The integration of real-time multimodal traffic data fusion is critical for advancing Intelligent Transportation Systems (ITS) in urban environments. This paper presents a novel camera-based traffic data fusion system deployed at the Munich Mobility Research Campus (MORE) as a real-world laboratory. The system captures multimodal trajectories in real-time, which are processed using a state-of-the-art data fusion engine, integrating both low- and high-level fusion functionalities. The evaluation focuses on challenging traffic scenarios, including mixed-traffic environments, groups of vulnerable road users, and adjacent cycling pathways. Performance assessment for aggregated traffic counts is conducted using Root Mean Square Error and paired t-tests, while isolated detection is evaluated using True Positive Rate, Positive Predictive Value, and F1-Score. Results indicate high accuracy in detecting and classifying vehicles and bicycles, with minor underestimation biases observed in pedestrian detection within dense groups. The findings demonstrate the potential of real-time trajectory data fusion for multimodal ITS applications, supporting enhanced traffic planning, management, and control. Future work will extend this research by analyzing system latencies, refining speed accuracy, and exploring real-time anomaly detection. The proposed system contributes to the deployment and evaluation of scalable ITS solutions for connected, automated, and multimodal mobility.      «

The integration of real-time multimodal traffic data fusion is critical for advancing Intelligent Transportation Systems (ITS) in urban environments. This paper presents a novel camera-based traffic data fusion system deployed at the Munich Mobility Research Campus (MORE) as a real-world laboratory. The system captures multimodal trajectories in real-time, which are processed using a state-of-the-art data fusion engine, integrating both low- and high-level fusion functionalities. The evaluation...     »