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Wavelet‐Based Denoising for Traffic Volume Time Series Forecasting with Self‐Organizing Neural Networks
Author(s) -
BotoGiralda Daniel,
DíazPernas Francisco J.,
GonzálezOrtega David,
DíezHiguera José F.,
AntónRodríguez Míriam,
MartínezZarzuela Mario,
TorreDíez Isabel
Publication year - 2010
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/j.1467-8667.2010.00668.x
Subject(s) - series (stratigraphy) , noise reduction , artificial neural network , computer science , artificial intelligence , wavelet , time series , volume (thermodynamics) , pattern recognition (psychology) , wavelet transform , data mining , machine learning , geology , paleontology , physics , quantum mechanics
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.