基于时空循环卷积网络的城市区域人口流量预测

摘要/Abstract

摘要： 城市区域人口流量的准确预测可以为交通监管和市民出行提供有效的决策支持。城市各区域人口流量同时具有时间维度上的变化规律和空间维度上的相关性,这给流量的精准预测带来了极大的挑战。文中提出了一种基于注意力机制的时空循环卷积网络(ASTRCNs)模型,可以全面地对影响区域人口流量的多种因素进行统一建模。ASTRCNs共包含3个组件,分别用于描述人口流量的短时依赖关系、日周期规律、周周期规律。在真实的北京市人口流量数据集上进行了实验,结果表明ASTRCNs模型的预测效果优于传统的时间序列预测模型以及其他现有的基于深度学习的人口流量预测模型。

关键词: 人口流量预测, 深度学习, 时空数据, 循环卷积网络

Abstract: Accurately forecasting the crowd flows in urban areas can provide effective decision-making support for traffic management and citizens’ travel.The crowd flows in each urban region have strong correlations in both temporal dimensionsand spatial dimensions.These complex factors bring great challenges to accurate predictions.A novel neural network structure named attention-based spatio-temporal recurrent convolution networks (ASTRCNs) was proposed,which can simultaneously model various factors that affect the crowd flows.ASTRCNs consists of three components,which can respectively capture the short-term dependences,the daily periodicity influence and the weekly patterns of the crowd flows.Experimental results on a real data set of crowd flows in Beijing demonstrate that the proposed ASTRCNs outperforms the classical time series methods and the existing deep-learning based prediction methods.

Key words: Crowd flows prediction, Deep learning, Recurrent convolutional networks, Spatio-Temporal data

中图分类号:

TP181

郭晟楠, 林友芳, 金文蔚, 万怀宇. 基于时空循环卷积网络的城市区域人口流量预测[J]. 计算机科学, 2019, 46(6A): 385-391. https://doi.org/

GUO Sheng-nan, LIN You-fang, JIN Wen-wei, WAN Huai-yu. Citywide Crowd Flows Prediction Based on Spatio-Temporal Recurrent Convolutional Networks[J]. Computer Science, 2019, 46(6A): 385-391. https://doi.org/

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