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Conference Paper: Predicting the subway volume using local linear kernel regression

TitlePredicting the subway volume using local linear kernel regression
Authors
Yang, YCCao, JDing, CJin, Y
Issue Date2015
Citation
The 3rd Taiwan Summer Workshop on Information Management (TSWIM 2015), National Taiwan University, Taipei, Taiwan, 12-14 July 2015. How to Cite?
AbstractFor a newly-built subway in the City of Kaohsiung, predicting the subway volume is very important. Future development and planning of the subway and new business centers are based on accurate prediction of the future subway volume. Currently the Kaohsiung Rapid Transit Corporation applies the ARIMA model to predict the future subway volumes. In this paper, we implement a linear model and a local linear kernel regression model to forecast the one month ahead of the subway volume. Our methods demonstrate an excellent in-sample performance.
DescriptionPaper Presentation - Session 4: no. S4-02
Persistent Identifierhttp://hdl.handle.net/10722/215494

 

DC FieldValueLanguage
dc.contributor.authorYang, YC-
dc.contributor.authorCao, J-
dc.contributor.authorDing, C-
dc.contributor.authorJin, Y-
dc.date.accessioned2015-08-21T13:27:54Z-
dc.date.available2015-08-21T13:27:54Z-
dc.date.issued2015-
dc.identifier.citationThe 3rd Taiwan Summer Workshop on Information Management (TSWIM 2015), National Taiwan University, Taipei, Taiwan, 12-14 July 2015.-
dc.identifier.urihttp://hdl.handle.net/10722/215494-
dc.descriptionPaper Presentation - Session 4: no. S4-02-
dc.description.abstractFor a newly-built subway in the City of Kaohsiung, predicting the subway volume is very important. Future development and planning of the subway and new business centers are based on accurate prediction of the future subway volume. Currently the Kaohsiung Rapid Transit Corporation applies the ARIMA model to predict the future subway volumes. In this paper, we implement a linear model and a local linear kernel regression model to forecast the one month ahead of the subway volume. Our methods demonstrate an excellent in-sample performance.-
dc.languageeng-
dc.relation.ispartofTaiwan Summer Workshop on Information Management, TSWIM 2015-
dc.titlePredicting the subway volume using local linear kernel regression-
dc.typeConference_Paper-
dc.identifier.emailDing, C: chaoding@hku.hk-
dc.identifier.authorityDing, C=rp01952-
dc.identifier.hkuros248106-

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