Comparison of parametric and nonparametric models for traffic flow forecasting |
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| Institution: | 1. Department of Civil Engineering, Thornton Hall, University of Virginia, Charlottesville, VA 22903-2442, USA;2. School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA;3. Department of Systems Engineering, Olsson Hall, University of Virginia, Charlottesville, VA 22903-2442, USA;1. ENEA (Italian Energy New technologies and sustainable Economic development Agency);2. Università degli Studi “Roma Tre”, Computer Science and Automation Department;1. School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure, Systems, and Safety Control, Beihang University, Beijing 100191, China;2. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, SiPaiLou #2, Nanjing 210096, China;3. Department of Science and Technology, Beijing Traffic Management Bureau, Beijing 100037, China;4. Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195-2700, United States;1. Civil & Environmental Engineering, Transportation Research Institute at Old Dominion University (ODU), 132 Kufman Hall, Norfolk, VA 23529, United States;2. Director of Transportation Research Institute at ODU, Civil & Environmental Engineering, Transportation Research Institute at Old Dominion University (ODU), 135 Kufman Hall, Norfolk, VA 23529, United States;1. Faculty of Science, Engineering and Technology, Swinburne University of Technology, Australia;2. Department of Civil Engineering, Monash University, Australia;3. Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, China;4. Faculty of Information Technology, Monash University, Australia |
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| Abstract: | 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. |
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