DeepFlowGen: Intention-Aware Fine Grained Crowd Flow Generation via Deep Neural Networks

Shao, Erzhuo; Wang, Huandong; Tong, Xia; Yang, Hu; Geng, Lu; Jin, Depeng; Li, Yong

doi:10.1109/tkde.2021.3061813

Cited by 5 publications

(1 citation statement)

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“…Among which a key aspect is the trip purposes inference, aiming to reveal the latent motivations behind the mobility. Determining trip purposes not only provide strong support for urban planning [5,6], traffic management [7], and policy-making [8] but also further improve the quality of life for residents.…”

Section: Introductionmentioning

confidence: 99%

A geometry-driven neural topic model for trip purpose inference

Zhang,

Fan,

Song

et al. 2023

Geoinformatica

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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 trajectory data. To address this issue, we propose a geometry-driven neural topic model for trip purpose inference. We integrate trajectory data with nearby points of interest (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 insights into human mobility patterns and the influence of trip purposes, paving the way for potential implications and future research directions.

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