IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v152y2021ics1366554521001496.html
   My bibliography  Save this article

A behaviorally-integrated individual-level state-transition model that can predict rapid changes in evacuation demand days earlier

Author

Listed:
  • Guan, Xiangyang
  • Chen, Cynthia

Abstract

Timely and accurate forecast of evacuation demand is key for emergency responders to plan and organize effective evacuation efforts during a disaster. The state of the art in evacuation demand forecasting includes behavior-based models and dynamic flow-based models. Both lines of work have limitations: behavioral models are static, meaning that they cannot adjust model predictions by utilizing field observation in real time as the disaster unfolds; and the flow-based models often have relatively short prediction windows ranging from minutes to hours. Consequently, both types of models fall short of being able to predict sudden changes (e.g., a surge or an abrupt drop) of evacuation demand in advance. This paper develops a behaviorally-integrated individual-level state-transition model for online predictions of evacuation demand. On a daily basis, the model takes in observed evacuation data and updates its forecasted evacuation demand for the future. An individual-level survival model formulation is devised for the state-transition model to account for history-dependent transition probabilities and allow individual heterogeneity. A Bayesian updating approach is employed to assimilate observed evacuation data in real time. To enable a longer-term perspective on how evacuation demand may evolve over time so that rapid surges or drops in demand can be predicted days in advance, the model integrates insights from existing behavioral curves (either from past disasters or simply expert opinions). Using a likelihood-based approach, the state-transition model integrates the future trends of evacuation demand informed by the behavioral curve when updating its forecasts. The theoretical proof of the developed state-transition model shows that the likelihood function guarantees a unique global solution. The model is tested in six scenarios using mobile app-based data for Hurricane Harvey that hit the US in 2017. The results demonstrate its robustness: in all six scenarios, the model is able to predict accurately the occurrence of the rapid surges or drops in evacuation demand at least two days ahead. The current study contributes to the field of evacuation modeling by integrating the two lines of work (behavior-based and flow-based models) using mobile app-based data for Hurricane Harvey.

