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Early Warning of Human Crowds Based on Query Data from Baidu Maps: Analysis Based on Shanghai Stampede
4 references·16 citing·45 second hop
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References

  • 2008Understanding individual human mobility patterns6,071 cited
  • 2013Unravelling daily human mobility motifs584 cited
  • 1996Estimation and prediction of time-dependent origin-destination flows142 cited
  • 2012An unsupervised framework for sensing individual and cluster behavior patterns from human mobile data64 cited

Cited by

  • 2020Applications of artificial intelligence for disaster management488 cited
  • 2017Measuring economic activity in China with mobile big data63 cited
  • 2020Data-driven framework for delineating urban population dynamic patterns: Case study on Xiamen Island, China61 cited
  • 2018A mobility network approach to identify and anticipate large crowd gatherings61 cited
  • 2018Online Deep Ensemble Learning for Predicting Citywide Human Mobility53 cited
  • 2021Prediction of human activity intensity using the interactions in physical and social spaces through graph convolutional networks50 cited
  • 2020Walking as a service – Does it have legs?33 cited
  • 2019Urban human activity density spatiotemporal variations and the relationship with geographical factors: An exploratory Baidu heatmaps‐based analysis of Wuhan, China32 cited
  • 2019Decentralized Attention-based Personalized Human Mobility Prediction32 cited
  • 2020Artificial Intelligence and Early Warning Systems

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30 cited
  • 2020Crowd Detection in Mass Gatherings Based on Social Media Data: A Case Study of the 2014 Shanghai New Year’s Eve Stampede29 cited
  • 2020A Method for the Estimation of Finely-Grained Temporal Spatial Human Population Density Distributions Based on Cell Phone Call Detail Records26 cited
  • 2021Characterizing the complex influence of the urban built environment on the dynamic population distribution of Shenzhen, China, using geographically and temporally weighted regression22 cited
  • 2021Understanding the spatiotemporal patterns of nighttime urban vibrancy in central Shanghai inferred from mobile phone data21 cited
  • 2019Large-Scale, Fine-Grained, Spatial, and Temporal Analysis, and Prediction of Mobile Phone Users’ Distributions Based upon a Convolution Long Short-Term Model16 cited
  • 2019Fine-Grained Dynamic Population Mapping Method Based on Large-Scale Sparse Mobile Phone Data11 cited