PhaseShift: Topology-Aware Data Harmonization and Model Consolidation Across Signalized Intersections
Learned traffic-behavior models are commonly trained separately for each intersection, creating model portfolios that cannot share evidence across sites. We present PhaseShift, a topology-aware framework that harmonizes heterogeneous roadside trajectories into a shared actor-centric representation and trains one reusable backbone. Ego-relative coordinates, trajectory-induced movement paths, normalized signal context, and variable-cardinality interaction tokens remove site conventions while preserving behaviorally relevant topology. The backbone supports pooled operation, zero-shot at a held-out intersection, and low-data adaptation. We evaluate five intersections in two Florida regions on...
| Authors | Yash Ranjan, Artur Kumik, Rahul Sengupta et al. |
|---|---|
| Primary category | cs.AI |
| Published | 26 August 2026 |
| Last updated at source | 26 August 2026 |
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