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Near‐Term Travel Speed Prediction Utilizing Hilbert–Huang Transform
Author(s) -
Hamad Khaled,
Shourijeh Morteza Tabatabaie,
Lee Earl,
Faghri Ardeshir
Publication year - 2009
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.1467-8667.2009.00620.x
Subject(s) - hilbert–huang transform , computer science , nonlinear system , artificial neural network , backpropagation , intelligent transportation system , time series , series (stratigraphy) , data mining , term (time) , algorithm , artificial intelligence , machine learning , engineering , white noise , telecommunications , paleontology , physics , civil engineering , quantum mechanics , biology
Accurate short‐term prediction of travel speed as a proxy for time is central to many Intelligent Transportation Systems, especially for Advanced Traveler Information Systems and Advanced Traffic Management Systems. In this study, we propose an innovative methodology for such prediction. Because of the inherently direct derivation of travel time from speed data, the study was limited to the use of speed only as a single predictor. The proposed method is a hybrid one that combines the use of the empirical mode decomposition (EMD) and a multilayer feedforward neural network with backpropagation. The EMD is the key part of the Hilbert–Huang transform, which is a newly developed method at NASA for the analysis of nonstationary, nonlinear time series. The rationale for using the EMD is that because of the highly nonlinear and nonstationary nature of link speed series, by decomposing the time series into its basic components, more accurate forecasts would be obtained. We demonstrated the effectiveness of the proposed method by applying it to real‐life loop detector data obtained from I‐66 in Fairfax, Virginia. The prediction performance of the proposed method was found to be superior to previous forecasting techniques. Rigorous testing of the distribution of prediction errors revealed that the model produced unbiased predictions of speeds. The superiority of the proposed model was also verified during peak periods, midday, and night. In general, the method was accurate, computationally efficient, easy to implement in a field environment, and applicable to forecasting other traffic parameters.