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

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  • Sivakumar R.
  • Angayarkanni S. A.
  • Ramana Rao Y. V.
  • Ali Safaa Sadiq

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.

Suggested Citation

  • Sivakumar R. & Angayarkanni S. A. & Ramana Rao Y. V. & Ali Safaa Sadiq, 2022. "Traffic flow forecasting using natural selection based hybrid Bald Eagle Search—Grey Wolf optimization algorithm," PLOS ONE, Public Library of Science, vol. 17(9), pages 1-15, September.
    • Handle: RePEc:plo:pone00:0275104
    DOI: 10.1371/journal.pone.0275104
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    References listed on IDEAS

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    1. Okutani, Iwao & Stephanedes, Yorgos J., 1984. "Dynamic prediction of traffic volume through Kalman filtering theory," Transportation Research Part B: Methodological, Elsevier, vol. 18(1), pages 1-11, February.
    2. Jayabarathi, T. & Raghunathan, T. & Adarsh, B.R. & Suganthan, Ponnuthurai Nagaratnam, 2016. "Economic dispatch using hybrid grey wolf optimizer," Energy, Elsevier, vol. 111(C), pages 630-641.
    3. Mohammed Kharrich & Salah Kamel & Mohamed H. Hassan & Salah K. ElSayed & Ibrahim B. M. Taha, 2021. "An Improved Heap-Based Optimizer for Optimal Design of a Hybrid Microgrid Considering Reliability and Availability Constraints," Sustainability, MDPI, vol. 13(18), pages 1-25, September.
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