Physics-informed graph modeling for traffic shockwave analysis through optimized change-point detection

Shockwaves represent one of the most consequential and pervasive disturbances in highway operations, yet existing shockwave identification methods struggle to capture the underlying causal mechanisms. This paper introduces a novel framework that fundamentally reconceptualizes driver dynamics through graph theory, redefining shockwave formation and dissipation as chains of causally linked braking and acceleration behaviors. The methodology employs an optimized Change Point Detection (CPD) algorithm to segment individual vehicle trajectories and identify significant acceleration and deceleration events at the microscopic level. A graph-based clustering technique then connects these events across multiple vehicles, explicitly capturing the inherent causation of shockwave propagation by tracing how one vehicle’s deceleration triggers a cascade of braking responses in following vehicles, propagating upstream against the direction of traffic flow. This approach transforms shockwave identification from a statistical classification problem into a network propagation perspective, ensuring the detection of physically reasonable propagation waves that reflect actual driver-to-driver interactions. Comprehensive performance evaluation against baseline methods demonstrates the superiority of the framework in characterizing key shockwave properties, including propagation speed, spatial extent, duration, and oscillation intensity. Through sensitivity analysis and ablation study, this approach has been proved a robust foundation for traffic flow analysis, effectively bridging microscopic driver behaviors with macroscopic flow phenomena through the lens of graph modeling methodology.