Multi-Source Urban Traffic Flow Forecasting With Drone and Loop Detector Data

Traffic forecasting is a fundamental task in transportation research, however, the scope of current research has mainly focused on a single data modality of loop detectors. Recently, the advances in Artificial Intelligence and drone technologies have enabled novel solutions to efficient, accurate, and flexible aerial observations of urban traffic. As a promising traffic monitoring approach, drones can create an accurate multi-sensor mobility observatory for large-scale urban networks, when combined with existing infrastructure. Therefore, this paper investigates urban traffic prediction from a novel perspective, where multiple input sources, i.e., loop detectors and drones, are utilized to predict both the segment-level traffic of all road segments and the regional traffic. A simple yet effective graph-based model, Hierarchical Multi-Source Neural Network (HiMSNet), is proposed to integrate multiple data modalities and learn spatio-temporal correlations. Detailed analysis shows that predicting accurate segment-level speed is more challenging than the regional speed, especially under high-demand scenarios with heavier congestion and varying traffic dynamics. Utilizing both drone and loop detector data, the prediction accuracy can be improved compared to single-modality cases, when the sensors have lower coverage and are subject to noise. Our simulation study based on vehicle trajectories in a real urban road network has highlighted the value of integrating drones in traffic forecasting and monitoring.