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- Title
Wavelet-Based Denoising for Traffic Volume Time Series Forecasting with Self-Organizing Neural Networks.
- Authors
Boto-Giralda, Daniel; Díaz-Pernas, Francisco J.; González-Ortega, David; Díez-Higuera, José F.; Antón-Rodríguez, Míriam; Martínez-Zarzuela, Mario; Torre-Díez, Isabel
- Abstract
In their goal to effectively manage the use of existing infrastructures, intelligent transportation systems require precise forecasting of near-term traffic volumes to feed real-time analytical models and traffic surveillance tools that alert of network links reaching their capacity. This article proposes a new methodological approach for short-term predictions of time series of volume data at isolated cross sections. The originality in the computational modeling stems from the fit of threshold values used in the stationary wavelet-based denoising process applied on the time series, and from the determination of patterns that characterize the evolution of its samples over a fixed prediction horizon. A self-organizing fuzzy neural network is optimized in its configuration parameters for learning and recognition of these patterns. Four real-world data sets from three interstate roads are considered for evaluating the performance of the proposed model. A quantitative comparison made with the results obtained by four other relevant prediction models shows a favorable outcome.
- Subjects
INTELLIGENT transportation systems; TRANSPORTATION management; FUZZY expert systems; TRAFFIC engineering; TRAFFIC monitoring; DMS/VS (Computer system); INFORMATION display systems; TRANSPORTATION industry
- Publication
Computer-Aided Civil & Infrastructure Engineering, 2010, Vol 25, Issue 7, p530
- ISSN
1093-9687
- Publication type
Article
- DOI
10.1111/j.1467-8667.2010.00668.x