In smart cities, region-based prediction (e.g. traffic flow and electricity flow) is of great importance to city management and public safety, and it remains a daunting challenge that involves complicated spatial-temporal-related factors such as weather, holidays, events, etc. Region-based forecasting aims to predict the future situation for regions in a city based on historical data. In the existing literature, the state-of-the-art method solve region-based problems with long short-term memory (LSTM) algorithms that extract the temporal view and local convolutional neural network (CNN) algorithms that extract the spatial view (local spatial correlation via local CNN). In this paper, we propose a deep learning-based method for region-based prediction for smart cities. First, we divide the cities into regions based on the space dimension and model the situation of the cities in 3D volumes. Based on the constructed 3D volumes, we design a model called multiple local 3D CNN spatial-temporal residual networks (LMST3D-ResNet) for region-based prediction in smart cities. LMST3D-ResNet can extract multiple temporal dependencies (including trend, period and closeness) for local regions and then predict the future citywide activities according to the learned multiple spatial-temporal features. LMST3D-ResNet can also combine the spatial-temporal features with external factors. LMST3D-ResNet includes 3D CNNs and ResNet mechanisms for processing spatial-temporal information. In particular, 3D CNNs have the ability to model 3-dimensional information due to 3D convolution and 3D pooling operations, while ResNet enables the connection of the convolutional neural network across layers to obtain a deeper network structure. Specifically, in our proposed model, a novel region-based information extraction mechanism and an end-to-end multiple spatial-temporal dependency learning structure are designed for local regions. Extensive experimental results on two datasets, i.e., MLElectricity and BJTaxi demonstrate the superior performance of our proposed method over the exisiting state-of-the-art methods.

在智慧城市中，基于区域的预测（例如交通流量和电力流量）对于城市管理和公共安全至关重要，并且仍然是一项艰巨的挑战，涉及复杂的时空相关因素，例如天气，假期，事件，基于区域的预测旨在根据历史数据预测城市中区域的未来情况。在现有文献中，最先进的方法是利用提取时间视图的长短期记忆（LSTM）算法和提取空间视图（局部）的局部卷积神经网络（CNN）算法解决基于区域的问题通过本地CNN进行空间关联）。在本文中，我们提出了一种基于深度学习的方法，用于智能城市的基于区域的预测。第一，我们根据空间维度将城市划分为多个区域，并以3D体积建模城市的状况。基于构造的3D体积，我们设计了一个称为多个局部3D CNN时空残差网络（LMST3D-ResNet）的模型，用于智能城市中基于区域的预测。LMST3D-ResNet可以提取本地区域的多个时间相关性（包括趋势，周期和紧密度），然后根据学习到的多个时空特征预测未来的全市范围活动。LMST3D-ResNet还可以将时空特征与外部因素结合起来。LMST3D-ResNet包括3D CNN和ResNet机制，用于处理时空信息。特别地，由于3D卷积和3D池化操作，3D CNN能够对3D信息进行建模，ResNet可以跨层连接卷积神经网络以获得更深的网络结构。具体来说，在我们提出的模型中，针对本地区域设计了一种新颖的基于区域的信息提取机制和一个端到端的多个时空依赖学习结构。在两个数据集（即MLElectricity和BJTaxi）上的大量实验结果表明，我们提出的方法优于现有的最新方法。