Intelligent Group Prediction Algorithm of GPS Trajectory Based on Vehicle Communication

Guobin Chen, Lukun Wang*, Muhammad Alam, Mohamed Elhoseny

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


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.

Original languageEnglish
Article number9123686
Pages (from-to)3987-3996
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number7
Publication statusPublished - Jul 2021


  • GPS
  • Universal gravitational optimization algorithm
  • extreme learning methods
  • position prediction


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