Short-Term Forecasting of Subway Traffic Based on K-Nearest Neighbour Pattern Matching

被引：7

作者：

Lin, Peiqun ^{[1
]}

Chen, Litian ^{[1
]}

Lei, Yongwei ^{[1
]}

机构：

[1] School of Civil Engineering and Transportation, South China University of Technology, Guangzhou,Guangdong,510640, China

来源：

Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science) | 2018年 / 46卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Subways - Motion compensation - Forecasting - Nearest neighbor search - Scheduling;

D O I：

10.3969/j.issn.1000-565X.2018.01.007

中图分类号：

学科分类号：

摘要：

In order to forecast the subway traffic accurately and better the vehicle scheduling and site management, a short-term forecasting of subway traffic based on K-nearest neighbour pattern matching is proposed. By analyzing the subway traffic data, it is found that the day passenger flow development mode of subway has a certain law. Thus, an adaptive K acquisition algorithm based on the calculation of error rate is proposed, it can improve the universality of the prediction algorithm by automatically obtaining the appropriate K. Finally, Guangzhou South Railway Station is taken as an example for the case study. The experimental results show that the method proposed is applicable to the subway passenger flow forecast of two different modes of traffic-holidays and non-holidays, its average prediction accuracy is about 90%. It can be seen that this method has a good application value. © 2018, Editorial Department, Journal of South China University of Technology. All right reserved.

引用

页码：50 / 57