File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1007/978-3-030-50341-3_20
- Scopus: eid_2-s2.0-85088747545
Supplementary
-
Citations:
- Scopus: 0
- Appears in Collections:
Conference Paper: Forecasting the Subway Volume Using Local Linear Kernel Regression
Title | Forecasting the Subway Volume Using Local Linear Kernel Regression |
---|---|
Authors | |
Keywords | Subway Volume Forecasting Local linear kernel regression ARIMA model |
Issue Date | 2020 |
Publisher | Springer. |
Citation | The 7th International Conference on HCI in Business, Government, and Organizations (HCIBGO 2020), as part of 22nd HCI (Human-Computer Interaction) International Conference (HCII 2020), Copenhagen, Denmark, 19-24 July 2020, Proceedings, p. 254-265 How to Cite? |
Abstract | Entrusted by the Kaohsiung Rapid Transit Corporation (KRTC), this study attempts to devise a more effective methodology to forecast the passenger volume of the subway system in the city of Kaohsiung, Taiwan. We propose a local linear kernel model to incorporate different weights for each realized observations. It enables us to capture richer information and improve rate of accuracy. We compare different methodologies, for example, ARIMA, Best in-sample fit ARIMA, linear model, and their rolling versions with our proposed local linear kernel regression model by examining the in-sample and out-of-sample performances. Our results indicate that the proposed rolling local linear kernel regression model performs the best in forecasting the passenger volume in terms of smaller prediction errors in a wide range of measurements. |
Persistent Identifier | http://hdl.handle.net/10722/294731 |
ISBN | |
Series/Report no. | Lecture Notes in Computer Science (LNCS) ; v. 12204 |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yang, YC | - |
dc.contributor.author | Ding, C | - |
dc.contributor.author | Jin, Y | - |
dc.date.accessioned | 2020-12-08T07:41:02Z | - |
dc.date.available | 2020-12-08T07:41:02Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | The 7th International Conference on HCI in Business, Government, and Organizations (HCIBGO 2020), as part of 22nd HCI (Human-Computer Interaction) International Conference (HCII 2020), Copenhagen, Denmark, 19-24 July 2020, Proceedings, p. 254-265 | - |
dc.identifier.isbn | 9783030503406 | - |
dc.identifier.uri | http://hdl.handle.net/10722/294731 | - |
dc.description.abstract | Entrusted by the Kaohsiung Rapid Transit Corporation (KRTC), this study attempts to devise a more effective methodology to forecast the passenger volume of the subway system in the city of Kaohsiung, Taiwan. We propose a local linear kernel model to incorporate different weights for each realized observations. It enables us to capture richer information and improve rate of accuracy. We compare different methodologies, for example, ARIMA, Best in-sample fit ARIMA, linear model, and their rolling versions with our proposed local linear kernel regression model by examining the in-sample and out-of-sample performances. Our results indicate that the proposed rolling local linear kernel regression model performs the best in forecasting the passenger volume in terms of smaller prediction errors in a wide range of measurements. | - |
dc.language | eng | - |
dc.publisher | Springer. | - |
dc.relation.ispartof | HCI in Business, Government and Organizations. HCII 2020 | - |
dc.relation.ispartof | International Conference on Human-Computer Interaction | - |
dc.relation.ispartofseries | Lecture Notes in Computer Science (LNCS) ; v. 12204 | - |
dc.subject | Subway Volume Forecasting | - |
dc.subject | Local linear kernel regression | - |
dc.subject | ARIMA model | - |
dc.title | Forecasting the Subway Volume Using Local Linear Kernel Regression | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Ding, C: chaoding@hku.hk | - |
dc.identifier.authority | Ding, C=rp01952 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-030-50341-3_20 | - |
dc.identifier.scopus | eid_2-s2.0-85088747545 | - |
dc.identifier.hkuros | 320408 | - |
dc.identifier.spage | 254 | - |
dc.identifier.epage | 265 | - |
dc.publisher.place | Cham | - |