Freeway traffic data prediction using neural networks

Cynthia Taylor; Deirdre Meldrum

Freeway traffic data prediction using neural networks

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 language	English (US)
Title of host publication	Vehicle Navigation and Information Systems Conference (VNIS)
Editors	Daniel J. Dailey, Mark P. Haselkorn
Place of Publication	Piscataway, NJ, United States
Publisher	IEEE
Pages	225-230
Number of pages	6
State	Published - 1995
Externally published	Yes
Event	Proceedings of the 6th 1995 Vehicle Navigation and Information Systems Conference - Seattle, WA, USA Duration: Jul 30 1995 → Aug 2 1995

Other

Other	Proceedings of the 6th 1995 Vehicle Navigation and Information Systems Conference
City	Seattle, WA, USA
Period	7/30/95 → 8/2/95

ASJC Scopus subject areas

Engineering(all)

Cite this

@inproceedings{65bae217f1f3409f968fd3b5307bb638,

title = "Freeway traffic data prediction using neural networks",

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.",

author = "Cynthia Taylor and Deirdre Meldrum",

year = "1995",

language = "English (US)",

pages = "225--230",

editor = "Dailey, {Daniel J.} and Haselkorn, {Mark P.}",

booktitle = "Vehicle Navigation and Information Systems Conference (VNIS)",

publisher = "IEEE",

note = "Proceedings of the 6th 1995 Vehicle Navigation and Information Systems Conference ; Conference date: 30-07-1995 Through 02-08-1995",

}

TY - GEN

T1 - Freeway traffic data prediction using neural networks

AU - Taylor, Cynthia

AU - Meldrum, Deirdre

PY - 1995

Y1 - 1995

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0029200954&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0029200954&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0029200954

SP - 225

EP - 230

BT - Vehicle Navigation and Information Systems Conference (VNIS)

A2 - Dailey, Daniel J.

A2 - Haselkorn, Mark P.

PB - IEEE

CY - Piscataway, NJ, United States

T2 - Proceedings of the 6th 1995 Vehicle Navigation and Information Systems Conference

Y2 - 30 July 1995 through 2 August 1995

ER -

Freeway traffic data prediction using neural networks

Abstract

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ASJC Scopus subject areas

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