Ingest, model, and optimize. We provide mission-critical infrastructure data and AI-driven congestion management for smart cities, DOTs, and enterprise logistics.
An end-to-end intelligence suite for urban infrastructure.
Ingestion, processing, and analysis of massive, real-time datasets including sensor feeds, IoT arrays, and GPS telemetry at true enterprise scale.
High-fidelity simulation and predictive modeling of vehicle and pedestrian flow under complex conditions, weather events, and structural changes.
Real-time bottleneck detection, adaptive signal control overrides, and automated incident response tools designed to slash travel times and emissions.
Deep learning computer vision for vehicle detection, anomaly tracking, and LLM-powered natural language reporting overlays for operational commands.
Move beyond static origin-destination matrices. DataNomic Labs utilizes continuous macroscopic traffic flow models based on the kinematic wave theory, incorporating conservation equations such as $ \frac{\partial \rho}{\partial t} + \frac{\partial (\rho u)}{\partial x} = 0 $ to calculate density ($\rho$) and flow velocity ($u$) across vast municipal grids in real time.
A robust, scalable pipeline moving from raw endpoints to actionable operational intelligence.
IoT Sensors, CCTV, GPS, 3rd-Party APIs
Kafka Streaming, Data Normalization
Computer Vision, Predictive Analytics
Dashboards, Signal Overrides, Policy
Real-world deployments. Measurable results.
A major 12-mile arterial corridor was experiencing severe gridlock during rush hour, compounded by unpredictable freight rail crossings.
Deployed DataNomic's ML-driven signal override system, integrating real-time API feeds from rail operators with existing municipal loop detectors.
Static routing software could not account for micro-weather events or spontaneous urban bottlenecks, leading to missed delivery SLAs.
Integrated our continuous anomaly tracking API directly into the client's fleet management software for dynamic rerouting.