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Neural Processing Letters

, Volume 50, Issue 3, pp 2305–2322 | Cite as

Short-Term Traffic Flow Prediction Based on Least Square Support Vector Machine with Hybrid Optimization Algorithm

  • Chuan Luo
  • Chi HuangEmail author
  • Jinde Cao
  • Jianquan Lu
  • Wei Huang
  • Jianhua Guo
  • Yun Wei
Article

Abstract

Accurate short-term traffic flow prediction plays an indispensable role for solving traffic congestion. However, the structure of traffic data is nonlinear and complicated. It is a challenge to get high precision. The least square support vector machine (LSSVM) has powerful capabilities for time series and nonlinear regression prediction problems if it can select appropriate parameters. To search the optimal parameters of LSSVM, this paper proposes a hybrid optimization algorithm which combines particle swarm optimization (PSO) with genetic algorithm. The main contributions are twofold: (1) A hybrid optimization method is proposed, which can skip the local optimal pitfall with less learning time by introducing a selection strategy, crossover and mutation operators into PSO; (2) the crossover and mutation operators are controlled by adaptive probability functions. The crossover and mutation probabilities increase when the population fitness is concentrated, and decrease when the fitness is dispersed. It can effectively improve the precision and speed of convergence. The proposed model is verified based on the measured data. The experimental results show that our new model yields better prediction ability and relatively high computational efficiency compared with other related models.

Keywords

Least square support vector machine Traffic flow prediction Particle swarm optimization Genetic algorithm 

Notes

Acknowledgements

This work was jointly supported by the National Science Foundation of China under Grants 61603268, 61272530, 61573096 and 61573102, the Shanxi province plan project on Science and Technology of Social Development under Grant 201703D321032.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Chuan Luo
    • 1
  • Chi Huang
    • 1
    • 2
    • 3
    Email author
  • Jinde Cao
    • 3
  • Jianquan Lu
    • 3
  • Wei Huang
    • 4
  • Jianhua Guo
    • 4
  • Yun Wei
    • 5
  1. 1.College of Data ScienceTaiyuan University of TechnologyTaiyuanChina
  2. 2.School of Economic Information EngineeringSouthwestern University of Finance and EconomicsChengduChina
  3. 3.School of MathematicsSoutheast UniversityNanjingChina
  4. 4.Intelligent Transportation System Research CenterSoutheast UniversityNanjingChina
  5. 5.National Engineering Laboratory for Green and Safe Construction Technology in Urban Rail TransitBeijingChina

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