Abnormal cascading dynamics in transportation networks with a dynamic origin–destination demand matrix

Abstract

In real-world road networks, origin–destination (OD) demand dynamics, influenced by origin capacity and destination attractiveness, determine network load. For instance, weekday mornings see high travel demand from residential to commercial areas, shaping the OD matrix critical for transportation efficiency. Our study introduces a dynamic OD demand matrix and a novel method to gauge initial network edge load. This informs a new cascading failure model with adjustable parameters: the generation parameter $\alpha$, representing the intensity of departure willingness at origin nodes; the attraction parameter $\beta$, capturing the relative attractiveness of destination nodes; and the capability parameter $\gamma$, reflecting the capacity of each edge to accommodate excess load. Simulation across three transportation networks reveals two phases of cascading failures: initial mild propagation followed by rapid collapse, linked to connectivity shifts. Introducing a Gaussian-corrected distance factor mitigates rapid collapse risks. Analysis of WS and BA network models underscores the importance of a balanced load-to-initial load ratio for network stability. Effective management of subnet loads is crucial to achieve this balance, ensuring robust network performance and resilience.