计算机工程与应用2019，Vol.55Issue(20)：216-221,239,7.DOI:10.3778/j.issn.1002-8331.1903-0262
基于EEMD-GWO-LSSVM的公共交通短期客流预测
Short-Term Passenger Flow Forecasting of Public Transport Based on EEMD-GWO-LSSVM
摘要
Abstract
In order to improve the accuracy of short-term passenger flow forecasting for large-scale public transport, an optimization algorithm of Least Squares Support Vector Machine(EEMD-GWO-LSSVM)based on Grey Wolf Optimiza-tion algorithm is proposed under the condition of decomposing the original data with integrated empirical mode. The algo-rithm is used to realize short-term passenger flow forecasting for large-scale public transport. The model uses EEMD to decompose the original data, and uses LSSVM to predict the decomposed IMF components. The prediction parameters of LSSVM are optimized by grey wolf algorithm. By training and forecasting the number of people entering and leaving the North Passenger Station of Xi’an Metro Line 2 in a month, the forecasting results and Support Vector Machine(SVM), Autoregressive Integrated Moving Average Mode(l ARIMA)are predicted, and the Least Squares Support Vector Machine (GWO-LSSVM)algorithm based on Grey Wolf Optimization parameters and cross-checking are used only. The Least Squares Support Vector Machines(LS-SVMs)with row parameter optimization are compared and the results show that the proposed algorithm has more accurate prediction results.关键词
公共交通/短期预测/灰狼优化/最小二乘支持向量机Key words
public transportation/short term prediction/grey wolf optimization/least square support vector machine分类
信息技术与安全科学引用本文复制引用
王盛,杨信丰..基于EEMD-GWO-LSSVM的公共交通短期客流预测[J].计算机工程与应用,2019,55(20):216-221,239,7.基金项目
国家自然科学基金(No.71761024) （No.71761024）
兰州局集团公司2019年科技发展项目计划. （）
