Author Type

Graduate Student

Date of Award

Summer 7-14-2026

Document Type

Dissertation

Publication Status

Version of Record

Submission Date

July 2026

Department

Computer and Electrical Engineering and Computer Science

College Granting Degree

College of Engineering and Computer Science

Department Granting Degree

Electrical Engineering and Computer Science

Degree Name

Doctor of Philosophy (PhD)

Thesis/Dissertation Advisor [Chair]

KwangSoo Yang

Abstract

Emergency Management Information Systems (EMIS) are defined as a set of tools that assist decision-makers in risk assessment and disaster response for significant multi-hazard threats and disasters. Over the past several decades, EMIS have become increasingly important for understanding, managing, and governing transportation systems during large-scale emergency events. One of the primary objectives of EMIS is to efficiently utilize spatial and network datasets to support evacuation planning, identify critical transportation patterns during emergencies, and allocate resources effectively. However, the increasing complexity and scale of modern transportation systems present significant challenges in developing reliable evacuation planning solutions.

One of the most critical challenges in disaster management is the efficient evacuation of large populations using transportation infrastructure. Traditional evacuation routing approaches primarily focus on minimizing evacuation time while accounting for traffic congestion and road capacity constraints. However, the rapid adoption of electric vehicles (EVs) introduces additional operational constraints for evacuation planning. Electric vehicles must periodically recharge at charging stations due to limited battery capacity, and evacuation routes must ensure that vehicles can reach charging infrastructure without depleting their energy. Existing evacuation routing approaches do not incorporate charging constraints, which may lead to infeasible evacuation routes, vehicle stalling, charging station congestion, and severe delays during emergency evacuations.

To address these challenges, this dissertation investigates the Resource Constrained Evacuation Route Planning (RC-ERP) problem. Given a transportation network, a set of evacuees, and a set of charging stations, the goal of RC-ERP is to compute evacuation routes that minimize total evacuation time while ensuring that vehicles satisfy both transportation capacity constraints and electric vehicle charging constraints.

The RC-ERP problem is computationally challenging due to the large size of real-world transportation networks, the large number of evacuees, and the need to simultaneously consider traffic congestion and vehicle energy feasibility. Previous work has focused on minimizing evacuation time using spatial or spatio-temporal network models. However, these approaches do not guarantee charging feasibility and may result in evacuation plans that cannot be executed in practice.

To address these limitations, this dissertation proposes novel algorithmic approaches for RC-ERP. First, we introduce a Charge-Encoded Routing framework, which models vehicle energy constraints using a charge-encoded state-space representation. Based on this framework, we develop the Node-Encoded Shortest Path (NESP) algorithm for identifying feasible charging-aware evacuation routes. Second, we propose a Time-Expanded Charge-Encoded Routing Algorithm (TE-CERA), which uses time-expanded capacity-aware scheduling to incorporate road capacity constraints and generate feasible evacuation schedules for large populations.

Experimental evaluations using real-world transportation datasets demonstrate that the proposed approaches produce evacuation routes that satisfy both charging feasibility and network capacity constraints while maintaining competitive evacuation times. These results highlight the importance of integrating energy constraints into evacuation planning frameworks and contribute new algorithmic tools for improving emergency evacuation strategies in transportation systems increasingly dominated by electric vehicles.

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