Attentive Crowd Flow Machines
Attentive Crowd Flow Machines
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DOI：
10.1145/3240508.3240681
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发表时间：
2018-09
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影响因子：
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通讯作者：
Lingbo Liu;Ruimao Zhang;Jiefeng Peng;Guanbin Li;Bowen Du;Liang Lin
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作者：
Lingbo Liu;Ruimao Zhang;Jiefeng Peng;Guanbin Li;Bowen Du;Liang Lin
交通流预测对于城市交通管理和公共安全至关重要。其关键挑战在于如何自适应地整合影响流量变化的各种因素。在本文中，我们提出了一个统一的神经网络模块来解决这个问题，称为Attentive Crowd Flow Machine~（ACFM），它能够通过学习具有注意力机制的随时间变化的数据的动态表示来推断人群流的演变。具体来说，ACFM由两个渐进式ConvLSTM单元组成，它们与卷积层相连，用于空间权重预测。第一个LSTM将顺序流密度表示作为输入，并在每个时间步生成隐藏状态以进行注意力地图推理，而第二个LSTM旨在从注意力加权的人群流特征中学习有效的时空特征表达。在ACFM的基础上，我们进一步构建了一个应用于城市人群流量预测的深层架构，该架构自然地包含了顺序和周期数据以及其他外部影响。在两个标准基准上进行了广泛的实验（即，北京和纽约市的人群流）表明，所提出的方法实现了比现有技术方法的显著改进。
Traffic flow prediction is crucial for urban traffic management and public safety. Its key challenges lie in how to adaptively integrate the various factors that affect the flow changes. In this paper, we propose a unified neural network module to address this problem, called Attentive Crowd Flow Machine~(ACFM), which is able to infer the evolution of the crowd flow by learning dynamic representations of temporally-varying data with an attention mechanism. Specifically, the ACFM is composed of two progressive ConvLSTM units connected with a convolutional layer for spatial weight prediction. The first LSTM takes the sequential flow density representation as input and generates a hidden state at each time-step for attention map inference, while the second LSTM aims at learning the effective spatial-temporal feature expression from attentionally weighted crowd flow features. Based on the ACFM, we further build a deep architecture with the application to citywide crowd flow prediction, which naturally incorporates the sequential and periodic data as well as other external influences. Extensive experiments on two standard benchmarks (i.e., crowd flow in Beijing and New York City) show that the proposed method achieves significant improvements over the state-of-the-art methods.