Short-term forecasting of high-speed rail demand: A hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China |
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Affiliation: | 1. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China;2. Department of Civil and Environmental Engineering, University of Maryland, College Park, MD 20742, USA;1. Department of Automation, Tsinghua University, Beijing 100084, China;2. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing 210096, China;3. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China;1. Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China;2. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China;1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China;2. School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure System and Safety Control, Beihang University, Beijing 100191, China;3. Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, China;1. Institute of Systems Engineering and Control School of Traffic and Transport, Beijing Jiaotong University, Beijing 100044, PR China;2. Beijing Transport Management Technical Support Center, Beijing 100055, PR China;3. School of Computer Science & Engineering, Beihang University, Beijing 100191, PR China |
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Abstract: | Short-term forecasting of high-speed rail (HSR) passenger flow provides daily ridership estimates that account for day-to-day demand variations in the near future (e.g., next week, next month). It is one of the most critical tasks in high-speed passenger rail planning, operational decision-making and dynamic operation adjustment. An accurate short-term HSR demand prediction provides a basis for effective rail revenue management. In this paper, a hybrid short-term demand forecasting approach is developed by combining the ensemble empirical mode decomposition (EEMD) and grey support vector machine (GSVM) models. There are three steps in this hybrid forecasting approach: (i) decompose short-term passenger flow data with noises into a number of intrinsic mode functions (IMFs) and a trend term; (ii) predict each IMF using GSVM calibrated by the particle swarm optimization (PSO); (iii) reconstruct the refined IMF components to produce the final predicted daily HSR passenger flow, where the PSO is also applied to achieve the optimal refactoring combination. This innovative hybrid approach is demonstrated with three typical origin–destination pairs along the Wuhan-Guangzhou HSR in China. Mean absolute percentage errors of the EEMD-GSVM predictions using testing sets are 6.7%, 5.1% and 6.5%, respectively, which are much lower than those of two existing forecasting approaches (support vector machine and autoregressive integrated moving average). Application results indicate that the proposed hybrid forecasting approach performs well in terms of prediction accuracy and is especially suitable for short-term HSR passenger flow forecasting. |
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Keywords: | High-speed rail (HSR) Demand forecasting Hybrid model Ensemble empirical mode decomposition (EEMD) Grey support vector machine (GSVM) |
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