Transport Network Topology as a Determinant of Forced Displacement Dynamics Case Study of Rail-Based Evacuation from Ukraine

Conflict-induced displacement is strongly shaped by transportation networks, yet most displacement models represent mobility as continuous or distance-based, neglecting network constraints such as corridor structure, connectivity, and capacity. This limits the ability of existing approaches to capture evacuation dynamics in situations where movement is mediated by infrastructure-specific determinants.
This paper presents an agent-based model of infrastructure-constrained forced displacement that explicitly represents a real-world railway network. Implemented in Flee 3, the model simulates displacement during the early phase of the 2022 Russian invasion of Ukraine by dynamically generating agents in response to conflict-onset data from ACLED and routing movement exclusively along operational rail corridors toward railway-accessible border checkpoints. Simulations cover the period from February to June 2022, producing daily arrival counts at eight major border crossings for validation against a curated empirical dataset.
The model achieves strong overall fit, with a median across-camp average relative difference (ARD) of 0.31 and sustained post-surge errors below 20% for most corridors. Beyond aggregate fit, the results reveal a clear two-phase structural transition. During the initial surge, flows are distributed across multiple corridors, with no single route consistently dominant. In the stabilized phase, flows consolidate sharply, indicating persistent single-corridor dominance. Ultimately, the findings suggest displacement trajectories are not governed by distance or conflict intensity alone, but by network topology, asymmetric capacity constraints, and endogenous corridor competition.