TY - GEN
T1 - Forecasting the subway volume using local linear kernel regression
AU - Yang, Yu chen
AU - Ding, Chao
AU - Jin, Yong
N1 - Funding Information:
Y. Jin?We thank the anonymous reviewers, the track associate editor, and the participants at Taiwan Summer Workshop on Information Management for helpful discussions and useful suggestions. Yong Jin also acknowledges the financial support of the PolyU Central Research Grant (Project No. G-YBZV). This work is partially supported by the Intelligent Electronic Commerce Research Center from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.
Funding Information:
Y. Jin—We thank the anonymous reviewers, the track associate editor, and the participants at Taiwan Summer Workshop on Information Management for helpful discussions and useful suggestions. Yong Jin also acknowledges the financial support of the PolyU Central Research Grant (Project No. G-YBZV). This work is partially supported by the Intelligent Electronic Commerce Research Center from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.
Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - ARIMA model
KW - Local linear kernel regression
KW - Subway Volume Forecasting
UR - http://www.scopus.com/inward/record.url?scp=85088747545&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-50341-3_20
DO - 10.1007/978-3-030-50341-3_20
M3 - Conference article published in proceeding or book
AN - SCOPUS:85088747545
SN - 9783030503406
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 254
EP - 265
BT - HCI in Business, Government and Organizations - 7th International Conference, HCIBGO 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings
A2 - Nah, Fiona Fui-Hoon
A2 - Siau, Keng
PB - Springer
T2 - 7th International Conference on HCI in Business, Government, and Organizations, HCIBGO 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020
Y2 - 19 July 2020 through 24 July 2020
ER -