Computer Engineering and Applications ›› 2013, Vol. 49 ›› Issue (17): 226-229.

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Short-time traffic prediction model based on LSSVM optimized by artificial fish swarm algorithm

LIU Jing   

  1. Institute of Software and Service Outsourcing, Chien-shiung Institute of Technology, Taicang, Jiangsu 215411, China
  • Online:2013-09-01 Published:2013-09-13


刘  静   

  1. 健雄职业技术学院 软件与服务外包学院,江苏 太仓 215411

Abstract: In order to improve the prediction accuracy of short-term traffic flow, in view of parameters optimization problem for Least Squares Support Vector Machine(LSSVM), this paper proposes a short-term traffic prediction model based on Artificial Fish Swarm Algorithm(AFSA) and LSSVM(AFSA-LSSVM) which LSSVM parameters are optimized by improved AFSA, and the simulation experiment is carried out based on short-term traffic flow data. The simulation results show that the proposed model can obtain better LSSVM parameters and can more accurately reflect the changes of short-term traffic flow, and improves the short-term traffic prediction accuracy compared with the reference model, and it provides a new research method for the nonlinear short-term traffic flow prediction.

Key words: short-time traffic flow, least squares support vector machine, artificial fish swarm algorithm, time series

摘要: 为了提高短时交通流量的预测精度,针对最小二乘支持向量机(LSSVM)参数优化难题,提出一种人工鱼群算法(AFSA)和LSSVM相结合的短时交流量预测模型(AFSA-LSSVM),通过采用AFSA优化LSSVM参数,并采用具体短时交通流量数据进行仿真实验。仿真结果表明,相对于参比模型,AFSA-LSSVM可以获得更优的LSSVM参数,能够更加准确地描述短时交通流量变化趋势,提高了短时交通量的预测精度,为非线性短时交通流量预测提供了一种新的研究思路。

关键词: 短时交通流量, 最小二乘支持向量机, 人工鱼群算法, 时间序列