GSEN: An ensemble deep learning benchmark model for urban hotspots spatiotemporal prediction

Abstract

Urban hotspots spatiotemporal prediction is a long-term but challenging task for urban management and smart city construction. Accurate urban hotspots spatiotemporal prediction can improve urban planning, scheduling and, security capability, reduce resource consumption. Existing deep spatiotemporal prediction methods mainly utilize geographic grid based image, some given network structure or some additional data to capture spatiotemporal dynamic. However, we observed that mining some latent self-semantics from raw data and fusing them with geospatial based grid images can also improve the performance of spatiotemporal predictions. In this paper, we propose Geographic-Semantic Ensemble Neural Network (GSEN), a novel deep learning approach to stack geographical prediction neural network and semantical prediction neutral network. GSEN model integrates the structures of Predictive Recurrent Neural Network (PredRNN), Graph Convolutional Predictive Recurrent Neural Network (GC-PredRNN), and Ensemble Layer to capture spatiotemporal dynamics from different views. And this model can also be correlated with some latent high-level dynamics in the real-world without any external data. We evaluate our proposed model on three different domains real-world datasets and the experimental studies demonstrate the generalization and effectiveness of GSEN in different urban hotspots spatiotemporal prediction tasks.