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Cited by 346

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Forecasting Freeway Link Travel Times with a Multilayer Feedforward Neural Network

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Park, Dongjoo, Rilett, Laurence R.·1999·Computer-Aided Civil and Infrastructure Engineering

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.

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Metadata

Authors
Park, D., & Rilett, L. R.
Venue
Computer-Aided Civil and Infrastructure Engineering
Publisher
Elsevier BV
Published
1999-09
Volume
14
Issue
5
Pages
357-367
ISSN
  • 1093-9687 (ISSN-L)
  • 1467-8667
OpenAlex
W2041567331
Cited by
346
References
0
FWCI
28.06
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Forecasting Freeway Link Travel Times with a Multilayer Feedforward Neural Network. https://app.cochat.ai/papers/10.1111/0885-9507.00154
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Park, D., & Rilett, L. R. (1999). Forecasting Freeway Link Travel Times with a Multilayer Feedforward Neural Network. Computer-Aided Civil and Infrastructure Engineering. https://doi.org/10.1111/0885-9507.00154

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