Machine Learning Strategies for Fusing Drone-Based Visual Data and Vehicle Telemetry in Congestion Mitigation

Abstract

The integration of drone-based visual data and vehicle telemetry offers a promising approach to addressing urban traffic congestion. Machine learning (ML) provides an effective framework for processing and fusing these diverse data sources to generate actionable insights for congestion mitigation. This paper explores strategies for leveraging ML techniques to combine visual data from drones and telemetry data from vehicles, focusing on applications in traffic flow optimization, incident detection, and real-time rerouting. Key challenges, such as data heterogeneity, computational efficiency, and the need for robust models under dynamic conditions, are examined. We review existing ML methods, including deep learning for visual data analysis and ensemble techniques for telemetry fusion, and propose novel approaches that leverage spatiotemporal modeling and federated learning. Experimental results on simulated and real-world datasets demonstrate the potential of these strategies to improve traffic prediction accuracy and reduce congestion through proactive interventions. The paper concludes with recommendations for implementing scalable ML systems that integrate drone and vehicle data streams, addressing practical considerations such as edge computing, privacy, and adaptability to varying urban contexts.