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 a
city based on historical observations. Nowadays, Convolutional Neural Networks
(CNNs) have been widely adopted in raster-based crowd flow analytics by virtue
of their capability in capturing spatial dependencies. After revisiting
CNN-based methods for different analytics tasks, we expose two common critical
drawbacks in the existing uses: 1) inefficiency in learning global spatial
dependencies, and 2) overlooking latent region functions. To tackle these
challenges, in this paper we present a novel framework entitled DeepLGR that
can be easily generalized to address various citywide crowd flow analytics
problems. This framework consists of three parts: 1) a local feature extraction
module to learn representations for each region; 2) a global context module to
extract global contextual priors and upsample them to generate the global
features; and 3) a region-specific predictor based on tensor decomposition to
provide customized predictions for each region, which is very
parameter-efficient compared to previous methods. Extensive experiments on two
typical crowd flow analytics tasks demonstrate the effectiveness, stability,
and generality of our framework.

本文提出了一种名为 DeepLGR 的新型框架，以有效应对城市范围内人流量分析面临的挑战。该框架包含局部特征提取模块、全局上下文模块和基于张量分解的区域特定预测器，具有较高的效率、稳定性和泛化性能，能够有效学习全局空间关系和区域潜在功能。