Urban population density estimation based on spatio‐temporal trajectories

Abstract

Summary Regional population density has temporal and spatial characteristics, and most of the existing prediction models fail to take these two characteristics into account at the same time, which results in unsatisfactory forecasting results. To address this problem, we use the deep learning models to predict the crowd distribution in the evacuation area, so as to realize the recommendation of the evacuation area. First, a raster population density prediction model based on long short‐term memory (LSTM) is studied, and then a multiarea population density prediction model considering temporal and spatial characteristics, named ST‐LSTM, is designed. The results of our extensive experiments on the real dataset show that our proposed ST‐LSTM is both effective and efficient.

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