A geometry-driven neural topic model for trip purpose inference

Zhang, Jiaqi1; Fan, Zipei1,2; Song, Xuan1; Shibasaki, Ryosuke1,2

2023-08-01

10.1007/s10707-023-00504-6

GEOINFORMATICA   影响因子和分区

1384-6175

1573-7624

Understanding urban human mobility, particularly trip purposes, is essential for optimizing traffic management, personalized recommendations, and urban planning. However, in real-world scenarios, trip purposes cannot be directly extracted from the trajectory data. To address this issue, we propose a geometry-driven neural topic model for trip purpose inference. We integrate trajectory data with nearby POI data using a geometry-driven technique to enhance the interpretability of the results. Furthermore, our model captures the semantics and relationships of the data in a high-dimensional space and identifies latent topics representing distinct trip purposes. These learned topics are analyzed using clustering algorithms to group similar trips, enabling trip purpose inference. And we evaluate our model using the trajectory data of Shenzhen and Chengdu, and compare it with baseline models. The results demonstrate that our model performs well. Furthermore, we analyze trajectory data containing trip purpose information to gain insight into human mobility patterns and the influence of trip purposes, paving the way for potential implications and future research directions.

Urban human mobility Trip purposes inference Neural network Trip embedding

SCI ; EI

英语

第一 ; 通讯

Grants of National Key Research and Development Program of China[2021YFB1714400] ; Guangdong Provincial Key Laboratory[2020B121201001] ; Japan Society for the Promotion of Science (JSPS)[22H03573]

Computer Science ; Physical Geography

Computer Science, Information Systems ; Geography, Physical

WOS:001050726700002

SPRINGER

20233414605299

Geometry ; Inference engines ; Semantics ; Trajectories

Expert Systems:723.4.1 ; Information Sources and Analysis:903.1 ; Mathematics:921

Web of Science

1.Southern Univ Sci & Technol, SUSTech UTokyo Joint Res Ctr Super Smart Cities, Dept Comp Sci & Engn, Shenzhen, Guangdong, Peoples R China
2.Univ Tokyo, Ctr Spatial Informat Sci, Tokyo, Japan

Zhang, Jiaqi,Fan, Zipei,Song, Xuan,et al. A geometry-driven neural topic model for trip purpose inference[J]. GEOINFORMATICA,2023.

Zhang, Jiaqi,Fan, Zipei,Song, Xuan,&Shibasaki, Ryosuke.(2023).A geometry-driven neural topic model for trip purpose inference.GEOINFORMATICA.

Zhang, Jiaqi,et al."A geometry-driven neural topic model for trip purpose inference".GEOINFORMATICA (2023).