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

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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.

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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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Authors and Affiliations

  1. College of Data Science, Taiyuan University of Technology, Taiyuan, China

    Chuan Luo & Chi Huang

  2. School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu, China

    Chi Huang

  3. School of Mathematics, Southeast University, Nanjing, China

    Chi Huang, Jinde Cao & Jianquan Lu

  4. Intelligent Transportation System Research Center, Southeast University, Nanjing, China

    Wei Huang & Jianhua Guo

  5. National Engineering Laboratory for Green and Safe Construction Technology in Urban Rail Transit, Beijing, China

    Yun Wei

Authors
  1. Chuan Luo
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  2. Chi Huang
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  3. Jinde Cao
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  4. Jianquan Lu
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  5. Wei Huang
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Correspondence to Chi Huang.

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Luo, C., Huang, C., Cao, J. et al. Short-Term Traffic Flow Prediction Based on Least Square Support Vector Machine with Hybrid Optimization Algorithm. Neural Process Lett 50, 2305–2322 (2019). https://doi.org/10.1007/s11063-019-09994-8

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