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
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
复制标题
DOI：
10.1016/j.trc.2014.03.016
复制
发表时间：
2014-07-01
影响因子：
8.3
通讯作者：
Chen, Xiqun (Michael)
中科院分区：
文献类型：
--
作者：
Jiang, Xiushan;Zhang, Lei;Chen, Xiqun (Michael)
高铁客流的短期预测提供每日客运量估计，考虑到近期(例如，下周、下个月)的日常需求变化。它是高速客运专线规划、运营决策和运营动态调整中最关键的任务之一。准确的短期高铁需求预测为有效的铁路收入管理提供了基础。将集成经验模式分解(EEMD)模型和灰色支持向量机(GSVM)模型相结合，提出了一种混合短期需求预测方法。该混合预测方法分为三个步骤：(1)将含有噪声的短期客流数据分解为若干个本征模式函数(IMF)和一个趋势项；(2)利用粒子群算法(PSO)校正的广义支持向量机对每个IMF进行预测；(3)重构精化IMF分量，得到最终的高铁日客流预测结果，同时利用PSO实现最优重构组合。这种创新的混合方法在中国以武汉-广州高铁沿线三个典型的始发地-目的地对进行了演示。使用测试集进行EEMD-GSVM预测的平均绝对百分比误差分别为6.7%、5.1%和6.5%，远低于现有的两种预测方法(支持向量机和自回归整合移动平均)。应用结果表明，该混合预测方法具有较高的预测精度，特别适用于高铁客流的短期预测。(C)2014爱思唯尔有限公司。保留所有权利。
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. (C) 2014 Elsevier Ltd. All rights reserved.