非参数回归短时客流预测中状态向量研究

doi:10.16182/j.issn1004731x.joss.201709034

系统仿真学报 ›› 2017, Vol. 29 ›› Issue (9): 2128-2134.doi: 10.16182/j.issn1004731x.joss.201709034

非参数回归短时客流预测中状态向量研究

郭晗, 焦朋朋

北京建筑大学土木与交通工程学院,北京 100044

收稿日期:2017-05-19 发布日期:2020-06-02
通讯作者: 焦朋朋(1980-),男,安徽,博士,教授,研究方向为交通运输规划。
作者简介:郭晗(1993-), 女, 北京, 硕士生, 研究方向为交通运输规划。
基金资助:
国家自然科学基金(51578040),北京市科技新星计划(Z151100000315050),北京市自然科学基金(8162013)

Research of State Vector in Short-Term Passengers Flow Forecasting Based on Nonparametric Regression

Guo Han, Jiao Pengpeng

School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

Received:2017-05-19 Published:2020-06-02

摘要/Abstract

摘要： 采用K近邻非参数回归的方法对轨道交通站点短时进站客流量进行了预测,并对状态向量的选择进行了研究。预测结果表明：以预测时段前m个时段的历史数据作为状态向量具有较好的预测效果,而相邻站点历史客流数据虽然在数值上与预测站点的客流数据具有较大相关性,但由于其忽视了各站进站客流是相对独立的,因此不宜作为状态向量。

关键词: 短时客流预测, 非参数回归, 状态向量, K近邻

Abstract: KNNR (K Nearest Neighbor Based Nonparametric Regression) Method was used for short-term traffic forecast and the choice of state vector was studied. The result shows that taking the data of some historical periods as the state vector has a good prediction. Although the correlation of the historical passenger flow between different Rail transit sites is significant, it neglects the fact that the passengers enter each station is independent. So taking the historical passenger flow of adjacent sites as the state vector is not appropriate.

Key words: short-term passengers flow forecasting, nonparametric regression, state vector, k nearest neighbors

中图分类号:

U491

郭晗, 焦朋朋. 非参数回归短时客流预测中状态向量研究[J]. 系统仿真学报, 2017, 29(9): 2128-2134.

Guo Han, Jiao Pengpeng. Research of State Vector in Short-Term Passengers Flow Forecasting Based on Nonparametric Regression[J]. Journal of System Simulation, 2017, 29(9): 2128-2134.

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非参数回归短时客流预测中状态向量研究

Research of State Vector in Short-Term Passengers Flow Forecasting Based on Nonparametric Regression

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