Journal
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
Volume 10, Issue 4, Pages 303-321Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/S0968-090X(02)00009-8
Keywords
traffic forecasting; nonparametric regression; ARIMA models; motorway flows; short-term traffic prediction; statistical forecasting
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Single point short-term traffic flow forecasting will play a key role in supporting demand forecasts needed by operational network models. Seasonal autoregressive integrated moving average (ARIMA), a classic parametric modeling approach to time series, and nonparametric regression models have been proposed as well suited for application to single point short-term traffic flow forecasting. Past research has shown seasonal ARIMA models to deliver results that are statistically superior to basic implementations of nonparametric regression. However, the advantages associated with a data-driven nonparametric forecasting approach motivate further investigation of refined nonparametric forecasting methods. Following this motivation, this research effort seeks to examine the theoretical foundation of nonparametric regression and to answer the question of whether nonparametric regression based on heuristically improved forecast generation methods approach the single interval traffic flow prediction performance of seasonal ARIMA models. (C) 2002 Elsevier Science Ltd. All rights reserved.
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