AIoT-Mauritius/MorrisTrafik

AI based project to solve Traffic congestion in Mauritius # Automated CCTV Traffic Data Extraction and Analysis System This project provides a complete system for automated data extraction and analysis from live public CCTV feeds. It leverages open-source tools like OpenDataCam and SUMO to build a data pipeline for training and evaluating AI-based adaptive traffic control systems. ## Table of Contents - System Overview - Features - Project Structure - Installation - Usage - AI Models - Contributing - License ## System Overview The system is designed to perform the following tasks: 1. **Data Acquisition**: Record video footage from public CCTV feeds. 2. **Data Extraction**: Use OpenDataCam to extract traffic data (vehicle counts, speeds, trajectories) from the recorded videos. 3. **Simulation Environment**: Generate a realistic SUMO simulation of the target intersection using data from OpenStreetMap. 4. **Data Transformation**: Process the extracted traffic data to calibrate the SUMO simulation. 5. **AI-based Traffic Control**: Train and evaluate AI agents (LSTM for congestion prediction and DQN for traffic light control) in the SUMO environment. ## Features - Automated data extraction from video files using OpenDataCam. - Generation of realistic SUMO traffic simulations from OpenStreetMap data. - Calibration of SUMO simulations using real-world traffic data. - Implementation of an LSTM-based Predictive Congestion Agent (PCA). - Implementation of a DQN-based Local Junction Agent (LJA) for adaptive traffic light control. - Comprehensive data visualization and analysis tools. ## Project Structure ``` /cctv-traffic-analysis |-- config/ # Configuration files |-- data/ | |-- raw/ # Raw video files | `-- processed/ # Processed traffic data (CSVs) |-- docs/ | `-- figures/ # Generated figures and plots |-- models/ | |-- lstm_congestion_predictor.py | `-- dqn_junction_agent.py |-- opendatacam/ # OpenDataCam installation and data | …