Department of Computer Science and Engineering, Shanghai Jiao Tong University, China.
Department of Computer Science, Abdul Wali Khan University, Pakistan.
Neural Netw. 2022 Jan;145:233-247. doi: 10.1016/j.neunet.2021.10.021. Epub 2021 Oct 28.
The prediction of crowd flows is an important urban computing issue whose purpose is to predict the future number of incoming and outgoing people in regions. Measuring the complicated spatial-temporal dependencies with external factors, such as weather conditions and surrounding point-of-interest (POI) distribution is the most difficult aspect of predicting crowd flows movement. To overcome the above issue, this paper advises a unified dynamic deep spatio-temporal neural network model based on convolutional neural networks and long short-term memory, termed as (DHSTNet) to simultaneously predict crowd flows in every region of a city. The DHSTNet model is made up of four separate components: a recent, daily, weekly, and an external branch component. Our proposed approach simultaneously assigns various weights to different branches and integrates the four properties' outputs to generate final predictions. Moreover, to verify the generalization and scalability of the proposed model, we apply a Graph Convolutional Network (GCN) based on Long Short Term Memory (LSTM) with the previously published model, termed as GCN-DHSTNet; to capture the spatial patterns and short-term temporal features; and to illustrate its exceptional accomplishment in predicting the traffic crowd flows. The GCN-DHSTNet model not only depicts the spatio-temporal dependencies but also reveals the influence of different time granularity, which are recent, daily, weekly periodicity and external properties, respectively. Finally, a fully connected neural network is utilized to fuse the spatio-temporal features and external properties together. Using two different real-world traffic datasets, our evaluation suggests that the proposed GCN-DHSTNet method is approximately 7.9%-27.2% and 11.2%-11.9% better than the AAtt-DHSTNet method in terms of RMSE and MAPE metrics, respectively. Furthermore, AAtt-DHSTNet outperforms other state-of-the-art methods.
人群流动预测是一个重要的城市计算问题，其目的是预测区域内的未来进出人数。衡量与外部因素（如天气条件和周围兴趣点 (POI) 分布）的复杂时空依赖关系是预测人群流动运动的最困难方面。为了克服上述问题，本文提出了一种基于卷积神经网络和长短时记忆的统一动态深度时空神经网络模型，称为（DHSTNet），以同时预测城市中每个区域的人群流动。DHSTNet 模型由四个独立的组件组成：近期、日常、每周和外部分支组件。我们的方法同时为不同的分支分配不同的权重，并整合四个属性的输出以生成最终的预测。此外，为了验证所提出模型的泛化和可扩展性，我们应用了基于长短时记忆 (LSTM) 的图卷积网络 (GCN) 与之前发布的模型，称为 GCN-DHSTNet；以捕获空间模式和短期时间特征；并说明其在预测交通人群流量方面的卓越成就。GCN-DHSTNet 模型不仅描绘了时空依赖关系，还揭示了不同时间粒度的影响，分别为近期、日常、每周周期性和外部属性。最后，一个全连接神经网络用于融合时空特征和外部属性。使用两个不同的真实交通数据集进行评估，我们的评估表明，所提出的 GCN-DHSTNet 方法在 RMSE 和 MAPE 指标上分别比 AAtt-DHSTNet 方法好约 7.9%-27.2%和 11.2%-11.9%。此外，AAtt-DHSTNet 优于其他最先进的方法。
