journal article
Oct 01, 2025
FLEE 2.0: An Improved Agent-Based Model of Hurricane Evacuations
Abstract
Abstract
For this study, we present and evaluate an improved agent-based modeling framework, the Forecasting Laboratory for Exploring the Evacuation-system, version 2.0 (FLEE 2.0), designed to investigate relationships between hurricane forecast uncertainty and evacuation outcomes. Presented improvements include doubling its spatial resolution, using a quantitative approach to map real-world data onto the model’s virtual world, and increasing the number of possible risk magnitudes for wind, surge, and rain risk. To assess model realism, we compare FLEE 2.0’s simulated evacuations—specifically its evacuation orders, evacuation rates, and traffic—to available observational data collected during Hurricanes Irma, Dorian, and Ian. FLEE 2.0’s evacuation response is encouraging, given that FLEE 2.0 responds reasonably and differently to all three different types of forecast scenarios. FLEE 2.0 well represents the spatial distribution of observed evacuation rates, and relative to a lower spatial resolution version of the model, FLEE 2.0 better captures sharp gradients in evacuation behaviors across the coastlines and metropolitan areas. Quantitatively evaluating FLEE 2.0’s evacuation rates during Irma establishes model errors, uncertainties, and opportunities for improvement. In summary, this paper increases our confidence in FLEE 2.0, develops a framework for evaluating and improving these types of models, and sets the stage for additional analyses to quantify the impacts of forecast track, intensity, and other positional errors on evacuation.
Significance Statement
This paper describes and evaluates an updated version of a modeling system [the Forecasting Laboratory for Exploring the Evacuation-system, version 2.0 (FLEE 2.0)] designed to explore relationships between hurricane forecasts and evacuation impacts. FLEE 2.0’s simulated evacuations compare favorably with different types of observational evacuation data collected during Hurricanes Irma, Dorian, and Ian. A statistical comparison with Irma’s observed evacuation rates highlights uncertainties and opportunities for improvement in FLEE 2.0. In summary, this paper increases our confidence in FLEE 2.0, develops a framework for evaluating these types of models, and provides a foundation for additional work using FLEE 2.0 as a research tool.
For this study, we present and evaluate an improved agent-based modeling framework, the Forecasting Laboratory for Exploring the Evacuation-system, version 2.0 (FLEE 2.0), designed to investigate relationships between hurricane forecast uncertainty and evacuation outcomes. Presented improvements include doubling its spatial resolution, using a quantitative approach to map real-world data onto the model’s virtual world, and increasing the number of possible risk magnitudes for wind, surge, and rain risk. To assess model realism, we compare FLEE 2.0’s simulated evacuations—specifically its evacuation orders, evacuation rates, and traffic—to available observational data collected during Hurricanes Irma, Dorian, and Ian. FLEE 2.0’s evacuation response is encouraging, given that FLEE 2.0 responds reasonably and differently to all three different types of forecast scenarios. FLEE 2.0 well represents the spatial distribution of observed evacuation rates, and relative to a lower spatial resolution version of the model, FLEE 2.0 better captures sharp gradients in evacuation behaviors across the coastlines and metropolitan areas. Quantitatively evaluating FLEE 2.0’s evacuation rates during Irma establishes model errors, uncertainties, and opportunities for improvement. In summary, this paper increases our confidence in FLEE 2.0, develops a framework for evaluating and improving these types of models, and sets the stage for additional analyses to quantify the impacts of forecast track, intensity, and other positional errors on evacuation.
Significance Statement
This paper describes and evaluates an updated version of a modeling system [the Forecasting Laboratory for Exploring the Evacuation-system, version 2.0 (FLEE 2.0)] designed to explore relationships between hurricane forecasts and evacuation impacts. FLEE 2.0’s simulated evacuations compare favorably with different types of observational evacuation data collected during Hurricanes Irma, Dorian, and Ian. A statistical comparison with Irma’s observed evacuation rates highlights uncertainties and opportunities for improvement in FLEE 2.0. In summary, this paper increases our confidence in FLEE 2.0, develops a framework for evaluating these types of models, and provides a foundation for additional work using FLEE 2.0 as a research tool.
