FamenoRa/TraffiQ---Team-4-Quantathon-AIMS-Ghana-2025

Quantum-AI Traffic Optimization # Quantathon-2025---TEAM-4 Quantum-AI Traffic Optimization Traffic congestion is a critical and costly challenge across many African cities, impacting economic productivity, quality of life, and environmental sustainability. The project demonstrates the use of quantum-classical hybrid algorithms (primarily QAOA) to optimize traffic congestion and green-light timings using quantum approach. ## SDG11 ## Next Steps ### Please add after each title your ideas and/or suggestions and DO NOT FORGET TO COMMIT CHANGES (green button top right) 1. Problem dee dive/refine idea 3. Look for available traffic /geospatial data - both simulated and real 5. Create a work plan and assign responsabilities - nest meeting ## Datasets: 1- From kaggle : kaggle.com traffic.csv 2- From UTD19 under the following Terms and Conditions: The data will be used only for academic and/or non-commercial purposes. For any publication that utilizes the UTD19 dataset, authors should include a reference to doi.org. Include the data source as UTD19 (utd19.ethz.ch) in the acknowledgment section of your publication. These are the datasets titled: manual.pdf, links.csv, detectors_public.csv, utd_19_u.csv ## Usage - install packages with `poetry install` - run optimization with `./optimize.sh` - for streamlit interface first run `cp run.examle.sh run.sh` - fill up the environment variables - then run `./run.sh`