Primary supervisor

Isma Farah Siddiqui

This project focuses on implementing an AI-powered digital twin for intelligent electric vehicle (EV) traffic management in smart cities, utilising the YOLO algorithm. It develops a basic digital twin system designed to monitor and manage EV traffic in urban areas. The system detects and tracks EVs using feeds from traffic cameras. The digital twin simulates traffic flow, providing a visualisation of EV movement, congestion points, and route patterns.

Student cohort

Single Semester

Aim/outline

To develop a lightweight digital twin framework that uses YOLO-based computer vision to detect and manage electric vehicle (EV) traffic in urban environments, supporting smarter mobility decisions and traffic flow visualisation. Here is a tentative outline for the project:

  • Introduction – Define the problem of EV traffic in smart cities and the role of AI and digital twins.
  • Literature Review – Summarise existing work on YOLO, digital twins, and EV traffic systems.
  • System Design – Describe the architecture, including YOLO detection, data flow, and simulation.
  • Implementation – Build the YOLO model, integrate with traffic data, and simulate EV movement.
  • Visualisation & Testbed – Create a dashboard and testbed to display and experiment with traffic scenarios.
  • Results & Evaluation – Analyse detection accuracy, system performance, and usability.
  • Conclusion – Summarise findings, contributions, and suggest future improvements.

Required knowledge

  • Computer Vision

    • Basics of image processing.
    • Object detection algorithms (especially YOLO).
    • Working with image datasets and annotations.

  • Machine Learning / Deep Learning

    • Neural networks and training models.
    • Using frameworks like PyTorch or TensorFlow.
    • Model evaluation and performance metrics.

  • Programming Skills

    • Proficiency in Python (essential for AI and simulation).
    • Familiarity with libraries like OpenCV, NumPy, Matplotlib.

  • Digital Twin Concepts

    • Understanding of digital twin architecture.
    • Simulation of real-world systems using virtual models.

  • Urban Mobility & EV Systems

    • Basics of traffic flow and electric vehicle infrastructure.
    • Knowledge of smart city technologies and IoT.

  • Data Handling

    • Working with real-time data (e.g., traffic feeds, sensors).
    • Data cleaning, integration, and visualization.

  • Software Tools

    • Jupyter Notebook or VS Code for development.
    • Git for version control.
    • Simple dashboard tools (e.g., Dash, Streamlit).