Topics
No keywords indexed for this article. Browse by subject →
References
94
[1]
Alam, M. S., M. W. Horner, E. Erman Ozguven, B. Ventimiglia, and D. Smith, 2024:
Understanding people’s safety perceptions during a recent evacuation: The case of Hurricane Ian (2022). Findings, https://doi.org/10.32866/001c.91268.
[2]
Anand, H., N. Alemazkoor, and M. Shafiee-Jood, 2024:
HEvOD: A database of hurricane evacuation orders in the United States. Sci. Data, 11, 270, https://doi.org/10.1038/s41597-024-03100-x.
[3]
Avila, L. A., S. R. Stewart, R. Berg, and A. B. Hagen, 2020:
National Hurricane Center tropical cyclone report: Hurricane Dorian (AL052019). NHC Tech. Rep., 74 pp., https://www.nhc.noaa.gov/data/tcr/AL052019_Dorian.pdf.
[4]
Axelrod, R., 2006: Agent-based modeling as a bridge between disciplines. Handbook of Computational Economics, Vol. 2,
Elsevier, 1565–1584, https://doi.org/10.1016/S1574-0021(05)02033-2.
[5]
Bucci, L., and Coauthors, 2023:
National Hurricane Center tropical cyclone report: Hurricane Ian (AL092022). NHC Tech. Rep., 72 pp., https://www.nhc.noaa.gov/data/tcr/AL092022_Ian.pdf.
[6]
Cangialosi, J. P., A. S. Latto, and R. Berg, 2021:
National Hurricane Center tropical cyclone report: Hurricane Irma (AL112017). NHC Tech. Rep., 111 pp., https://www.nhc.noaa.gov/data/tcr/AL112017_Irma.pdf.
[7]
Cangialosi, J. P., B. J. Reinhart, and J. Martinez, 2024:
National Hurricane Center forecast verification report: 2023 Hurricane Season. NHC Tech. Rep., 81 pp., https://www.nhc.noaa.gov/verification/pdfs/Verification_2023.pdf.
[8]
Computational and Information Systems Laboratory, 2024:
Casper: HPE/SGI ICE XA System (NCAR Community Computing).
National Center for Atmospheric Research, accessed 1 June 2024, https://doi.org/10.5065/D6RX99HX.
[9]
Czajkowski, J., 2011:
Is it time to go yet? Understanding household hurricane evacuation decisions from a dynamic perspective. Nat. Hazards Rev., 12, 72–84, https://doi.org/10.1061/(ASCE)NH.1527-6996.0000037.
[10]
Darzi, A., V. Frias-Martinez, S. Ghader, H. Younes, and L. Zhang, 2021:
Constructing evacuation evolution patterns and decisions using mobile device location data: A case study of Hurricane Irma. arXiv, 2102.12600v1, https://arxiv.org/abs/2102.12600.
[11]
Di Noia, J., 2024:
Agent-based models for climate change adaptation in coastal zones. A review. FEEM Working Paper 20, 45 pp., https://doi.org/10.2139/ssrn.4180554.
[12]
Ebrahim, R., Y. Li, and W. Ji, 2024:
Evaluating road utilization during hurricane evacuation: A case study of Hurricane Ian. Proc. Construction Research Congress, Des Moines, IA,
ASCE, 1229–1237, https://doi.org/10.1061/9780784485262.125.
[13]
FDEM, 2018:
Regional emergency management liaison team. https://www.floridadisaster.org/dem/directors-office/regions/.
[14]
FDEM, 2024: Florida Division of Emergency Management. Accessed 1 September 2024, https://floridadisaster.maps.arcgis.com/home/index.html.
