Non-linear analysis of traffic flow

被引：37

作者：

Nair, AS ^{[1
]}

Liu, JC ^{[1
]}

Rilett, L ^{[1
]}

Gupta, S ^{[1
]}

机构：

[1] Texas A&M Univ, Dept Comp Sci, College Stn, TX 77843 USA

来源：

2001 IEEE INTELLIGENT TRANSPORTATION SYSTEMS - PROCEEDINGS | 2001年

关键词：

phase space embedding; chaos; time delay neural network; Lyapunov exponent; time series prediction;

D O I：

10.1109/ITSC.2001.948742

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];

摘要：

Traffic flow prediction is an important application of the ITS technology. In this paper, we applied non-linear time-series modeling techniques to analyze a traffic data. Our objective is to investigate the deterministic properties of traffic flow using a nonlinear time series analysis technique. The experiment is performed for inductance loop data collected from the San Antonio freeway system. Our study concludes that the traffic data exhibits chaotic properties and techniques based on phase space dynamics can be used to analyze and predict the traffic flow.

引用

页码：681 / 685

页数：5

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