Personalized route recommendation for passengers in urban rail transit based on collaborative filtering algorithm

Autor: Wei Li, Zhiyuan Li, Qin Luo
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: IET Intelligent Transport Systems, Vol 18, Iss 10, Pp 1815-1829 (2024)
Druh dokumentu: article
ISSN: 1751-9578
1751-956X
DOI: 10.1049/itr2.12476
Popis: Abstract The rapid advancements in information technology and intelligent systems within urban rail transit (URT) systems have highlighted the need for more personalized route recommendations that consider passengers’ travel habits. This study aims to address this issue by investigating passenger travel routes alongside other passengers who share similar travel preferences, utilizing collaborative filtering (CF) techniques. The approach involves analyzing historical card data to assess passenger travel profiles, including actual travel time under crowded conditions. By considering both individual passenger preferences and the preferences of similar passengers, a CF algorithm is employed to offer personalized route recommendations. The Shenzhen metro is used as a case study to illustrate the proposed method. The results demonstrate that the proposed approach surpasses traditional route recommendation methods by providing tailored suggestions that align more closely with passengers’ travel preferences. These findings emphasize the value of incorporating passenger travel preferences into route recommendation models, thereby enhancing the accuracy and effectiveness of personalized route recommendations within URT systems.
Databáze: Directory of Open Access Journals
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