[15]
Feng, K., and N. Lin, 2021:
Reconstructing and analyzing the traffic flow during evacuation in Hurricane Irma (2017). Transp. Res., 94D, 102788, https://doi.org/10.1016/j.trd.2021.102788.
[16]
Flanagan, B. E., E. W. Gregory, E. J. Hallisey, J. L. Heitgerd, and B. Lewis, 2011:
A social vulnerability index for disaster management. J. Homeland Secur. Emerg. Manage., 8, https://doi.org/10.2202/1547-7355.1792.
[17]
Ghorbanzadeh, M., S. Burns, L. V. N. Rugminiamma, E. Erman Ozguven, and W. Huang, 2021:
Spatiotemporal analysis of highway traffic patterns in Hurricane Irma evacuation. Transp. Res. Rec., 2675, 321–334, https://doi.org/10.1177/03611981211001870.
[18]
Hammond, R. A., 2015:
Considerations and best practices in agent-based modeling to inform policy.
Committee on the Assessment of Agent-Based Models to Inform Tobacco Product Regulation; Board on Population Health and Public Health Practice; Institute of Medicine, https://www.ncbi.nlm.nih.gov/books/NBK305917/.
[19]
Harris, A., P. Roebber, and R. Morss, 2022:
An agent-based modeling framework for examining the dynamics of the hurricane-forecast-evacuation system. Int. J. Disaster Risk Reduct., 67, 102669, https://doi.org/10.1016/j.ijdrr.2021.102669.
[20]
Harris, A., R. Morss, and P. Roebber, 2023a:
What improves evacuations: Exploring the hurricane-forecast-evacuation system dynamics using an agent-based framework. Nat. Hazards Rev., 24, 04023042, https://doi.org/10.1061/NHREFO.NHENG-1671.
[21]
Harris, A., P. Roebber, and R. Morss, 2023b:
A new verification approach? Using coupled natural–human models to evaluate the impact of forecast errors on evacuations. Bull. Amer. Meteor. Soc., 104, E1166–E1178, https://doi.org/10.1175/BAMS-D-22-0136.1.
[22]
Hatzis, J. J., J. Kim, and K. E. Klockow-McClain, 2024:
An agent-based modeling approach to protective action decision-related travel during tornado warnings. Nat. Hazards Rev., 25, 04023057, https://doi.org/10.1061/NHREFO.NHENG-1783.
[23]
Huang, S.-K., M. K. Lindell, and C. S. Prater, 2015:
Who leaves and who stays? A review and statistical meta-analysis of hurricane evacuation studies. Environ. Behav., 48, 991–1029, https://doi.org/10.1177/0013916515578485.
[24]
Hyun, J.-Y., S.-Y. Huang, Y.-C. E. Yang, V. Tidwell, and J. Macknick, 2019:
Using a coupled agent-based modeling approach to analyze the role of risk perception in water management decisions. Hydrol. Earth Syst. Sci., 23, 2261–2278, https://doi.org/10.5194/hess-23-2261-2019.
[25]
Kowaleski, A. M., R. E. Morss, D. Ahijevych, and K. R. Fossell, 2020:
Using a WRF-ADCIRC ensemble and track clustering to investigate storm surge hazards and inundation scenarios associated with Hurricane Irma. Wea. Forecasting, 35, 1289–1315, https://doi.org/10.1175/WAF-D-19-0169.1.
[26]
Kutela, B., K. J. Msechu, E. Kidando, S. Das, and A. E. Kitali, 2023:
Eliciting the influence of roadway and traffic conditions on hurricane evacuation decisions using regression-content analysis approach. Travel Behav. Soc., 33, 100623, https://doi.org/10.1016/j.tbs.2023.100623.
[27]
Li, X., Y. Qiang, and G. Cervone, 2024:
Using human mobility data to detect evacuation patterns in Hurricane Ian. Ann. GIS, 30, 493–511, https://doi.org/10.1080/19475683.2024.2341703.
