Spatial Attention Based Grid Representation Learning For Predicting Origin?Destination Flow
Spatial Attention Based Grid Representation Learning For Predicting Origin?Destination Flow
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基于空间注意力的网格表示学习用于预测出发点？目的地流
DOI：
10.1109/bigdata55660.2022.10021023
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Sekimoto Yoshihide
中科院分区：
文献类型：
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作者：
Cai Mingfei;Pang Yanbo;Sekimoto Yoshihide
OD流数据是城市规划和交通系统设计的重要数据。这些数据适合于描述宏观层面的运动。然而，大规模收集它们，例如在城市中，是具有挑战性的。它们的形式不兼容性使得将它们用于其他任务变得困难。因此，我们提出了一个深度模型来学习城市内网格上有意义的OD信息，以解决这些问题。我们从几个开源数据集中收集了区域的多模态特征，如道路网络密度和设施分布，并将其用作网格信号。然后，我们构建了一个基于空间注意力的深度图网络来生成网格嵌入，并使用它们来预测OD量。所提出的方法进行了评估，对一组基线方法，使用真实世界的数据集在日本。分析表明，我们的模型可以提取更准确的潜在的地形信息OD图，并产生合理的网格嵌入，这些表示适用于其他下游任务。
Origin–destination (OD) flow d ata a re c ritical for urban planning and traffic system design. Such data are suitable for describing movement at the macroscopic level. However, collecting them on a large scale, such as in a city, is challenging. Their form incompatibility makes using them for other tasks difficult. Therefore, we propose a deep model to learn meaningful OD information on grids within a city to address these problems. We collected multimodal characteristics of regions, such as road network densities and facility distributions, from several open-source datasets and used them as grid signals. We then constructed a spatial attention-based deep graph network to generate grid embeddings and used them to predict the OD volumes. The proposed method was evaluated against a set of baseline approaches using a real-world dataset in Japan. The analysis indicated that our model can extract more accurate latent topographical information from OD graphs and produce reasonable grid embeddings; these representations apply to other downstream tasks.