Freeway traffic data prediction using neural networks

Cynthia Taylor, Deirdre Meldrum

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

14 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 testing.

Original languageEnglish (US)
Title of host publicationVehicle Navigation and Information Systems Conference (VNIS)
EditorsDaniel J. Dailey, Mark P. Haselkorn
Place of PublicationPiscataway, NJ, United States
PublisherIEEE
Pages225-230
Number of pages6
StatePublished - 1995
Externally publishedYes
EventProceedings of the 6th 1995 Vehicle Navigation and Information Systems Conference - Seattle, WA, USA
Duration: Jul 30 1995Aug 2 1995

Other

OtherProceedings of the 6th 1995 Vehicle Navigation and Information Systems Conference
CitySeattle, WA, USA
Period7/30/958/2/95

ASJC Scopus subject areas

  • Engineering(all)

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