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Travel Links Prediction in Shared Mobility Networks Using Graph Neural Network Models by Yinshuang Xiao; Faez Ahmed; Zhenghui Sha is a scholarly article available to read on EtoBox.

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Abstract The emerging sharing mobility systems are gaining increasing popularity because of the significant economical and environmental benefits. To facilitate the operation of sharing mobility systems, many studies are conducted to analyze and predict users’ travel behaviors. However, most research focuses on investigating every station’s usage and demand; therefore, insight into the user behavior and travel demand between stations from origin to destination is little known. Aiming to better understand the factors that would influence origin-destination travel demand, we present a complex network-based approach to predicting the travel demand between stations (e.g., whether two stations have sufficient trips to form a strong connection in a month) in sharing mobility systems. Particularly, in this study, we are interested in knowing whether local network information (e.g., the neighboring station’s features of a station and its surrounding points of interest (POI), such as banks, schools, etc.) would influence the formation of a strong connection or not. If so, to what extent do such factors play a role in it. To answer this question, we adopt Graph Neural Network (GNN), in which

Author

Yinshuang Xiao; Faez Ahmed; Zhenghui Sha

Published

2022

Language

EN