Urban population density estimation based on spatio‐temporal trajectories. (6th February 2020)

• Record Type: Journal Article
• Authors: Xue, Fei; Cao, Yang; Ding, Zhiming; Tang, Hengliang; Yang, Xi; Chen, Lei; Li, Juntao
• Is Part Of: Concurrency and computation. Volume 32:Number 14(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-02-06
• Subjects: deep learning -- LSTM‐CNN -- population density prediction -- spatio‐temporal trajectory; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5685
• Languages: English
• ISSNs: 1532-0626

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.