Macroscopic Traffic Flow Network Modeling For Wildfire Evacuation: A Game-Theoretic Junction Optimization Approach with Application to Lahaina Fire

TL;DR

This paper models wildfire evacuation traffic flow using game theory and network optimization, revealing critical lane capacities and effective strategies for improving evacuation efficiency.

Contribution

It introduces a novel game-theoretic junction model with flux optimization and applies it to wildfire evacuation, providing quantitative insights and practical lane management strategies.

Findings

Exit lane capacity causes a phase transition in throughput.

Additional lanes improve evacuation linearly until a critical threshold.

Reversing one southbound lane significantly enhances evacuation efficiency.

Abstract

The 2023 Lahaina wildfire killed 102 people on a peninsula served by a single two-lane highway, making exit lane capacity the binding constraint on evacuation time. We model the evacuation as a system of hyperbolic scalar conservation laws on a directed graph with game-theoretic junction conditions that maximize total network flux, an evacuation-calibrated piecewise linear-quadratic flux function, and a loss-driven optimization framework that tunes traffic distribution toward priority corridors. Analytical results on a toy network and numerical simulations of the Lahaina road network reveal a phase transition in exit lane capacity. Additional lanes improve throughput linearly until a computable critical threshold, beyond which no route optimization yields further benefit. For Lahaina, reversing one southbound lane captures nearly all achievable improvement, and a fourth lane can be reserved for emergency vehicles with negligible impact on civilian clearance time. These results provide a rigorous mathematical basis for contraflow recommendations in wildland-urban interface evacuations.