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Background

Evacuation is a critical tool for reducing casualties during natural disasters, yet large-scale events like Hurricane Irma—which triggered the largest mass evacuation in U.S. history—have exposed significant gaps in traffic management and planning. At the same time, the widespread adoption of smartphones and connected navigation devices has created new opportunities to improve evacuation modeling through mobile location data. This research leverages these emerging data sources to better understand and model human mobility during hurricane evacuations, with the goal of improving future evacuation planning and response.

Research Objectives

• Review the social factors influencing evacuation and determine how well they are captured in prominent composite indices measuring Disaster Resilience, Social Vulnerability, and Social Capital currently used in disaster planning efforts.
• Analyze the influence of social demographic variables on travel behavior during a hurricane evacuation using mobile location data.
• Propose a protocol demonstrating the implications of different data manipulation and belief updating methods on computational results when using Evidence Theory.
• Demonstrate the application of Evidence Theory to incorporate highly uncertain sensor data in pavement condition assessment.

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This research explored how social factors influence evacuation behavior, beginning with a literature review that highlighted the need for more human-centered approaches in evacuation modeling.Ìý

Using mobile location data and traffic counts from Hurricane Michael (2018), the study found that different social groups exhibit distinct travel behaviors during evacuations, though results varied across sites, underscoring the uncertainty inherent in social data.Ìý

To address this uncertainty, the research developed a structured protocol for applying Evidence Theory—a method well-suited for combining diverse, uncertain datasets—and demonstrated its effectiveness relative to traditional probability-based approaches such as Markov Decision Processes. Overall, the work advances transportation decision-making by providing tools and frameworks for incorporating uncertain social data into planning and analysis.

This project resulted in the following publications:

• Seites-Rundlett, W.; Corotis, R.; Torres-Machi, C. (2022) Development of a Protocol for Engineering Applications of Evidence Theory. Submitted to: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 8(3): 04022036, DOI: 10.1061/AJRUA6.0001241.
• Seites-Rundlett, W.; Bashar, M.;Torres-Machi, C.; Corotis, R. (2022) Combined Evidence Model to Enhance Pavement Condition Prediction from Highly Uncertain Sensor Data. Reliability Engineering & System Safety, 217, 108031, DOI: 10.1016/j.ress.2021.108031
• Seites-Rundlett, W.; Garcia-Bande E.; Alvarez-Mingo, A.; Torres-Machi, C.; Corotis, R. (2020) Social Indicators to Inform Community Evacuation Modeling and Planning. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 6(3):03120001, DOI:10.1061/AJRUA6.0001069.

Funding

US Department of Education Graduate Assistance in Areas of National Need (GAANN) Fellowship: 2018-2023.