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Particle filter for real-time human mobility prediction following unprecedented disaster

Published: 31 October 2016 Publication History

Abstract

Real-time estimation of human mobility following a massive disaster will play a crucial role in disaster relief. Because human mobility in massive disasters is quite different from their usual mobility, real-time human location data is necessary for precise estimation. Due to privacy concerns, real-time data is anonymized and a popular form of anonymization is population distribution. In this paper, we aim to estimate human mobility following an unprecedented disaster using such population distribution data. To overcome technical obstacles including high dimensionality, we propose novel particle filter by devising proposal distribution. Our proposal distribution provides states considering both prediction model and acquired observation. Therefore, particles maintain high likelihood. In the experiments, our methods realized more accurate estimation than the baselines, and its estimated mobility was consistent with the survey researches. The computational cost is significantly low enough for real-time operations. The GPS data collected on the day of the Great East Japan Earthquake is used for the evaluation.

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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 the author(s) 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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    Published: 31 October 2016

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    Author Tags

    1. GPS data
    2. bayesian inference
    3. disaster management
    4. human mobility prediction

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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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    • (2024)Forecasting Citywide Crowd Transition Process via Convolutional Recurrent Neural NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2023.3310789(1-13)Online publication date: 2024
    • (2022)Crowd flow forecasting via agent-based simulations with sequential latent parameter estimation from aggregate observationScientific Reports10.1038/s41598-022-14646-412:1Online publication date: 1-Jul-2022
    • (2021)DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow PredictionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3077056(1-1)Online publication date: 2021
    • (2020)Live SimulationsProceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3398761.3398961(1721-1725)Online publication date: 5-May-2020
    • (2020)People Flow Prediction by Multi-Agent Simulatorマルチエージェントシミュレータを用いたリアルタイム人流予測Transactions of the Japanese Society for Artificial Intelligence10.1527/tjsai.D-wd0535:2(D-wd05_1-10)Online publication date: 1-Mar-2020
    • (2020)Supervised-CityProphetProceedings of the 28th International Conference on Advances in Geographic Information Systems10.1145/3397536.3422219(175-178)Online publication date: 3-Nov-2020
    • (2020)Considering a Method for Generating Human Mobility Model by Reinforcement LearningAdvances in Intelligent Networking and Collaborative Systems10.1007/978-3-030-57796-4_12(121-132)Online publication date: 21-Aug-2020
    • (2019)Intrinsic dimensionality of human behavioral activity dataPLOS ONE10.1371/journal.pone.021896614:6(e0218966)Online publication date: 27-Jun-2019
    • (2019)Enhancing a Crowd-based Delivery Network with Mobility PredictionsProceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility10.1145/3356995.3364542(66-75)Online publication date: 5-Nov-2019
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