Open AccessAnalysis of peak and non‐peak traffic forecasts using combined models
Author(s) -
Zhang Yang,
Liu Yuncai
Publication year - 2011
Publication title -
journal of advanced transportation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.577
H-Index - 46
eISSN - 2042-3195
pISSN - 0197-6729
DOI - 10.1002/atr.128
Subject(s) - computer science , selection (genetic algorithm) , field (mathematics) , index (typography) , sample (material) , baseline (sea) , model selection , traffic count , operations research , econometrics , data mining , machine learning , engineering , transport engineering , mathematics , traffic volume , chemistry , oceanography , chromatography , world wide web , pure mathematics , geology
Accurate and timely traffic forecasting is crucial to effective management of intelligent transportation systems (ITS). To predict travel time index (TTI) data, we select six baseline individual predictors as basic combination components. Applying the one‐step‐ahead out‐of‐sample forecasts, the paper proposes several linear combined forecasting techniques. States of traffic situations are classified into peak and non‐peak periods. Based on detailed data analyses, some practical guidance and comments are given in what situation a combined model is better than an individual model or other types of combined models. Indicating which model is more appropriate in each state, persuasive comparisons demonstrate that the combined procedures can significantly reduce forecast error rates. It reveals that the approaches are practically promising in the field. To the best of our knowledge, it is the first time to systematically investigate these approaches in peak and non‐peak traffic forecasts. The studies can provide a reference for optimal forecasting model selection in each period. Copyright © 2010 John Wiley & Sons, Ltd.