Suggested Citation

  • Guan, Xiangyang & Chen, Cynthia, 2021. "A behaviorally-integrated individual-level state-transition model that can predict rapid changes in evacuation demand days earlier," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    • Handle: RePEc:eee:transe:v:152:y:2021:i:c:s1366554521001496
    DOI: 10.1016/j.tre.2021.102381
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554521001496
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2021.102381?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mai, Tien & Fosgerau, Mogens & Frejinger, Emma, 2015. "A nested recursive logit model for route choice analysis," Transportation Research Part B: Methodological, Elsevier, vol. 75(C), pages 100-112.
    2. Aguirregabiria, Victor & Mira, Pedro, 2010. "Dynamic discrete choice structural models: A survey," Journal of Econometrics, Elsevier, vol. 156(1), pages 38-67, May.
    3. Stepanov, Alexander & Smith, James MacGregor, 2009. "Multi-objective evacuation routing in transportation networks," European Journal of Operational Research, Elsevier, vol. 198(2), pages 435-446, October.
    4. Victor Aguirregabiria & Arvind Magesan, 2013. "Euler Equations for the Estimation of Dynamic Discrete Choice Structural Models," Advances in Econometrics, in: Structural Econometric Models, volume 31, pages 3-44, Emerald Group Publishing Limited.
    5. Fosgerau, Mogens & Frejinger, Emma & Karlstrom, Anders, 2013. "A link based network route choice model with unrestricted choice set," Transportation Research Part B: Methodological, Elsevier, vol. 56(C), pages 70-80.
    6. Urena Serulle, Nayel & Cirillo, Cinzia, 2017. "The optimal time to evacuate: A behavioral dynamic model on Louisiana resident data," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 447-463.
    7. Panda, Manoj & Ngoduy, Dong & Vu, Hai L., 2019. "Multiple model stochastic filtering for traffic density estimation on urban arterials," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 280-306.
    8. Adam Pel & Michiel Bliemer & Serge Hoogendoorn, 2012. "A review on travel behaviour modelling in dynamic traffic simulation models for evacuations," Transportation, Springer, vol. 39(1), pages 97-123, January.
    9. Jian Li & Kaan Ozbay & Bekir Bartin, 2015. "Effects of Hurricanes Irene and Sandy in New Jersey: traffic patterns and highway disruptions during evacuations," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 78(3), pages 2081-2107, September.
    10. Aguirregabiria, Victor & Magesan, Arvind, 2013. "Euler Equations for the Estimation of Dynamic Discrete Choice Structural," MPRA Paper 46056, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sheu, Jiuh-Biing, 2024. "Mass evacuation planning for disasters management: A household evacuation route choice behavior analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rambha, Tarun & Nozick, Linda K. & Davidson, Rachel, 2021. "Modeling hurricane evacuation behavior using a dynamic discrete choice framework," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 75-100.
    2. Song, Yuchen & Li, Dawei & Liu, Dongjie & Cao, Qi & Chen, Junlan & Ren, Gang & Tang, Xiaoyong, 2022. "Modeling activity-travel behavior under a dynamic discrete choice framework with unobserved heterogeneity," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).
    3. Jiaming Mao & Jingzhi Xu, 2020. "Ensemble Learning with Statistical and Structural Models," Papers 2006.05308, arXiv.org.
    4. Christopher Ferrall, 2023. "Object Oriented (Dynamic) Programming: Closing the “Structural” Estimation Coding Gap," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 761-816, October.
    5. Robert L. Bray, 2019. "Markov Decision Processes with Exogenous Variables," Management Science, INFORMS, vol. 65(10), pages 4598-4606, October.
    6. Mai, Tien & Bastin, Fabian & Frejinger, Emma, 2017. "On the similarities between random regret minimization and mother logit: The case of recursive route choice models," Journal of choice modelling, Elsevier, vol. 23(C), pages 21-33.
    7. Mogens Fosgerau & Mads Paulsen & Thomas Kj{ae}r Rasmussen, 2021. "A perturbed utility route choice model," Papers 2103.13784, arXiv.org, revised Sep 2021.
    8. Tien Mai & The Viet Bui & Quoc Phong Nguyen & Tho V. Le, 2022. "Estimation of Recursive Route Choice Models with Incomplete Trip Observations," Papers 2204.12992, arXiv.org.
    9. Meyer de Freitas, Lucas & Becker, Henrik & Zimmermann, Maëlle & Axhausen, Kay W., 2019. "Modelling intermodal travel in Switzerland: A recursive logit approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 119(C), pages 200-213.
    10. Urena Serulle, Nayel & Cirillo, Cinzia, 2017. "The optimal time to evacuate: A behavioral dynamic model on Louisiana resident data," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 447-463.
    11. Yao, Rui & Bekhor, Shlomo, 2022. "A variational autoencoder approach for choice set generation and implicit perception of alternatives in choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 158(C), pages 273-294.
    12. David Canning & Declan French & Michael Moore, 2016. "The Economics of Fertility Timing: An Euler Equation Approach," CHaRMS Working Papers 16-03, Centre for HeAlth Research at the Management School (CHaRMS).
    13. Victor Aguirregabiria & Allan Collard-Wexler & Stephen P. Ryan, 2021. "Dynamic Games in Empirical Industrial Organization," NBER Working Papers 29291, National Bureau of Economic Research, Inc.
    14. Harris, Jeremiah & Siebert, Ralph, 2017. "Firm-specific time preferences and postmerger firm performance," International Journal of Industrial Organization, Elsevier, vol. 53(C), pages 32-62.
    15. Cortés, Cristián E. & Donoso, Pedro & Gutiérrez, Leonel & Herl, Daniel & Muñoz, Diego, 2023. "A recursive stochastic transit equilibrium model estimated using passive data from Santiago, Chile," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    16. Evanthia Kazagli & Michel Bierlaire & Matthieu de Lapparent, 2020. "Operational route choice methodologies for practical applications," Transportation, Springer, vol. 47(1), pages 43-74, February.
    17. Kazagli, Evanthia & Bierlaire, Michel & Flötteröd, Gunnar, 2016. "Revisiting the route choice problem: A modeling framework based on mental representations," Journal of choice modelling, Elsevier, vol. 19(C), pages 1-23.
    18. Mai, Tien & Yu, Xinlian & Gao, Song & Frejinger, Emma, 2021. "Routing policy choice prediction in a stochastic network: Recursive model and solution algorithm," Transportation Research Part B: Methodological, Elsevier, vol. 151(C), pages 42-58.
    19. Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza-Rodrigues, 2018. "Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models," NBER Working Papers 25134, National Bureau of Economic Research, Inc.
    20. Hung Tran & Tien Mai, 2023. "Network-based Representations and Dynamic Discrete Choice Models for Multiple Discrete Choice Analysis," Papers 2306.04606, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transe:v:152:y:2021:i:c:s1366554521001496. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.