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Spatial and temporal analysis of pedestrian egress behavior and efficiency

Published: 07 November 2007 Publication History

Abstract

This research reports on exploring analytical methodologies for spatio-temporal data of pedestrian egress dynamics in a crowded environment. The research objective is to spatially and temporally quantify, visualize, and examine pedestrian egress behaviors and efficiency. The data of pedestrian dynamics on four different egress scenarios were collected with the use of Global Positioning System. The data were spatially analyzed with the measurement of tortuosity, which is a property of a movement path being tortuous. Specifically, fractal analysis was employed for quantifying tortuosity of movement paths and answering two specific research questions; 1) how does the structure of egress route affect the egress efficiency; 2) how does the pedestrian mode impact the egress efficiency? In terms of spatio-temporal analysis, the data of each path were visualized as a 3D space-time path, which is an individual trajectory starting from its origin and ending at its destination when using two-dimensional plain to show geographical positions and use perpendicular dimension to represent the time. Providing the 3D visualization of space-time paths helps to qualitatively and quantitatively analyze the spatio-temporal patterns and tendencies for pedestrian egress dynamics.

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  1. Spatial and temporal analysis of pedestrian egress behavior and efficiency

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    cover image ACM Other conferences
    GIS '07: Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
    November 2007
    439 pages
    ISBN:9781595939142
    DOI:10.1145/1341012
    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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    • ESRI
    • Google Inc.
    • Oak Ridge National Laboratory
    • Microsoft: Microsoft

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 07 November 2007

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    Author Tags

    1. GIS
    2. GPS
    3. fractal
    4. pedestrian behavior
    5. spatial analysis
    6. spatio-temporal

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