Forecasting Multiple-Period Freeway Link Travel Times Using Modular Neural Networks
- 1 January 1998
- journal article
- Published by SAGE Publications in Transportation Research Record: Journal of the Transportation Research Board
- Vol. 1617 (1) , 163-170
- https://doi.org/10.3141/1617-23
Abstract
With the advent of route guidance systems (RGS), the prediction of short-term link travel times has become increasingly important. For RGS to be successful, the calculated routes should be based on not only historical and real-time link travel time information but also anticipatory link travel time information. An examination is conducted on how realtime information gathered as part of intelligent transportation systems can be used to predict link travel times for one through five time periods (of 5 minutes’ duration). The methodology developed consists of two steps. First, the historical link travel times are classified based on an unsupervised clustering technique. Second, an individual or modular artificial neural network (ANN) is calibrated for each class, and each modular ANN is then used to predict link travel times. Actual link travel times from Houston, Texas, collected as part of the automatic vehicle identification system of the Houston Transtar system were used as a test bed. It was found that the modular ANN outperformed a conventional singular ANN. The results of the best modular ANN were compared with existing link travel time techniques, including a Kalman filtering model, an exponential smoothing model, a historical profile, and a real-time profile, and it was found that the modular ANN gave the best overall results.Keywords
This publication has 8 references indexed in Scilit:
- Foundations of Neural Networks, Fuzzy Systems, and Knowledge EngineeringPublished by MIT Press ,1996
- Combining kohonen maps with arima time series models to forecast traffic flowTransportation Research Part C: Emerging Technologies, 1996
- Classifying Highways: Hierarchical Grouping versus Kohonen Neural NetworksJournal of Transportation Engineering, 1995
- Efficient classification for multiclass problems using modular neural networksIEEE Transactions on Neural Networks, 1995
- On the generalization ability of neural network classifiersPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1994
- Learning piecewise control strategies in a modular neural network architectureIEEE Transactions on Systems, Man, and Cybernetics, 1993
- On fuzzy modeling using fuzzy neural networks with the back-propagation algorithmIEEE Transactions on Neural Networks, 1992
- Self-Organization and Associative MemoryPublished by Springer Nature ,1988