Traffic plus

Traffic Plus is an AI-powered digital twin that monitors traffic, predicts congestion, detects accidents, optimizes routes, and prioritizes emergency vehicles using intelligent AI agents.u

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Inspiration

Traffic congestion is one of the biggest challenges in rapidly growing cities like Bengaluru. Silk Board Junction is well known for heavy traffic, long waiting times and delays for emergency vehicles. We wanted to develop a smarter solution that goes beyond traditional traffic monitoring.

This inspired us to build Traffic Plus, an AI-powered Smart Traffic Digital Twin that creates a virtual representation of a real-world junction to monitor, analyze and simulate traffic conditions and support intelligent traffic management.

What it does

Traffic Plus aims to make urban traffic management smarter, faster and more efficient through AI and digital twin technology.

  • Vehicle Detection: Uses YOLOv8 and computer vision to detect, classify and count vehicles.
  • Traffic Monitoring: Analyzes traffic density, vehicle volume and road conditions.
  • Congestion Prediction: Uses traffic data to identify potential congestion.
  • Smart Route Optimization: Helps identify alternative routes to reduce delays.
  • Emergency Vehicle Prioritization: Supports ambulance routing and simulated emergency green corridors.
  • 3D Digital Twin: Creates a virtual traffic environment to visualize roads, vehicles and traffic scenarios.
  • Traffic Simulation: Uses SUMO to simulate traffic movement and evaluate different traffic management strategies.

How we built it

We developed Traffic Plus by integrating multiple technologies:

  1. Python: Core processing, traffic analysis and backend logic.
  2. YOLOv8 and OpenCV: Vehicle detection, classification and counting from traffic videos.
  3. Traffic Dataset: Used Bangalore traffic data for understanding traffic patterns and congestion.
  4. MQTT: Enables real-time communication between system components.
  5. SUMO: Simulates vehicle movement and traffic conditions using a road network.
  6. Blender: Creates realistic 3D road structures and environmental assets.
  7. Unity: Provides an interactive environment for visualizing the digital twin.
  8. NetworkX and FastAPI: Support route optimization and backend services.

Our approach combines real-world traffic information, AI-based analysis, simulation and 3D visualization into a unified system.

Challenges we ran into

  • Integrating real-world traffic data with a virtual simulation environment.
  • Processing large volumes of vehicle detection data efficiently.
  • Converting geographical road networks into simulation-ready environments.
  • Connecting real-time MQTT communication with the traffic simulation.
  • Creating realistic 3D road structures and integrating assets into the visualization pipeline.
  • Coordinating multiple technologies into one integrated system.

We addressed these challenges through modular development, continuous testing and integration of different technologies.

Accomplishments that we're proud of

  • Developed a YOLOv8-based vehicle detection pipeline for traffic analysis.
  • Processed over 272,000 vehicle detection records.
  • Integrated an MQTT-based real-time communication pipeline.
  • Built a SUMO-based traffic simulation environment for Silk Board Junction.
  • Generated 359 road lane objects and corresponding 3D road surfaces in Blender.
  • Established a workflow connecting traffic analysis, simulation and 3D visualization.
  • Designed the foundation for intelligent route optimization and emergency traffic management.

We are proud to bring together AI, simulation and digital twin technology to address a real-world urban transportation challenge.

What we learned

Through this project, we gained practical experience in computer vision, machine learning, real-time communication, traffic simulation, 3D modeling and system integration.

We learned how to process traffic data, detect vehicles using YOLOv8, work with geographical road networks, simulate vehicle movement using SUMO and develop realistic 3D environments using Blender.

Most importantly, we understood how digital twin technology can connect physical infrastructure with intelligent, data-driven decision-making.

What's next for Traffic Plus

Our vision is to evolve Traffic Plus into a predictive and multi-agent intelligent traffic management platform.

Future developments include:

  • AI Multi-Agent System: Specialized agents for traffic monitoring, accident detection, route optimization and emergency response.
  • Adaptive Traffic Signals: Intelligent signal timing based on real-time vehicle density.
  • Accident Detection: Automatic identification of road incidents and affected routes.
  • Emergency Green Corridors: Prioritizing ambulance movement through coordinated route planning and simulated traffic signals.
  • Predictive Analytics: Forecasting congestion before it becomes critical.
  • What-if Simulation: Testing different traffic scenarios, road closures and traffic management strategies.
  • Advanced 3D Digital Twin: Developing a more realistic, interactive and data-driven virtual representation of Silk Board Junction.

Our ultimate goal is to transform Traffic Plus from a traffic monitoring solution into an intelligent traffic management ecosystem that helps create safer, smarter and more efficient cities.

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