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Urban population density estimation based on spatio‐temporal trajectories
Author(s) -
Xue Fei,
Cao Yang,
Ding Zhiming,
Tang Hengliang,
Yang Xi,
Chen Lei,
Li Juntao
Publication year - 2020
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5685
Subject(s) - computer science , raster graphics , population , artificial intelligence , term (time) , deep learning , long short term memory , machine learning , data mining , density estimation , statistics , artificial neural network , mathematics , recurrent neural network , physics , demography , quantum mechanics , estimator , sociology
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.