Non-linear analysis of traffic flow

A. S. Nair; J. C. Liu; L. Rilett; S. Gupta

Non-linear analysis of traffic flow

A. S. Nair, J. C. Liu, L. Rilett, S. Gupta

Research output: Contribution to conference › Paper › peer-review

Abstract

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.

Original language	English (US)
Pages	681-685
Number of pages	5
State	Published - 2001
Externally published	Yes
Event	2001 IEEE Intelligent Transportation Systems Proceedings - Oakland, CA, United States Duration: Aug 25 2001 → Aug 29 2001

Other

Other	2001 IEEE Intelligent Transportation Systems Proceedings
Country	United States
City	Oakland, CA
Period	8/25/01 → 8/29/01

Keywords

Chaos
Lyapunov exponent
Phase space embedding
Time delay neural network
Time series prediction

ASJC Scopus subject areas

Automotive Engineering
Mechanical Engineering
Computer Science Applications

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Cite this

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title = "Non-linear analysis of traffic flow",

abstract = "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.",

keywords = "Chaos, Lyapunov exponent, Phase space embedding, Time delay neural network, Time series prediction",

author = "Nair, {A. S.} and Liu, {J. C.} and L. Rilett and S. Gupta",

year = "2001",

language = "English (US)",

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note = "2001 IEEE Intelligent Transportation Systems Proceedings ; Conference date: 25-08-2001 Through 29-08-2001",

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AU - Liu, J. C.

AU - Rilett, L.

AU - Gupta, S.

PY - 2001

Y1 - 2001

N2 - 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.

AB - 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.

KW - Chaos

KW - Lyapunov exponent

KW - Phase space embedding

KW - Time delay neural network

KW - Time series prediction

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