Document Type

Article

Publication Date

2018

DOI

https://doi.org/10.1016/j.procs.2018.04.074

Publication Title

Procedia Computer Science

Volume

130

Pages

836-843

Abstract

Transportation modeling and simulation play an important role in the planning and management of emergency evacuation. It is often indispensable for the preparedness and timely response to extreme events occurring in highly populated areas. Reliable and robust agent-based evacuation models are of great importance to support evacuation decision making. Nevertheless, these models rely on numerous hypothetical causal relationships between the evacuation behavior and a variety of factors including socio-economic characteristics and storm intensity. Understanding the impacts of these factors on evacuation behaviors (e.g., destination and route choices) is crucial in preparing optimal evacuation plans. This paper aims to contribute to the literature by integrating well-calibrated behavior models with an agent-based evacuation simulation model in the context of hurricane evacuation. Specifically, discrete choice models were developed to estimate the evacuation behaviors based on large-scale survey data in Northern New Jersey. Monte-Carlo Markov Chain (MCMC) sampling method was used to estimate evacuation propensity and destination choices for the whole population. Finally, evacuation of over a million residents in the study area was simulated using agent-based simulation built in MATSim. The agent-based modeling framework proposed in this paper provides an integrated methodology for evacuation simulation with specific consideration of agents’ behaviors. The simulation results need to be further validated and verified using real-world evacuation data.

Comments

Article is open access under the Creative Commons Attribution Non-Commercial license.

© 2018 The Authors. Published by Elsevier B.V.

Original Publication Citation

Zhu, Y., Xie, K., Ozbay, K., & Yang, H. (2018). Hurricane evacuation modeling using behavior models and scenario-driven agent-based simulations. Procedia Computer Science, 130, 836-843. doi:https://doi.org/10.1016/j.procs.2018.04.074

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