A Two-Step Model for Predicting Travel Demand in Expanding Subways

Autor:	Kaipeng Wang, Pu Wang, Zhiren Huang, Ximan Ling, Fan Zhang, Anthony Chen
Přispěvatelé:	Central South University, Department of Computer Science, Shenzhen Institutes of Advanced Technology, Hong Kong Polytechnic University, Aalto-yliopisto, Aalto University
Rok vydání:	2022
Předmět:	Mechanical Engineering Automotive Engineering GeneralLiterature_MISCELLANEOUS Computer Science Applications
Zdroj:	IEEE Transactions on Intelligent Transportation Systems. 23:19534-19543
ISSN:	1558-0016 1524-9050
Popis:	In many cities, subways are expanding with new or extended lines being built and put into operations. The prediction of future travel demand in subway with the planned expansion is of significant importance because such information is crucial for new line planning and new network operations. In this study, we identify the determinant features from potential influential factors of passenger travel demand and develop a two-step model for predicting passenger travel demand in expanding subways. The proposed model is tested in an actual subway with a new line being put into operations, and achieves higher prediction accuracy than the benchmark models.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9de9a36ce13bf5abe2ead99f3af2ba44 https://doi.org/10.1109/tits.2022.3166669 Zobrazit plný text záznamu