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Predicting irregular individual movement following frequent mid-level disasters using location data from smartphones

Published: 31 October 2016 Publication History

Abstract

Mid-level disasters that frequently occur, such as typhoons and earthquakes, heavily affect human activities in urban areas by causing severe congestion and economic loss. Predicting the irregular movement of individuals following such disasters is crucial for managing urban systems. Past survey results show that mid-level disasters do not force many individuals to evacuate away from their homes, but do cause irregular movement by significantly delaying the movement timings, resulting in severe congestion in urban transportation. We propose a novel method that predicts such irregularity of individuals' movements in several mid-level disasters using various types of features including the victims' usual movement patterns, disaster information, and geospatial information of victims' locations. Using real GPS data of 1 million people in Tokyo, we show that our method can predict mobility delay with high accuracy,

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Cited By

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  • (2022)Multi-perspective convolutional neural networks for citywide crowd flow predictionApplied Intelligence10.1007/s10489-022-03980-953:8(8994-9008)Online publication date: 5-Aug-2022
  • (2021)Spatial Interpolation Techniques on Participatory Sensing DataACM Transactions on Spatial Algorithms and Systems10.1145/34576097:3(1-32)Online publication date: 8-Jun-2021
  • (2020)Intercity Simulation of Human Mobility at Rare Events via Reinforcement LearningProceedings of the 28th International Conference on Advances in Geographic Information Systems10.1145/3397536.3422244(293-302)Online publication date: 3-Nov-2020
  • Show More Cited By

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  1. Predicting irregular individual movement following frequent mid-level disasters using location data from smartphones

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      cover image ACM Other conferences
      SIGSPACIAL '16: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
      October 2016
      649 pages
      ISBN:9781450345897
      DOI:10.1145/2996913
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      New York, NY, United States

      Publication History

      Published: 31 October 2016

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      Author Tags

      1. GPS data
      2. L1-regularized logistic regression
      3. disaster alert
      4. frequent disasters
      5. urban dynamics

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      SIGSPACIAL '16 Paper Acceptance Rate 40 of 216 submissions, 19%;
      Overall Acceptance Rate 257 of 1,238 submissions, 21%

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      Cited By

      View all
      • (2022)Multi-perspective convolutional neural networks for citywide crowd flow predictionApplied Intelligence10.1007/s10489-022-03980-953:8(8994-9008)Online publication date: 5-Aug-2022
      • (2021)Spatial Interpolation Techniques on Participatory Sensing DataACM Transactions on Spatial Algorithms and Systems10.1145/34576097:3(1-32)Online publication date: 8-Jun-2021
      • (2020)Intercity Simulation of Human Mobility at Rare Events via Reinforcement LearningProceedings of the 28th International Conference on Advances in Geographic Information Systems10.1145/3397536.3422244(293-302)Online publication date: 3-Nov-2020
      • (2020)Big Data and Emergency Management: Concepts, Methodologies, and ApplicationsIEEE Transactions on Big Data10.1109/TBDATA.2020.2972871(1-1)Online publication date: 2020
      • (2019)An Analysis of Factors Influencing Disaster Mobility Using Location Data from Smartphones: Case Study of Western Japan FloodingJournal of Disaster Research10.20965/jdr.2019.p090314:6(903-911)Online publication date: 1-Sep-2019
      • (2019)Cross-comparative analysis of evacuation behavior after earthquakes using mobile phone dataPLOS ONE10.1371/journal.pone.021137514:2(e0211375)Online publication date: 20-Feb-2019
      • (2019)Predicting Evacuation Decisions using Representations of Individuals' Pre-Disaster Web Search BehaviorProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330697(2707-2717)Online publication date: 25-Jul-2019
      • (2019)Decision-Making System for Road-Recovery Considering Human Mobility by Applying Deep Q-Network2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9006385(4075-4084)Online publication date: Dec-2019
      • (2018)Online Deep Ensemble Learning for Predicting Citywide Human MobilityProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/32649152:3(1-21)Online publication date: 18-Sep-2018
      • (2017)CityFlowFragilityProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/31309821:3(1-17)Online publication date: 11-Sep-2017

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