Multitime Resolution Hierarchical Attention-Based Recurrent Highway Networks for Taxi Demand Prediction
Multitime Resolution Hierarchical Attention-Based Recurrent Highway Networks for Taxi Demand Prediction
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DOI：
10.1155/2020/4173094
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发表时间：
2020-08
影响因子：
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通讯作者：
Baiping Chen;Wei Li
中科院分区：
文献类型：
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作者：
Baiping Chen;Wei Li
出租车需求预测是建设智慧城市的重要考虑因素。然而，需求数据中复杂的非线性时空关系使得很难构建准确的预测模型。考虑到一个单一的时间分辨率可能无法准确学习的时间模式的出租车需求，我们扩展了时间序列预测模型在我们提出的多时间分辨率层次的注意力为基础的递归公路网络（MTR-HRHN）模型，使用三个时间分辨率模型的时间接近，周期和趋势属性的需求数据，以捕捉一个更全面的时间模式。我们评估的MTR-HRHN的出租车出行记录数据集，结果表明，MTR-HRHN的预测性能超过了8个知名的方法在短期需求预测在一些高需求地区。
Taxi demand forecasting is an important consideration in building up smart cities. However, complex nonlinear spatiotemporal relationships in demand data make it difficult to construct an accurate prediction model. Considering that a single time resolution may not enable accurate learning of the time pattern of taxi demand, we expand the time series prediction model in our proposed multitime resolution hierarchical attention-based recurrent highway network (MTR-HRHN) model, using three time resolutions to model temporal closeness, period, and trend properties of demand data to capture a more comprehensive time pattern. We evaluate the MTR-HRHN on a taxi trip record dataset and the results show that the forecasting performance of the MTR-HRHN exceeds that of eight well-known methods in the short-term demand prediction in some high-demand regions.