Graduation Year

2026

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Civil and Environmental Engineering

Major Professor

Michael Maness, Ph.D.

Committee Member

Fred Mannering, Ph.D.

Committee Member

Jennifer Collins, Ph.D.

Committee Member

Maya Trotz, Ph.D.

Committee Member

Tapas K. Das, Ph.D.

Keywords

Range, Evacuation Decision, Hurricane, Public Charging, Stated Choice

Abstract

The increasing share of battery electric vehicles (BEVs) in the transportation system is introducing new challenges to the emergency evacuation planning and management. During Hurricane Milton (2024), incidents involving BEVs, including battery fires, attributed to saltwater exposure risk, and advisories recommending relocation to higher ground, highlighted operational vulnerabilities unique to this technology. These emerging risks are likely to be experienced heterogeneously, as access to charging infrastructure, available resources, and evacuation flexibility varies across the population. This raises critical questions about how BEV-specific constraints interact with existing inequalities during evacuation. Motivated by this context, the dissertation investigated inequity in BEV evacuation system, examining how social and financial disadvantage translates into distinct infrastructural constraints through a mixed-methods framework. The first method employed a qualitative investigation involving simulation-based evacuation scenarios and semi-structured interviews with 25 Florida-based motorists across diverse income groups. Reflexive thematic analysis revealed existing disparities in residential charging access, with low-income households heavily reliant on public charging infrastructure. Further, these differences structurally shape evacuation preparedness, departure timing, charger selection, and evacuation route preferences. Building on these insights, a combined revealed and stated preference survey is administered to model route and charger choice behavior under evacuation conditions. Random parameters logit models, and latent class specifications were estimated to test hypotheses involving route familiarity, charger availability, charging and waiting time, amenities at station, vehicle driving range, and range anxiety. Results revealed statistically significant heterogeneity in charging and route preferences at the 90% confidence level, with higher sensitivity towards time and infrastructure constraints and a stronger tendency to select shorter and faster routes. Across all groups, the availability of accessible charging stations exerted a significant positive influence on route selection.

Finally, a case study of Pinellas County, Florida, is conducted to examine system-level evacuation performance under a BEV-dominant fleet. A unified framework is developed that integrates population heterogeneity with vehicle energy constraints and charging infrastructure conditions to evaluate network-level evacuation outcomes. Using an equity-based approach, evacuation demand is first generated by explicitly accounting for social and financial disadvantage through socio-economic and demographic indicators. The resulting demand is then assigned to the transportation network using stochastic tree-based routing models to examine how response timing, battery-related constraints, and charging accessibility influence evacuation performance.

Overall, the findings indicate that inequities in BEV evacuation emerge across multiple stages of the evacuation process, from differences in charging access and preparedness, to heterogeneous behavioral responses, ultimately resulting in unequal system-level outcomes. It demonstrates that evacuation challenges in an electrified transportation system are not reducible to network conditions alone, but are shaped by the interaction of social and financial disadvantage, vehicle energy constraints, and charging infrastructure availability. These factors eventually manifest as disparities in charging access, pre-evacuation energy readiness, and the ability to maintain a feasible energy path during evacuation. By integrating qualitative insights, behavioral modeling, and network-level analysis, this dissertation provides a comprehensive framework for understanding evacuation equity under transportation electrification, with practical relevance for emergency management agencies, transportation planners, local governments, and charging infrastructure providers, and highlights the need for planning approaches that explicitly account for both population heterogeneity and infrastructure accessibility.

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