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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.