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(AAAI 2017) Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction

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Keyword [Citywide Crowd Flows]

Zhang J, Zheng Y, Qi D. Deep spatio-temporal residual networks for citywide crowd flows prediction[C]//Thirty-First AAAI Conference on Artificial Intelligence. 2017.

对于公共安全和交通管理而言，预测人群流动具有重要意义，但它受到事件、天气和区域间等复杂因素的影响，因此该篇论文提出一个基于深度学习的方法 (ST-ResNet, Figure 3)综合预测一个城市中各区域人群的流入 (inflow)与流出 (outflow) (Figure 1).





ST-ResNet基于时间和空间数据 (spatio-temporal data)，对temprtaol closeness, period和trend 3阶段交通拥挤程度进行建模，并结合external factors对进行预测。论文基于两个数据集Beijing和New York City (NYC)进行实验，并与6个baseline进行对比。

  • Mobile phone signal of pedestrians.
  • GPS trajectories of vehicles.

1. Complex Factors

  • Spatial dependencies. Region的inflow受到附近regions的outflow影响，甚至受更远的regions影响。同时region的inflow也会影响它自身的outflow.
  • Temporal dependencies. Region的flows也受到近期时间段的影响。例如，8点的交通阻塞可能会影响到9点的交通情况；每个工作日的高峰期相似等。
  • External influence. 天气条件、事件等。

2. Contribution

  • 使用Conv来model任意两region之间的spatial dependencies.
  • 使用3个ResNet来model temporal closeness, period和trend的temporal dependencies.
  • Assigning different weights to aggregate 3 outputs of ResNet, and then aggregate external factors.
  • Outperform 6 baselines on Beijing and NYC dataset.

3. Preliminaries

  • Region. 将城市划分为grid map (Figure 2).
  • Flow (2 channels). 归一化到[-1,1].


4. ST-ResNet (Figure 3)

  • ST-ResNet
    • Include temporal closeness (recent), period (near), trend (distant) and external 4个主要部分。
  • Temporal closeness (recent), period (near), trend (distant)的ResNet结构相同，其中包含2个Conv和L个Residual (Figure 4). 由于地铁或高速公路会导致两个很远的region具有很强的关联性，因此使用具有层级性的stack Conv来实现，并利用residual的形式来提高stack Conv的收敛性。Relu之前增加了ReLU层。


  • External通过两层FC. Regard as embedding layer + mapping layer (same shape as Xt).
  • Fusion包含3个可学习的权重参数，乘以对应的3个ResNet输出后相加，其结果再与FC输出相加，经过tanh激活函数。




  • MSE loss, Adam.


5. Algorithm

  • p: one day.
  • q: one-week.


6. DataSet (Table 1)

  • TaxiBJ. Taxicab GPS and moteorology data. 选择最近4周作为testing data.
  • BikeNYC. NYC Bike system. 选择最近10天作为testing data.


  • (Figure 5) 假期和天气会影响北京办公区的流量。


  • (Figure 6) recent时间段的相关性更大，办公区周末的流量较低，办公区流量呈现下降趋势，居住区呈现上升趋势。


7. Baseline

  • HA. 历史inflow,outflow均值。
  • ARIMA. Auto-Regressive Integrated Moving Average. 用于预测时间序列的值。
  • SARIMA. Seasonal ARIMA.
  • VAR. Vector Auto-Regressive(VAR), spatio-temporal model.
  • ST-ANN. extracts spatial (nearby 8 regions’ values) and temporal (8 previous time intervals) features, then fed into an artificial neural network.
  • DeepST. State-of-the-art. DNN for spatio-temporal data. 4 variants: DeepST-C, DeepST-CP, DeepST-CPT, and DeepST-CPTM. Focus on temporal dependencies and external factors.

8. Experiments

  • Theano&Keras.
  • Conv2: 2 filters.
  • Evaluation Metric: Root MSE.






9. Related Work

  • Conv: capture spatial dependencies.
  • RNN: capture temporal denpendencies.
  • ConvLSTM: capture spatial and temporal denpendencies. But can not model very long-range temporal denpendencies(period and trend).

10. Future

Consider other types of flows: taxi, truck, bus, phone signal, metro card swiping.