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Forecasting Freeway Link Travel Times with a Multilayer Feedforward Neural Network
Open accessAbstract
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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Cited by
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Spatiotemporal traffic forecasting: review and proposed directions
Ermagun, A., & Levinson, D. (2018) · Transport Reviews · doi:10.1080/01441647.2018.1442887 · Cited by 231
Bus Arrival Time Prediction: A Comprehensive Review
Kumar, B. A., Singh, R., Shaji, H. E., & Vanajakshi, L. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3545695 · Cited by 13
Chien (2002) · doi:10.1061/(asce)0733-947x(2002)128:5(429
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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- 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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