Modeling the dynamics of hurricane evacuation decisions from twitter data: An input output hidden markov modeling approach

Evacuations play a critical role in saving human lives during hurricanes. But individual evacuation decision-making is a complex dynamic process, often studied using post-hurricane survey data. Alternatively, ubiquitous use of social media generates a massive amount of data that can be used to predict evacuation behavior in real time. In this paper, the authors present a method to infer individual evacuation behaviors (e.g., evacuation decision, timing, destination) from social media data. The authors develop an input output hidden Markov model (IO-HMM) to infer evacuation decisions from user tweets. To extract the underlying evacuation context from tweets, the authors first estimate a word2vec model from a corpus of more than 100 million tweets collected over four major hurricanes. Using input variables such as evacuation context, time to landfall, type of evacuation order, and the distance from home, the proposed model infers what activities are made by individuals, when they decide to evacuate, and where they evacuate to. To validate their results, the authors have created a labeled dataset from 38,256 tweets posted between September 2, 2017 and September 19, 2017 by 2,571 users from Florida during hurricane Irma. The authors' findings show that the proposed IO-HMM method can be useful for inferring evacuation behavior in real time from social media data. Since traditional surveys are infrequent, costly, and often performed at a post-hurricane period, the proposed approach can be very useful for predicting evacuation demand as a hurricane unfolds in real time.

• Record URL:
  • https://doi.org/10.1016/j.trc.2021.102976
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0968090X21000139
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2021 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Roy, Kamol Chandra
  • Hasan, Samiul
• Publication Date: 2021-2

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; Maps; References; Tables;
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 123
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Data mining; Decision making; Evacuation; Hurricanes; Input output models; Markov processes; Social media
• Identifier Terms: Hurricane Irma; Twitter
• Geographic Terms: Florida
• Subject Areas: Data and Information Technology; Operations and Traffic Management; Planning and Forecasting; Security and Emergencies; Transportation (General);

Filing Info

• Accession Number: 01767771
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 23 2021 11:13AM