Learning-based traffic analysis
Built on Graph Neural Networks trained on real-world transportation data.
About us
We build learning-based traffic analysis models to support infrastructure planning decisions
FlowSense is developing a learning-based traffic modeling platform built on Graph Neural Networks trained on real-world transportation data. Traditional traffic modeling workflows are powerful but often require extensive manual calibration and are difficult to carry forward across studies. As a result, valuable institutional knowledge is frequently lost between projects.
FlowSense introduces a learning layer that captures recurring traffic behavior across networks, allowing agencies and engineers to evaluate scenarios with greater consistency and less repetitive effort. We are not replacing existing simulation tools. We are building foundational models that complement current workflows and improve how traffic behavior is represented over time.
Our current focus is building and validating an initial foundational model using real-world roadway data in collaboration with transportation agencies and domain experts.
Explore
FlowSense maintains a prototype dashboard to demonstrate how our learning-based traffic models can be accessed and explored in practice. The dashboard connects to our current model pipeline and visualizes network-level traffic behavior, scenario inputs, and resulting performance metrics using real-world roadway data.
Built on FlowSense’s internal model APIs and cloud-based training and inference infrastructure.
This prototype is intended for technical review and early validation. It represents one implementation of the underlying modeling framework, not a finished product interface.
About us
Our founding team combines deep expertise in transportation engineering, data science, and machine learning. Together, we’re uniting domain knowledge with rigorous machine learning methods to advance how mobility systems are analyzed and planned.
Founder / CEO
Transportation engineer with years of experience in traffic modeling, simulation, and corridor studies, leading projects for state and city agencies. Passionate about leveraging AI to transform how traffic analysis is done.
Founding Research Scientist
Machine learning researcher with deep expertise in graph-structured data, geometric deep learning, and sequence modeling. Interested in developing scalable, accurate, and explainable computational models which integrate well-tested theory with real-world mobility networks..
Founding Machine Learning Engineer
Machine learning engineer with end-to-end expertise in graph data science, scalable architectures and MLOps to bridge research, engineering, and deployment for reliable real-world mobility networks.
Advisor
Business strategist, notable for building scalable tech platforms and leading cross-functional teams. Expert in product vision, operations, and forming impactful partnerships.