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Citywide crowd flow analytics is of great importance to smart city efforts.It aims to model the crowd flow (e.g., inflow and outflow) of each region in acity based on historical observations. Nowadays, Convolutional Neural Networks(CNNs) have been widely adopted in raster-based crowd flow analytics by virtueof their capability in capturing spatial dependencies. After revisitingCNN-based methods for different analytics tasks, we expose two common criticaldrawbacks in the existing uses: 1) inefficiency in learning global spatialdependencies, and 2) overlooking latent region functions. To tackle thesechallenges, in this paper we present a novel framework entitled DeepLGR thatcan be easily generalized to address various citywide crowd flow analyticsproblems. This framework consists of three parts: 1) a local feature extractionmodule to learn representations for each region; 2) a global context module toextract global contextual priors and upsample them to generate the globalfeatures; and 3) a region-specific predictor based on tensor decomposition toprovide customized predictions for each region, which is veryparameter-efficient compared to previous methods. Extensive experiments on twotypical crowd flow analytics tasks demonstrate the effectiveness, stability,and generality of our framework.

Revisiting Convolutional Neural Networks for Citywide Crowd Flow Analytics