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Quantitative decision-making for the next generation of smarter evacuations

Fry, John; Galla, Tobias; Binner, Jane

Authors

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Dr John Fry J.M.Fry@hull.ac.uk
Senior Lecturer in Applied Mathematics

Tobias Galla

Jane Binner



Contributors

John Preston
Editor

Jane M Binner
Editor

Layla Branicki
Editor

Tobias Galla
Editor

Nick Jones
Editor

James King
Editor

Magdalini Kolokitha
Editor

Michalis Smyrnakis
Editor

Abstract

In this chapter we discuss the mathematical modelling of the next generation of smarter evacuations. Alongside a burgeoning literature on resilience we formulate a quantitative decision-making framework through which Social Media can be used to deliver more efficient evacuations. Our approach is flexible and improves upon existing models by allowing incoming information to be incorporated sequentially. Further, our model is the first of its kind to consider the effects of information quality (including abuse) and over-crowding upon network systems. In a high-quality information regime the potential benefits of Social Media increase as the size of the potential delays increases. Simulation results show that by not using updated information, as proposed in this study, final evacuation times are increased by 20 % and in some cases can be more than doubled. In a low-quality regime Social Media provides noisy information and other alternatives—including random allocation strategies and peer-to-peer communication—may be more effective.

Citation

Fry, J., Galla, T., & Binner, J. (2014). Quantitative decision-making for the next generation of smarter evacuations. In J. Preston, J. M. Binner, L. Branicki, T. Galla, N. Jones, J. King, …M. Smyrnakis (Eds.), City Evacuations: An Interdisciplinary Approach (63-87). Berlin: Springer. https://doi.org/10.1007/978-3-662-43877-0_4

Online Publication Date Aug 2, 2014
Publication Date 2014
Deposit Date Feb 4, 2022
Publisher Springer
Pages 63-87
Book Title City Evacuations: An Interdisciplinary Approach
ISBN 9783662438763; 9783662512364
DOI https://doi.org/10.1007/978-3-662-43877-0_4
Public URL https://hull-repository.worktribe.com/output/3920963