Non-linear analysis of traffic flow

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

Research output: Contribution to conferencePaper

40 Citations (Scopus)

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 languageEnglish (US)
Pages681-685
Number of pages5
StatePublished - Jan 1 2001
Event2001 IEEE Intelligent Transportation Systems Proceedings - Oakland, CA, United States
Duration: Aug 25 2001Aug 29 2001

Other

Other2001 IEEE Intelligent Transportation Systems Proceedings
CountryUnited States
CityOakland, CA
Period8/25/018/29/01

Fingerprint

Time series analysis
Highway systems
Nonlinear analysis
Inductance
Time series
Experiments

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

Cite this

Nair, A. S., Liu, J. C., Rilett, L., & Gupta, S. (2001). Non-linear analysis of traffic flow. 681-685. Paper presented at 2001 IEEE Intelligent Transportation Systems Proceedings, Oakland, CA, United States.

Non-linear analysis of traffic flow. / Nair, A. S.; Liu, J. C.; Rilett, L.; Gupta, S.

2001. 681-685 Paper presented at 2001 IEEE Intelligent Transportation Systems Proceedings, Oakland, CA, United States.

Research output: Contribution to conferencePaper

Nair, AS, Liu, JC, Rilett, L & Gupta, S 2001, 'Non-linear analysis of traffic flow' Paper presented at 2001 IEEE Intelligent Transportation Systems Proceedings, Oakland, CA, United States, 8/25/01 - 8/29/01, pp. 681-685.
Nair AS, Liu JC, Rilett L, Gupta S. Non-linear analysis of traffic flow. 2001. Paper presented at 2001 IEEE Intelligent Transportation Systems Proceedings, Oakland, CA, United States.
Nair, A. S. ; Liu, J. C. ; Rilett, L. ; Gupta, S. / Non-linear analysis of traffic flow. Paper presented at 2001 IEEE Intelligent Transportation Systems Proceedings, Oakland, CA, United States.5 p.
@conference{7d68f169806a46349143da3ed3ee6474,
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",
month = "1",
day = "1",
language = "English (US)",
pages = "681--685",
note = "2001 IEEE Intelligent Transportation Systems Proceedings ; Conference date: 25-08-2001 Through 29-08-2001",

}

TY - CONF

T1 - Non-linear analysis of traffic flow

AU - Nair, A. S.

AU - Liu, J. C.

AU - Rilett, L.

AU - Gupta, S.

PY - 2001/1/1

Y1 - 2001/1/1

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

UR - http://www.scopus.com/inward/record.url?scp=0034773657&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0034773657&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:0034773657

SP - 681

EP - 685

ER -