[28]
Liang, W., N. S.-N. Lam, X. Qin, and W. Ju, 2015:
A two-level agent-based model for hurricane evacuation in New Orleans. J. Homeland Secur. Emerg. Manage., 12, 407–435, https://doi.org/10.1515/jhsem-2014-0057.
[29]
Liu, L., X. Zhang, S. Jiang, and X. Zhao, 2024:
Hurricane evacuation analysis with large-scale mobile device location data during Hurricane Ian. arXiv, 2407.15249v1, https://doi.org/10.48550/arXiv.2407.15249.
[30]
Long, E. F., M. K. Chen, and R. Rohla, 2020:
Political storms: Emergent partisan skepticism of hurricane risks. Sci. Adv., 6, eabb7906, https://doi.org/10.1126/sciadv.abb7906.
[31]
Martin, Y., S. L. Cutter, and Z. Li, 2020:
Bridging twitter and survey data for evacuation assessment of Hurricane Matthew and Hurricane Irma. Nat. Hazards Rev., 21, 04020003, https://doi.org/10.1061/(ASCE)NH.1527-6996.0000354.
[32]
Mason-Dixon Polling and Strategy, 2017:
Mason-Dixon Florida poll: October 2017, Hurricane Irma.
Mason-Dixon Polling and Strategy, 9 pp., https://media.news4jax.com/document_dev/2017/10/26/MasonDixon%20Hurricane%20poll_1509043928726_10861977_ver1.0.pdf.
[33]
Mls, K., and Coauthors, 2023:
Agent-based models of human response to natural hazards: Systematic review of tsunami evacuation. Nat. Hazards, 115, 1887–1908, https://doi.org/10.1007/s11069-022-05643-x.
[34]
Mongold, E., R. A. Davidson, J. Trivedi, S. DeYoung, T. Wachtendorf, and P. Anyidoho, 2021:
Hurricane Evacuation beliefs and behaviour of inland vs. coastal populations. Environ. Hazards, 20, 363–381, https://doi.org/10.1080/17477891.2020.1829531.
[35]
Morss, R. E., and Coauthors, 2017:
Hazardous weather prediction and communication in the modern information environment. Bull. Amer. Meteor. Soc., 98, 2653–2674, https://doi.org/10.1175/BAMS-D-16-0058.1.
[36]
NHC, 2024: NHC Data Archive. National Hurricane Center, accessed 1 February 2024, https://www.nhc.noaa.gov/archive/.
[37]
Rahman, R., K. C. Roy, and S. Hasan, 2021:
Understanding network wide hurricane evacuation traffic pattern from large-scale traffic detector data. 2021 IEEE Int. Intelligent Transportation Systems Conf. (ITSC), Indianapolis, IN,
Institute of Electrical and Electronics Engineers, 1827–1832, https://doi.org/10.1109/ITSC48978.2021.9564480.
[38]
Rashid, M. M., R. Rahman, and S. Hasan, 2023:
Network wide evacuation traffic prediction in a rapidly intensifying hurricane from traffic detectors and Facebook movement data: A deep learning approach. arXiv, 2311.09498v1, https://doi.org/10.48550/arXiv.2311.09498.
[39]
Siam, M. R. K., H. Wang, M. K. Lindell, C. Chen, E. I. Vlahogianni, and K. Axhausen, 2022:
An interdisciplinary agent-based multimodal wildfire evacuation model: Critical decisions and life safety. Transp. Res., 103D, 103147, https://doi.org/10.1016/j.trd.2021.103147.
[40]
Templeton, A., H. Xie, S. Gwynne, A. Hunt, P. Thompson, and G. Köster, 2024:
Agent-based models of social behaviour and communication in evacuations: A systematic review. Saf. Sci., 176, 106520, https://doi.org/10.1016/j.ssci.2024.106520.
