基于正交差分演化无迹卡尔曼滤波的短时交通流量预测算法

doi:10.11772/j.issn.1001-9081.2015.11.3151

计算机应用 ›› 2015, Vol. 35 ›› Issue (11): 3151-3156.DOI: 10.11772/j.issn.1001-9081.2015.11.3151

• 2015年全国开放式分布与并行计算学术年会(DPCS 2015)论文 • 上一篇下一篇

基于正交差分演化无迹卡尔曼滤波的短时交通流量预测算法

袁磊¹, 梁丁文^1,2, 蔡之华², 吴钊¹, 谷琼^1,3

1. 湖北文理学院数学与计算机科学学院, 湖北襄阳 441053;
2. 中国地质大学计算机学院, 武汉 430074;
3. 西南大学逻辑与智能研究中心, 重庆 400715

收稿日期:2015-06-17 修回日期:2015-07-16 发布日期:2015-11-13
通讯作者: 谷琼(1973-),女,湖北荆门人,副教授,博士,CCF会员,主要研究方向: Web数据挖掘、网络舆情.
作者简介:袁磊(1959-),男,江苏南京人,教授,CCF会员,主要研究方向:数据库应用、智能计算; 梁丁文(1988-),男,四川南充人,硕士研究生,主要研究方向:卡尔曼滤波、演化算法; 蔡之华(1964-),男,湖北黄冈人,教授,博士生导师,博士,CCF高级会员,主要研究方向: 数据挖掘、演化计算、并行计算; 吴钊(1973-),男,湖北襄阳人,教授,博士,主要研究方向:可信计算、云计算.
基金资助:
国家自然科学基金资助项目(61172084,61272296);湖北省科技支撑计划软科学项目(2015BDH109,2015BHE029);中国博士后科学基金面上资助项目(2014M560700);襄阳市科技攻关项目.

Short-term traffic flow prediction algorithm based on orthogonal differential evolution unscented Kalman filter

YUAN Lei¹, LIANG Dingwen^1,2, CAI Zhihua², WU Zhao¹, GU Qiong^1,3

1. School of Mathematics and Computer Science, Hubei University of Arts and Science, Xiangyang Hubei 441053, China;
2. School of Computer, China University of Geosciences, Wuhan Hubei 430074, China;
3. Institute of Logic and Intelligence, Southwest University, Chongqing 400715, China

Received:2015-06-17 Revised:2015-07-16 Published:2015-11-13

摘要/Abstract

摘要： 针对复杂交通路段下的短时交通流量模型的参数估计问题,建立了基于宏观交通流量预测的状态空间模型,提出了基于正交自适应差分演化的无迹卡尔曼滤波(UKF)算法,解决交通流量预测动态模型的参数优化问题.对差分演化算法(DE)的初始化过程,使用基于正交设计和量化技术的交叉算子最大限度地提高种群的多样性,平衡差分演化算法的开采性和勘探性,更高效地搜索无迹卡尔曼滤波的模型参数.并针对UKF、DE的不同情况,分别采用不同的自适应策略提高调节算法性能.实验结果表明,相对于单独使用随机分布的方式初始化,或者根据经验设置模型参数的方法,使用正交设计方法的初始化策略、变异算子以及参数自适应控制策略的差分演化算法能够有效地节省计算资源,提升预测性能和精度,具有更高的鲁棒性.

关键词: 交通流量, 正交设计方法, 无迹卡尔曼滤波, 差分演化

Abstract: A state-space model was established for the short-term traffic flow prediction problem under complex road conditions, which is based on macroscopic traffic flow forecasting. In order to solve the problem of parameter optimization on the dynamic traffic forecast model, a method to improve the performance of Unscented Kalman Filter (UKF) with orthogonal adaptive Differential Evolution (DE) was proposed. The orthogonal method maximized the diversity of the initial population in DE algorithm. The crossover operator in DE was optimized by the orthogonal method and the technology of quantification to balance the exploitation and exploration, which was more beneficial to find the model parameters of UKF. The experimental results show that, with respect to use random distribution to initialize the parameters, or set model parameters based on the experience, the use of orthogonal design method for initialization strategy, mutation operator and adaptive control strategy of parameters in differential evolution algorithm can effectively save computing resources, improve forecasting performance and accuracy, and provide better robustness.

Key words: traffic flow, orthogonal designing method, Unscented Kalman Filter (UKF), Differential Evolution (DE)

中图分类号:

TP202
TN966

袁磊, 梁丁文, 蔡之华, 吴钊, 谷琼. 基于正交差分演化无迹卡尔曼滤波的短时交通流量预测算法[J]. 计算机应用, 2015, 35(11): 3151-3156.

YUAN Lei, LIANG Dingwen, CAI Zhihua, WU Zhao, GU Qiong. Short-term traffic flow prediction algorithm based on orthogonal differential evolution unscented Kalman filter[J]. Journal of Computer Applications, 2015, 35(11): 3151-3156.

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基于正交差分演化无迹卡尔曼滤波的短时交通流量预测算法

Short-term traffic flow prediction algorithm based on orthogonal differential evolution unscented Kalman filter

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