Multiple local 3D CNNs for region-based prediction in smart cities

Abstract

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.