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Jointly Modeling Spatio-Temporal Dependencies and Daily Flow Correlations for Crowd Flow Prediction

机译:共同建模时空依赖性和人群流动预测日常流动相关性

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Crowd flow prediction is a vital problem for an intelligent transportation system construction in a smart city. It plays a crucial role in traffic management and behavioral analysis, thus it has raised great attention from many researchers. However, predicting crowd flows timely and accurately is a challenging task that is affected by many complex factors such as the dependencies of adjacent regions or recent crowd flows. Existing models mainly focus on capturing such dependencies in spatial or temporal domains and fail to model relations between crowd flows of distant regions. We notice that each region has a relatively fixed daily flow and some regions (even very far away from each other) may share similar flow patterns which show strong correlations among them. In this article, we propose a novel model named Double-Encoder which follows a general encoder-decoder framework for multi-step citywide crowd flow prediction. The model consists of two encoder modules named ST-Encoder and FR-Encoder to model spatial-temporal dependencies and daily flow correlations, respectively. We conduct extensive experiments on two real-world datasets to evaluate the performance of the proposed model and show that our model consistently outperforms state-of-the-art methods.
机译:人群流量预测是智能城市智能交通系统建设的重要问题。它在交通管理和行为分析中起着至关重要的作用,因此它从许多研究人员中提出了很大的关注。然而,预测人群及时,准确地是一个具有挑战性的任务,这些任务受到许多复杂因素的影响,例如相邻地区或最近人群流动的依赖性。现有模型主要集中在空间或时间域中的这种依赖性,并且无法模拟遥远地区人群流动之间的关系。我们注意到每个地区的日常流动和一些区域(甚至远离彼此)可以共享类似的流动模式,其在它们之间显示出强烈的相关性。在本文中,我们提出了一种名为Double-ocdoder的新型模型,该模型跟随用于多步广告流量预测的一般编码器 - 解码器框架。该模型包括两个名为ST-CONODER和FR-CONODER的编码器模块,分别为模型空间依赖性和日常流相关性。我们对两个现实世界数据集进行了广泛的实验,以评估所提出的模型的性能,并表明我们的模型始终如一地优于最先进的方法。

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