Urban Railway Network Traffic Prediction with Spatiotemporal Correlations Matrix

被引：0

作者：

Shao, Weijuan ^{[1
,2
,3
]}

Li, Man ^{[1
,2
,3
]}

机构：

[1] Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, 3 Shang Yuan Cun, Beijing, Peoples R China

[2] Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing, Peoples R China

[3] Beijing Jiaotong Univ, Beijing Res Ctr Urban Traff Informat Sensing & Se, Beijing 100044, Peoples R China

来源：

PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON ELECTRICAL AND INFORMATION TECHNOLOGIES FOR RAIL TRANSPORTATION: TRANSPORTATION | 2016年 / 378卷

关键词：

Urban railway network; Traffic prediction; Spatiotemporal correlations matrix; NEURAL-NETWORKS; FLOW; MULTIVARIATE;

D O I：

10.1007/978-3-662-49370-0_35

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Urban railway network traffic prediction is a fundamental capability method as smart transportation technologies. Urban railway traffic prediction is a need that traffic authorities have begun demanding with a rapid increase in the number of passenger flow. Contemporary smart transportation technologies require the traffic prediction capability to be both available and scalable to apply to urban networks. In this paper, spatiotemporal correlations matrix method will be presented to traffic prediction.Urban railway network traffic prediction is a fundamental capability method as smart transportation technologies. Urban railway traffic prediction is a need that traffic authorities have begun demanding with a rapid increase in the number of passenger flow. Contemporary smart transportation technologies require the traffic prediction capability to be both available and scalable to apply to urban networks. In this paper, spatiotemporal correlations matrix method will be presented to traffic prediction.

引用

页码：335 / 343

页数：9

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