[41]
Washington, V., S. Guikema, J.-L. Mondisa, and A. Misra, 2024:
A data-driven method for identifying the locations of hurricane evacuations from mobile phone location data. Risk Anal., 44, 390–407, https://doi.org/10.1111/risa.14188.
[42]
Watts, J., R. E. Morss, C. M. Barton, and J. L. Demuth, 2019:
Conceptualizing and implementing an agent-based model of information flow and decision making during hurricane threats. Environ. Modell. Software, 122, 104524, https://doi.org/10.1016/j.envsoft.2019.104524.
[43]
Widener, M. J., M. W. Horner, and S. S. Metcalf, 2013:
Simulating the effects of social networks on a population’s hurricane evacuation participation. J. Geogr. Syst., 15, 193–209, https://doi.org/10.1007/s10109-012-0170-3.
[44]
Wolshon, B., Z. Zhang, S. Parr, B. Mitchell, and J. Pardue, 2015:
Agent-based modeling for evacuation traffic analysis in megaregion road networks. Procedia Comput. Sci., 52, 908–913, https://doi.org/10.1016/j.procs.2015.05.164.
[45]
Wong, S., S. Shaheen, and J. Walker, 2018:
Understanding evacuee behavior: A case study of hurricane Irma. Transportation Sustainability Research Center Final Rep., 72 pp., https://escholarship.org/uc/item/9370z127.
[46]
Yin, W., P. Murray-Tuite, S. V. Ukkusuri, and H. Gladwin, 2014:
An agent-based modeling system for travel demand simulation for hurricane evacuation. Transp. Res., 42C, 44–59, https://doi.org/10.1016/j.trc.2014.02.015.
[47]
Zachry, B. C., W. J. Booth, J. R. Rhome, and T. M. Sharon, 2015:
A national view of storm surge risk and inundation. Wea. Climate Soc., 7, 109–117, https://doi.org/10.1175/WCAS-D-14-00049.1.
[48]
Alam, M. S., M. W. Horner, E. Erman Ozguven, B. Ventimiglia, and D. Smith, 2024:
Understanding people’s safety perceptions during a recent evacuation: The case of Hurricane Ian (2022). Findings, https://doi.org/10.32866/001c.91268.
[49]
Anand, H., N. Alemazkoor, and M. Shafiee-Jood, 2024:
HEvOD: A database of hurricane evacuation orders in the United States. Sci. Data, 11, 270, https://doi.org/10.1038/s41597-024-03100-x.
[50]
Avila, L. A., S. R. Stewart, R. Berg, and A. B. Hagen, 2020:
National Hurricane Center tropical cyclone report: Hurricane Dorian (AL052019). NHC Tech. Rep., 74 pp., https://www.nhc.noaa.gov/data/tcr/AL052019_Dorian.pdf.
Showing 50 of 94 references
Metrics
1
Citations
94
References
Details
- Published
- Oct 01, 2025
- Vol/Issue
- 17(4)
- Pages
- 729-744
- License
- View
Authors
Cite This Article
Austin Harris, Rebecca Morss, Chris Davis, et al. (2025). FLEE 2.0: An Improved Agent-Based Model of Hurricane Evacuations. Weather, Climate, and Society, 17(4), 729-744. https://doi.org/10.1175/wcas-d-24-0112.1
Related
You May Also Like
The Effects of Past Hurricane Experiences on Evacuation Intentions through Risk Perception and Efficacy Beliefs: A Mediation Analysis
Julie L. Demuth, Rebecca E. Morss · 2016
175 citations
Economic Valuation of Coccidioidomycosis (Valley Fever) Projections in the United States in Response to Climate Change
Morgan E. Gorris, James E. Neumann · 2021
36 citations
Hurricane Hazards, Evacuations, and Sheltering: Evacuation Decision-Making in the Prevaccine Era of the COVID-19 Pandemic in the PRVI Region
Jennifer Collins, Amy Polen · 2022
20 citations