DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction
DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction
复制标题
DeepCrowd：一个用于大规模城市人群密度和流量预测的深度模型
DOI：
10.1109/tkde.2021.3077056
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发表时间：
2023-01
影响因子：
8.9
通讯作者：
Renhe Jiang;Z. Cai;Zhaonan Wang;Chuang Yang;Z. Fan;Quanjun Chen;K. Tsubouchi;Xuan Song;R. Shi
中科院分区：
文献类型：
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作者：
Renhe Jiang;Z. Cai;Zhaonan Wang;Chuang Yang;Z. Fan;Quanjun Chen;K. Tsubouchi;Xuan Song;R. Shi
通过使用大数据和尖端人工智能技术，预测全市范围内的人群或交通的密度和流量成为可能。可广泛应用于应急管理、交通管制、城市规划等领域，已成为一个具有重要社会影响的研究课题。特别是，通过将大城市区域划分为多个细粒度网格，可以用4D张量(时间步长、高度、宽度、通道)来表示连续时间段内的城市范围内的人群和交通信息。基于这一思想，人们提出了一系列基于网格的城市人群和交通预测方法。在这项研究中，我们重新讨论了密度和进出流量预测问题，并发布了一个新的聚合人类流动性数据集，该数据集是从现实世界的智能手机应用程序中生成的。与已有的数据集相比，我们的数据集具有网格数大、网格尺寸细、用户样本量大等优点。针对这一大规模人群数据集，通过设计金字塔结构和基于卷积LSTM的高维注意机制，提出了一种新的深度学习模型DeepCrowd。最后，对DeepCrowd进行了全面和深入的性能评估，证明了DeepCrowd与多种最先进的方法相比的优越性。
Predicting the density and flow of the crowd or traffic at a citywide level becomes possible by using the big data and cutting-edge AI technologies. It has been a very significant research topic with high social impact, which can be widely applied to emergency management, traffic regulation, and urban planning. In particular, by meshing a large urban area to a number of fine-grained mesh-grids, citywide crowd and traffic information in a continuous time period can be represented with 4D tensor (Timestep, Height, Width, Channel). Based on this idea, a series of methods have been proposed to address grid-based prediction for citywide crowd and traffic. In this study, we revisit the density and in-out flow prediction problem and publish a new aggregated human mobility dataset generated from a real-world smartphone application. Comparing with the existing ones, our dataset holds several advantages including large mesh-grid number, fine-grained mesh size, and high user sample. Towards this large-scale crowd dataset, we propose a novel deep learning model called DeepCrowd by designing pyramid architectures and high-dimensional attention mechanism based on Convolutional LSTM. Lastly, thorough and comprehensive performance evaluations are conducted to demonstrate the superiority of the proposed DeepCrowd comparing to multiple state-of-the-art methods.