Distributed Intelligent Traffic Data Processing and Analysis Based on Improved Longhorn Whisker Algorithm

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

3 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)13321-13329
Number of pages9
Journal / PublicationIEEE Transactions on Intelligent Transportation Systems
Volume24
Issue number11
Online published30 Dec 2022
Publication statusPublished - Nov 2023

Abstract

The purpose is to optimize the Beetle Antenna Search (BAS) algorithm and apply it to the Intelligent Transportation System (ITS) to process traffic data in time and solve traffic congestion. This work studies the development status of ITS and the application status of the BAS algorithm. It optimizes BAS to converge to local optimization prematurely in high-dimensional space, affecting the prediction accuracy. Then, combined with the Least Squares Support Vector Machine Algorithm (LSSVM), the algorithm with quadratic interpolation optimization is proposed. The proposed algorithm is named the Quadratic Interpolation Beetle Antenna Search (QIBAS). On this basis, a traffic flow prediction model based on QIBAS-LSSVM is established. Finally, the improved QIBAS algorithm and Traffic Flow Prediction (TFP) model are verified. The results show that the test Mean Square Error (MSE) of the TFP model based on QIBAS-LSSVM increases by 4.28%, 7.38%, and 18.23%, respectively compared with the other three models. The test Mean Absolute Percentage Error (MAPE) increases by 0.09%, 0.06%, and 0.36% respectively. The proposed QIBAS algorithm has a good effect and high accuracy in short-term TFP. The research has important reference value for the digital transformation of transportation systems in modern smart cities.

Research Area(s)

  • Beetle antenna search algorithm, intelligent transportation system, quadratic interpolation method, traffic flow prediction, least squares support vector machine, TRANSPORTATION SYSTEMS, OPTIMIZATION, VEHICLES

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