Recurrent flow networks: A recurrent latent variable model for density estimation of urban mobility

作者：

Highlights：

• Mobility demand characterized by spatial and temporal variability.

• Recurrent Flow Networks (RFN) formulated for spatio-temporal density estimation.

• RFNs exhibit long-term predictions and fine-grained distributions on urban topologies.

• Experiments with synthetic and real-world data demonstrate solution approach.

摘要

•Mobility demand characterized by spatial and temporal variability.•Recurrent Flow Networks (RFN) formulated for spatio-temporal density estimation.•RFNs exhibit long-term predictions and fine-grained distributions on urban topologies.•Experiments with synthetic and real-world data demonstrate solution approach.

论文关键词：Urban mobility,Latent variable models,Normalizing flows,Variational inference

论文评审过程：Received 11 October 2021, Revised 16 April 2022, Accepted 25 April 2022, Available online 27 April 2022, Version of Record 2 May 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108752