Data-driven simulation of urban human mobility constrained by natural disasters
Authors: 
Qi Wang, John E. TaylorAuthors Info & Claims
Pages 3357 - 3364
Abstract
Understanding of human movements in urban areas plays a key role in improving our disaster response, evacuation, and relief plans. However, there is a lack of research on human mobility perturbation under the influence of hurricanes. Furthermore, limited simulation studies have had access to empirical human travel data in urban areas during natural disasters. In this paper we developed a computational model to simulate human mobility during the approaching and strike of hurricanes. Inspired by animal movements in a fragmented habitat, we examined human movements in New York City and its adjacent areas during the striking of Hurricane Sandy. Based on the patterns observed, we established a data-driven model to simulate human movements during hurricanes. The model integrated multiple resources of urban informatics including U.S. census data, Twitter data, and Google Maps. The research effort aims to inform policymakers and support decision-making under different emergency situations that can arise during hurricanes.
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Published In
December 2016
3974 pages
ISBN:9781509044849
- General Chair:
- Todd Huschka,
- Program Chair:
- Stephen E. Chick
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- IIE: Institute of Industrial Engineers
- INFORMS-SIM: Institute for Operations Research and the Management Sciences: Simulation Society
- SIGSIM: ACM Special Interest Group on Simulation and Modeling
- SCS: Society for Modeling and Simulation International
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- SAS
- AnyLogic: The AnyLogic Company
- Palgrave: Palgrave Macmillan
- FlexSim: FlexSim Software Products, Inc.
- ASA: American Statistical Association
- IEEE/SMC: Institute of Electrical and Electronics Engineers: Systems, Man, and Cybernetics Society
- Simio: Simio LLC
- ODU: Old Dominion University
- ASIM: Arbeitsgemeinschaft Simulation
- ExtendSim: ExtendSim
- NIST: National Institute of Standards & Technology
- Amazon Simulations: Amazon Simulations
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IEEE Press
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Published: 11 December 2016
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Overall Acceptance Rate 3,413 of 5,075 submissions, 67%
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