Year

2025

Season

Summer

Paper Type

Master's Thesis

College

College of Computing, Engineering & Construction

Degree Name

Master of Science in Civil Engineering (MSCE)

Department

Engineering

NACO controlled Corporate Body

University of North Florida. School of Engineering

Committee Chairperson

Dr Thobias Sando

Second Advisor

Dr Emmanuel Kidando

Third Advisor

Dr Ryan Shamet

Department Chair

Dr Thobias Sando

College Dean

Dr William Klostermeyer

Abstract

The widespread adoption of electric vehicles (EVs) is essential for advancing sustainable transportation systems; however, disparities in the spatial distribution of electric vehicle charging stations (EVCS) present significant challenges for equitable access. This thesis investigates both the inequities in EVCS accessibility and offers a data driven approach for optimizing the siting of future charging infrastructure.

The first part of the study investigates the accessibility of EVCS in rural and urban areas, focusing on how socio-demographic factors influence accessibility metrics. Using geographic information systems (GIS), network analysis, and binary logistic regression, spatial data on EVCS locations and socio-demographic attributes such as income levels, educational attainment, and minority representation across diverse regions were analyzed. Accessibility metrics such as average distance and travel time to the nearest EVCS, were calculated to assess geographical and social inequities. The closest facility analysis revealed substantial disparities in EVCS accessibility between urban and rural areas, with urban areas having an average travel distance of 3 miles to the nearest EVCS compared to 9 miles in rural areas. Statistical analyses underscored the influence of income and educational attainment on the availability and accessibility of EVCS. The results highlight critical gaps in infrastructure planning and underscore the need for policies aimed at equitable distribution of EVCS to ensure inclusive participation in the electric vehicles (EV) transition. These findings provide valuable insights into the socio spatial dynamics of EVCS accessibility, offering actionable recommendations for policymakers and urban planners to address existing inequities and promote sustainable and inclusive transportation systems.

The second part applies a spatial optimization framework using location-allocation modeling and zero-inflated count regression to determine optimal EVCS sitting in underserved census tracts. Factors such as proximity to amenities, transportation corridors, and electrical grid capacity were used to refine model outputs and predict charger counts. Collectively, the findings underscore the critical need for equitable infrastructure planning and offer methodological guidance for policymakers and transportation agencies seeking to expand EV charging networks in a just and efficient manner. This study provides actionable insights by highlighting priority locations for EV charging stations, informing targeted investments in infrastructure development, and supporting strategic planning decisions aimed at bridging accessibility gaps. This approach can assist decision-makers in ensuring that resources are allocated effectively, maximizing benefits for both urban and rural populations.

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