Freeway traffic data prediction via artificial neural networks for use in a fuzzy logic ramp metering algorithm

Cynthia Taylor, Deirdre Meldrum

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

A multi-layer perceptron type of artificial neural network predicts congested freeway data while demonstrating robustness to faulty loop detector data. Test results on historical data from the I-5 freeway in Seattle, Washington demonstrate that a neural network can successfully predict volume and occupancy one minute in advance, as well as fill in the gaps for missing data with an appropriate prediction. The volume and occupancy predictions will be used as inputs to a fuzzy logic ramp metering algorithm currently under development.

Original languageEnglish (US)
Title of host publicationIntelligent Vehicles Symposium, Proceedings
Editors Anon
Place of PublicationPiscataway, NJ, United States
PublisherIEEE
Pages308-313
Number of pages6
StatePublished - 1994
Externally publishedYes
EventProceedings of the Intelligent Vehicles'94 Symposium - Paris, Fr
Duration: Oct 24 1994Oct 26 1994

Other

OtherProceedings of the Intelligent Vehicles'94 Symposium
CityParis, Fr
Period10/24/9410/26/94

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Taylor, C., & Meldrum, D. (1994). Freeway traffic data prediction via artificial neural networks for use in a fuzzy logic ramp metering algorithm. In Anon (Ed.), Intelligent Vehicles Symposium, Proceedings (pp. 308-313). IEEE.