Short-term traffic flow prediction based on multivariable phase space reconstruction and LSSVM

被引：3

作者：

Han, Fengqing ^{[1
]}

Zhang, Duo ^{[1
]}

机构：

[1] School of Management, Chongqing Jiaotong University

来源：

Journal of Information and Computational Science | 2014年 / 11卷 / 09期

关键词：

Chaos; Multivariable; Phase space reconstruction; Traffic flow prediction;

D O I：

10.12733/jics20103884

中图分类号：

学科分类号：

摘要：

Real-time and accurate short-term traffic flow prediction is the premise and key of intelligent traffic control and guidance system. According to this problem, this paper put forward a prediction model based on multivariable phase space reconstruction and Least Squares Support Vector Machine (LSSVM). Firstly, the model confirms embedding dimension and delay time of the traffic flow, occupancy and average speed time series by analyzing their chaotic characteristics, and reconstructs multivariable state space. Secondly, the phase points obtained after reconstruction are as input, and the last traffic flow parameters came from the following phase points are as output. Finally, the LSSVM which is trained is adapted to realize short-term traffic flow prediction. This research compares this model with a model based on univariate phase space reconstruction and LSSVM, and the results show that the model proposed in this paper predicts better. Copyright © 2014 Binary Information Press.

引用

页码：3209 / 3217

页数：8

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