Transfer Urban Human Mobility via POI Embedding over Multiple Cities
Transfer Urban Human Mobility via POI Embedding over Multiple Cities
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DOI：
10.1145/3416914
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发表时间：
2021-01
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通讯作者：
Renhe Jiang;Xuan Song;Z. Fan;Tianqi Xia;Zhaonan Wang;Quanjun Chen;Z. Cai;R. Shibasaki
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文献类型：
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作者：
Renhe Jiang;Xuan Song;Z. Fan;Tianqi Xia;Zhaonan Wang;Quanjun Chen;Z. Cai;R. Shibasaki
快速发展的位置采集技术为理解和预测城市人员流动提供了强有力的工具，对城市规划、交通管制和应急管理具有重要意义。然而，利用现有的方法，仍然很难准确预测东京、上海和香港等大城市地区数百万人的流动性，特别是当用于模型训练的收集数据通常仅限于总人口的一小部分时。显然，人类在城市中的活动与兴趣点（POI）信息密切相关，而兴趣点信息可以反映人类活动的语义意义。这促使我们将人类出行数据和城市POI数据融合在一起，在有限的训练数据下提高预测性能，但目前的融合技术很难处理这两种异构数据。因此，我们提出了一种独特的poi嵌入机制，该机制按类别聚合区域poi，为每个城市网格生成人工poi图像，并以类似于短视频的方式将每个轨迹片段丰富为四维张量。然后，我们设计了一个结合CNN和LSTM的深度学习架构，从丰富的轨迹中同时捕获时空和地理信息。此外，通过迁移学习将交通知识从一个城市转移到另一个城市，我们可以充分利用其他城市的数据，在数据有限的情况下，为目标城市训练更强的模型。最后，我们使用有限数量的轨迹作为训练数据，在城市层面上取得了令人满意的人类流动性预测效果，并在五个不同类型和规模的城市区域上进行了验证。
Rapidly developing location acquisition technologies provide a powerful tool for understanding and predicting human mobility in cities, which is very significant for urban planning, traffic regulation, and emergency management. However, with the existing methodologies, it is still difficult to accurately predict millions of peoples’ mobility in a large urban area such as Tokyo, Shanghai, and Hong Kong, especially when collected data used for model training are often limited to a small portion of the total population. Obviously, human activities in city are closely linked with point-of-interest (POI) information, which can reflect the semantic meaning of human mobility. This motivates us to fuse human mobility data and city POI data to improve the prediction performance with limited training data, but current fusion technologies can hardly handle these two heterogeneous data. Therefore, we propose a unique POI-embedding mechanism, that aggregates the regional POIs by categories to generate an artificial POI-image for each urban grid and enriches each trajectory snippet to a four-dimensional tensor in an analogous manner to a short video. Then, we design a deep learning architecture combining CNN with LSTM to simultaneously capture both the spatiotemporal and geographical information from the enriched trajectories. Furthermore, transfer learning is employed to transfer mobility knowledge from one city to another, so that we can fully utilize other cities’ data to train a stronger model for the target city with only limited data available. Finally, we achieve satisfactory performance of human mobility prediction at the citywide level using a limited amount of trajectories as training data, which has been validated over five urban areas of different types and scales.