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 |
| 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
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.