Chattanooga Digital Twin for Transportation

Leadership

• Led the data science team in deriving insights from different sensor data through analysis, visualization, and machine learning.
• Served as technical lead during the final year of the project.
• Communicated with city and state departments of transportation and other organizations to understand their priorities, inquire details about the data provided, and share insights and results.

Situational Awareness Tool

• Majorly contributed to the orchestration of CTwin, a real-time decision support platform (Docker, PostgreSQL, Angular, GeoServer, OpenLayers) which integrated data from over 30 sources and over 300 sensors.

Accident Detection

• Formulated a novel methodology for accident detection on highway systems.
• Led the development of an accident detection tool which identified accidents 2-3 minutes sooner than reports via 911 calls.

Traffic Signal Control

• Orchestrated experiments for a real-time traffic signal control in close collaboration with NREL and CDOT, and achieved a 19.4% travel time reduction compared to time-of-day scheduling.

Selected Publications:

• Chattanooga Digital Twin System
• Accident Detection from Sensor Data: Machine Learning Methodology, Data Workflow, and Dataset.
• Smooth Traffic Emulation from Stationary Sensor Data
• Visualizing Real-Time Traffic Operations Data
• Visualizing Traffic Safety
• Freight Safety (Presentation)

Pending Patents 💡

• Systems and Methods for Training and or Using Machine Learning Models to Detect Traffic Accidents on Roads

Awards 🏆

• Significant Event Award for the successful deployment of CTwin 1.1