CognitiveTwin-Edge: A Self-Adaptive Digital Twin Framework with Federated Edge Intelligence for Predictive Urban Mobility Orchestration

Abstract

The exponential growth of connected vehicles, IoT sensors, and smart city infrastructure generates unprecedented volumes of heterogeneous mobility data, creating both opportunities and challenges for real-time urban traffic optimization. Current traffic management systems predominantly operate as reactive systems, responding to congestion after it materializes, while simultaneously struggling with critical limitations in privacy preservation, energy efficiency, and scalability. This paper introduces CognitiveTwin-Edge, a novel framework that synergistically integrates cognitive digital twins with hierarchical federated edge intelligence to enable proactive, privacy-preserving urban mobility orchestration. Unlike existing digital twin implementations that function as passive mirror systems merely replicating current state without anticipatory capabilities, our framework endows digital twins with cognitive properties including autonomous self-configuration, adaptive self-healing, and predictive anticipatory behavior through a novel Temporal-Spatial Attention Mechanism (TSAM). The framework employs a three-tier hierarchical federated learning architecture where geographically distributed edge nodes collaboratively train mobility prediction models without sharing raw sensor data, thereby preserving citizen privacy while enabling city-wide traffic optimization. We introduce the innovative concept of Mobility Intent Graphs (MIGs) that capture latent movement intentions and aggregate demand patterns across urban zones, enabling proactive rather than reactive traffic management through predictive orchestration. The framework incorporates an adaptive synchronization protocol that dynamically adjusts model update frequency based on detected mobility dynamics, reducing communication overhead while maintaining prediction accuracy. Comprehensive experimental evaluation on synthetic datasets modeling a metropolitan area with 500,000 daily vehicle trips across 200 traffic zones demonstrates that CognitiveTwin-Edge reduces average travel time by 23.7% during peak hours, decreases total system energy consumption by 18.4%, and maintains high prediction accuracy (RMSE of 18.3 vehicles/hour at 15-minute horizon) while reducing communication overhead by 67% compared to centralized approaches. The framework provides differential privacy guarantees (ε=1.0) and demonstrates robust scalability from 20 to 200 edge nodes. This work represents a paradigm shift from reactive to anticipatory, centralized to federated, and privacy-compromising to privacy-preserving urban mobility systems.