Forecasting freeway link travel times with a multilayer feedforward neural network

Dongjoo Park, Laurence R. Rilett

Research output: Contribution to journalArticle

210 Citations (Scopus)

Abstract

One of the major requirements of advanced traveler information systems (ATISs) is a mechanism to estimate link travel times. This article examines the use of an artificial neural network (ANN) for predicting freeway link travel times for one through five time periods into the future. Actual freeway link travel times from Houston, Texas, that were collected as part of the automatic vehicle identification (AVI) system were used as a test bed. It was found that when predicting one or two time periods into the future, the ANN model that only considered previous travel times from the target link gave the best results. However, when predicting three to five time periods into the future, the ANN model that employed travel times from upstream and downstream links in addition to the target link gave superior results. The ANN model also gave the best overall results compared with existing link travel time forecasting techniques.

Original languageEnglish (US)
Pages (from-to)357-367
Number of pages11
JournalComputer-Aided Civil and Infrastructure Engineering
Volume14
Issue number5
DOIs
StatePublished - Jan 1 1999

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Feedforward neural networks
Highway systems
Travel time
Multilayer neural networks
Neural networks
Automatic vehicle identification
Advanced traveler information systems

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Computational Theory and Mathematics

Cite this

Forecasting freeway link travel times with a multilayer feedforward neural network. / Park, Dongjoo; Rilett, Laurence R.

In: Computer-Aided Civil and Infrastructure Engineering, Vol. 14, No. 5, 01.01.1999, p. 357-367.

Research output: Contribution to journalArticle

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