Forecasting the subway volume using local linear kernel regression

Yu chen Yang, Chao Ding, Yong Jin

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

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.

Original languageEnglish
Title of host publicationHCI in Business, Government and Organizations - 7th International Conference, HCIBGO 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings
EditorsFiona Fui-Hoon Nah, Keng Siau
PublisherSpringer
Pages254-265
Number of pages12
ISBN (Print)9783030503406
DOIs
Publication statusPublished - 2020
Event7th 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 - Copenhagen, Denmark
Duration: 19 Jul 202024 Jul 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12204 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th 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
Country/TerritoryDenmark
CityCopenhagen
Period19/07/2024/07/20

Keywords

  • ARIMA model
  • Local linear kernel regression
  • Subway Volume Forecasting

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this