UrbanFM: Inferring Fine-Grained Urban Flows

Abstract

Urban flow monitoring systems play important roles in smart city efforts around the world. However, the ubiquitous deployment of monitoring devices, such as CCTVs, induces a long-lasting and enormous cost for maintenance and operation. This suggests the need for a technology that can reduce the number of deployed devices, while preventing the degeneration of data accuracy and granularity. In this paper, we aim to infer the real-time and fine-grained crowd flows throughout a city based on coarse-grained observations. This task is challenging due to two reasons: the spatial correlations between coarse- and fine-grained urban flows, and the complexities of external impacts. To tackle these issues, we develop a method entitled UrbanFM based on deep neural networks. Our model consists of two major parts: 1) an inference network to generate fine-grained flow distributions from coarse-grained inputs by using a feature extraction module and a novel distributional upsampling module; 2) a general fusion subnet to further boost the performance by considering the influences of different external factors. Extensive experiments on two real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness and efficiency of our method compared to seven baselines, demonstrating the state-of-the-art performance of our approach on the fine-grained urban flow inference problem.

References in corpus (3)

• Adam: A Method for Stochastic Optimization
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• Cross-City Transfer Learning for Deep Spatio-Temporal Prediction

Cited by in corpus (10)

• Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction
• Graph Neural Processes for Spatio-Temporal Extrapolation
• Periodic Residual Learning for Crowd Flow Forecasting
• Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner
• Revisiting Convolutional Neural Networks for Citywide Crowd Flow Analytics
• Fine-Grained Urban Flow Inference
• Fine-Grained Traffic Inference from Road to Lane via Spatio-Temporal Graph Node Generation
• One-shot Transfer Learning for Population Mapping
• Leakage-Free Evaluation and Distribution-Robust Spatio-Temporal Graph Learning for Inductive Kriging
• Self-Checking Deep Neural Networks in Deployment