Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/172615
Title: Fine-Grained Urban Flow Inference
Authors: Ouyang, Kun
LIANG YUXUAN 
LIU YE 
Tong, Zekun
Ruan, Sijie
Zheng, Yu
DAVID SAMUEL ROSENBLUM 
Issue Date: 2020
Publisher: IEEE Computer Society
Citation: Ouyang, Kun, LIANG YUXUAN, LIU YE, Tong, Zekun, Ruan, Sijie, Zheng, Yu, DAVID SAMUEL ROSENBLUM (2020). Fine-Grained Urban Flow Inference. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING. ScholarBank@NUS Repository.
Abstract: Spatially fine-grained urban flow data is critical for smart city efforts. Though fine-grained information is desirable for applications, it demands much more resources for the underlying storage system compared to coarse-grained data. To bridge the gap between storage efficiency and data utility, in this paper, we aim to infer fine-grained flows throughout a city from their coarse-grained counterparts. This task exhibits two challenges: the spatial correlations between coarse- and fine-grained urban flows, and the complexities of external impacts. To tackle these issues, we develop a model entitled UrbanFM which consists of two major parts: 1) an inference network to generate fine-grained flow distributions from coarse-grained inputs that uses a feature extraction module and a novel distributional upsampling module; 2) a general fusion subnet to further boost the performance by considering the influence of different external factors. This structure provides outstanding effectiveness and efficiency for small scale upsampling. However, the single-pass upsampling used by UrbanFM is insufficient at higher upscaling rates. Therefore, we further present UrbanPy, a cascading model for progressive inference of fine-grained urban flows by decomposing the original tasks into multiple subtasks. Compared to UrbanFM, such an enhanced structure demonstrates favorable performance for larger-scale inference tasks.
Source Title: IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
URI: https://scholarbank.nus.edu.sg/handle/10635/172615
ISSN: 1041-4347
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