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• W2039190250 abstract "Abstract 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." @default.
• W2039190250 created "2016-06-24" @default.
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• W2039190250 date "2010-09-24" @default.
• W2039190250 modified "2023-10-16" @default.
• W2039190250 title "Analysis of peak and non-peak traffic forecasts using combined models" @default.
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• W2039190250 doi "https://doi.org/10.1002/atr.128" @default.
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