As organizations move beyond traditional monitoring systems, there is a growing need to anticipate events before they occur. Operational environments such as aviation, maritime transportation, and autonomous systems generate enormous volumes of real-time data, yet many organizations still rely on tools that simply display what is happening in the moment. The shift toward predictive operations, where systems can forecast outcomes, identify emerging risks, and support proactive decision-making, is transforming how complex transportation and mobility networks are managed. Real-time modeling and AI-driven inference are now critical components for organizations seeking to improve efficiency, safety, and responsiveness in dynamic environments.
Mosaic ATM’s RTS Agent (Real Time Simulation Agent) addresses this need by combining machine learning, probabilistic modeling, and knowledge graph analytics to extract predictive insight from streaming track data. Designed for high-volume operational environments, RTS-Agent continuously processes both historical and live data feeds to generate forward-looking intelligence. Rather than simply visualizing movement, the system analyzes patterns in motion, behavior, and historical context to forecast future trajectories, detect anomalies, and infer likely destinations or operational intent.
At its core, RTS-Agent leverages advanced deep learning sequence models to understand how moving objects behave over time. Long Short-Term Memory (LSTM) predictors analyze sequences of positional and velocity data to identify patterns and generate trajectory forecasts. These models are complemented by particle-filter-based kinematic forecasting techniques, which model the uncertainty inherent in real-world motion and produce probabilistic predictions of future positions.
Beyond motion prediction, RTS-Agent incorporates knowledge graph identity analysis to enhance contextual understanding. By linking track data with historical records, behavioral patterns, and entity relationships, the system can identify recurring actors, correlate activity across datasets, and detect unusual patterns that may indicate operational anomalies or emerging risks.
Mosaic ATM‘s RTS system is designed for continuous, real-time operation. RTS-Agent integrates with streaming data pipelines to perform live inference as new surveillance data arrives. This allows the platform to update predictions dynamically, ensuring that forecasts and alerts reflect the most current operational picture. To support large-scale deployments, RTS-Agent also includes scalable training pipelines capable of processing massive historical datasets, allowing models to be continuously refined as new data becomes available.
Key capabilities of RTS-Agent include:
- Deep learning sequence models using LSTM-based predictors
- Particle-filter kinematic forecasting for probabilistic trajectory prediction
- Knowledge graph identity analysis to detect relationships and behavioral patterns
- Real-time streaming inference for continuous operational awareness
- Scalable training pipelines for processing large historical datasets
Trained on hundreds of gigabytes of aviation and maritime data, RTS-Agent demonstrates the ability to transform raw surveillance streams into meaningful predictive intelligence. By learning from historical movement patterns and continuously analyzing live data, the system can identify emerging behaviors, anticipate operational changes, and highlight potential anomalies before they escalate into operational challenges.
This predictive capability supports a wide range of applications across complex mobility environments. In civil aviation traffic management, RTS-Agent can help forecast aircraft trajectories and identify deviations that may impact traffic flow. In maritime logistics, the system can predict vessel movements and optimize port operations. Autonomous fleets, including drones, surface vehicles, and other robotic platforms, can use these predictive insights to coordinate movement and avoid conflicts.
Additional applications include security monitoring and anomaly detection, where identifying unusual movement patterns is critical, as well as smart mobility and infrastructure monitoring across urban transportation systems.
By combining artificial intelligence with robust real-time data pipelines, RTS-Agent enables organizations to move beyond situational awareness toward true predictive advantage, providing the insight needed to anticipate change, respond faster, and operate more efficiently in complex, data-rich environments.
If you’d like to implement the Real Time Simulation Agent for AI behavioral analytics at your organization, contact us for a demo.
