CIWI-CKT: Chaos-Informed Wave Interference Feature Fusion and Cross-City Knowledge Transfer for Traffic Flow Forecasting
arXiv:2606.15642v1 Announce Type: cross Abstract: Accurate traffic flow prediction remains challenging in cross-city, data-scarce scenarios where limited historical data hinders model generalisation. The chaotic nature of traffic dynamics, complex spatio-temporal dependencies, and heterogeneous urban networks complicate few-shot learning across cities. Existing deep learning approaches either treat traffic as purely deterministic or lack mechanisms to model wave-like interference patterns essential for cross-regime traffic dynamics. To address these limitations, this paper proposes CIWI-CKT, a
The continuous drive for more accurate and generalizable AI models in complex real-world data-scarce environments makes this research timely.
Improved traffic forecasting, especially in data-limited scenarios, has significant implications for urban planning, logistics efficiency, and smart city development.
This research introduces a novel approach to traffic flow forecasting by explicitly modeling chaotic dynamics and wave-like interference, potentially improving model robustness across diverse urban networks.
- · Traffic management agencies
- · Logistics companies
- · Smart city technology providers
- · Urban planners
- · Cities with inefficient traffic infrastructure
More accurate traffic predictions will lead to reduced congestion and improved transportation efficiency.
Optimized traffic flows can decrease fuel consumption and associated carbon emissions, contributing to sustainability goals.
Enhanced urban mobility could support economic growth by reducing transit times for goods and services, and facilitate better public transport planning.
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Read at arXiv cs.AI