标题:A Data-Driven-Based Wavelet Support Vector Approach for Passenger Flow Forecasting of the Metropolitan Hub
作者:Tang, Ming; Li, Zhiwu; Tian, Guangdong
作者机构:[Tang, Ming; Tian, Guangdong] Jilin Univ, Transportat Coll, Changchun 130022, Jilin, Peoples R China.; [Tang, Ming] Jilin Univ, Sch Management, Chan 更多
通讯作者:Tian, Guangdong;Tian, GD;Tian, GD
通讯作者地址:[Tian, GD]Jilin Univ, Transportat Coll, Changchun 130022, Jilin, Peoples R China;[Tian, GD]Shandong Univ, Sch Mech Engn, Jinan 250061, Shandong, Peopl 更多
来源:IEEE ACCESS
出版年:2019
卷:7
页码:7176-7183
DOI:10.1109/ACCESS.2019.2890819
关键词:Transit hub; streamline network; chaos; wavelet support vector;; data-driven
摘要:With the rapid development of the construction and operation of mass transit hubs, passenger data collection, modeling, and prediction for optimal control have become very important. In this paper, pedestrian facilities are abstracted into connected nodes, and the passenger flow network is formed according to the facility connection relationship determined by the traffic organization; therefore, the state variables of the hub, such as saturation, and the traveling time can be estimated by pedestrian flow information collected by camera monitors and a free Wi-Fi network, including the fast analysis of data features and traffic flow prediction. The method is applied to a real case. The features of pedestrian flows are classified as chaotic and nonchaotic. We use a regression model to predict the nonchaotic situation, and the wavelet support vector machine model is proposed for the chaotic. The results can be used for the control of exits and ramps in the hub.
收录类别:EI;SCOPUS;SCIE
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060709737&doi=10.1109%2fACCESS.2019.2890819&partnerID=40&md5=afad9713c6a03844b74d06cb2ef8e52c
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