic congestion
Traffic flow
Traffic information
Transportation planning
Tuning
United Kingdom
ISSN:1932-6203, 1932-6203
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Beschreibung
Zusammenfassung: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.
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Competing Interests: The authors have declared that no competing interests exist
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0275104