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Author (up) Lida Huang; Tao Chen; Yan Wang; Hongyong Yuan pdf 
  Title Forecasting Daily Pedestrian Flows in the Tiananmen Square Based on Historical Data and Weather Conditions Type Conference Article
  Year 2015 Publication ISCRAM 2015 Conference Proceedings 12th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2015  
  Volume Issue Pages  
  Keywords APSO-SVR; forecasting; historical data; Pedestrian flows; weather conditions  
  Abstract It is important to forecast the pedestrian flows for organizing crowd activities and making risk assessments. In this article, the daily pedestrian flows in the Tiananmen Square are forecasted based on historical data, the distribution of holidays and weather conditions including rain, wind, temperature, relative humidity, and AQI (Air Quality Index). Three different methods have been discussed and the Support Vector Regression based on the Adaptive Particle Swarm Optimization (APSO-SVR) has been proved the most reliable and accurate model to forecast the daily pedestrian flows. The results of this paper can help to conduct security pre-warning system and enhance emergency preparedness and management for crowd activities.  
  Address  
  Corporate Author Thesis  
  Publisher University of Agder (UiA) Place of Publication Kristiansand, Norway Editor L. Palen; M. Buscher; T. Comes; A. Hughes  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 9788271177881 Medium  
  Track Planning, Foresight and Risk Analysis Expedition Conference ISCRAM 2015 Conference Proceedings 12th International Conference on Information Systems for Crisis Response and Management  
  Notes Approved yes  
  Call Number Serial 1315  
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