research-article

Intelligent Group Prediction Algorithm of GPS Trajectory Based on Vehicle Communication

Authors:

Mohamed ElhosenyAuthors Info & Claims

IEEE Transactions on Intelligent Transportation Systems, Volume 22, Issue 7

Pages 3987 - 3996

https://doi.org/10.1109/TITS.2020.3001188

Published: 01 July 2021 Publication History

Abstract

With the rapid development of in-vehicle communication technology and the integration of big data intelligent technology, intelligent algorithms for vehicle communication used to predict traffic flow and location information have been widely used. Aiming at the problem that the gravitational algorithm is difficult to minimize the complex function and easily fall into the local optimum, this paper proposes an improved IGSA algorithm. First, a gridding algorithm is introduced to initialize the population, and under the premise of ensuring the randomness of the initial individuals, improving the ergodicity of the population is conducive to improving the quality of the solution; then, an adaptive location-based update strategy of decreasing inertia weights is proposed. this strategy inherits the advantages of linearly decreasing weights, and adaptively adjusts the weights according to the fitness value to further improve the optimization performance. The optimization simulation of 8 classic test functions shows that the IGSA algorithm is an effective algorithm for solving complex optimization problems. Finally, the IGSA algorithm is used to predict the geographic location problem in the vehicle GPS data. The IGSA algorithm is used to optimize the extreme learning method to optimize the hyperparameters and establish a vehicle GPS data prediction model. Simulation results verify the feasibility of the method.

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cover image IEEE Transactions on Intelligent Transportation Systems

IEEE Transactions on Intelligent Transportation Systems Volume 22, Issue 7

July 2021

867 pages

ISSN:1524-9050

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1558-0016 © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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IEEE Press

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Published: 01 July 2021

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Cited By

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https://dl.acm.org/doi/10.1007/s10291-024-01787-4
Zhu YYe YWu YZhao XYu JOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)SynMobProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667117(22961-22977)Online publication date: 10-Dec-2023
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Wang YZhu Z(2022)The Application of Deep Learning Model in Recruitment DecisionWireless Communications & Mobile Computing10.1155/2022/96458302022Online publication date: 1-Jan-2022
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