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https://github.com/0xnu/ai_hackathon_in_transport2024
AI Hackathon in Transport 2024
https://github.com/0xnu/ai_hackathon_in_transport2024
dft dvsa road-safety transport
Last synced: 20 days ago
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AI Hackathon in Transport 2024
- Host: GitHub
- URL: https://github.com/0xnu/ai_hackathon_in_transport2024
- Owner: 0xnu
- License: mit
- Created: 2024-05-17T12:00:16.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-05-18T10:24:22.000Z (8 months ago)
- Last Synced: 2024-05-19T08:20:03.797Z (8 months ago)
- Topics: dft, dvsa, road-safety, transport
- Language: Jupyter Notebook
- Homepage:
- Size: 5.16 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## AI Hackathon in Transport 2024 - Team 4
This repository contains the work of Team 4 for the [AI Hackathon in Transport 2024](https://rsvp.withgoogle.com/events/dft-ai-hackathon-2024). Our project focuses on analysing accident data in [Greater Manchester](https://en.wikipedia.org/wiki/Greater_Manchester) from 2010 to 2021 and developing tools to provide insights to citizens and policymakers.
### Files
1. `unified_dataset.csv`
- Contains unified datasets of accidents, casualties and vehicles in [Greater Manchester from 2010-2021](https://www.data.gov.uk/dataset/25170a92-0736-4090-baea-bf6add82d118/gm-road-casualty-accidents-full-stats19-data)
- Combines and cleans the raw [accident](https://odata.tfgm.com/opendata/downloads/STATS19AccDataJan2010Dec2021forGMServers.csv), [casualty](https://odata.tfgm.com/opendata/downloads/STATS19CasDataJan2010Dec2021forGMServers.csv) and [vehicle](https://odata.tfgm.com/opendata/downloads/STATS19VehDataJan2010Dec2021forGMServers.csv) CSV files
- Use this as the master dataset for all subsequent analysis and visualisation
- The [Jupyter Notebook](./manchester_accident_create_unified_dataset.ipynb) for creating a unified dataset is here.
2. `manchester_accident_severity1_heatmap.ipynb`
- [Jupyter Notebook](./manchester_accident_severity1_heatmap.ipynb) generates an interactive heatmap of the severity of one accident in Greater Manchester
3. `manchester_accident_analysis.ipynb`
- The [Jupyter Notebook](./manchester_accident_analysis.ipynb) perform an in-depth analysis of the unified accident dataset.
- Generates insights such as clusters of top high-risk areas and top contributing factors.
4. `manchester_intelligent_road_safety_hub.py`
- [Code](./manchester_intelligent_road_safety_hub.py) for an interactive web portal presenting accident insights to citizens and policymakers
- Built using Gradio for the frontend interface
- Leverages the OpenAI API to power a simple chatbot that can answer questions about Manchester accidents
- Potential Question: What are the top three reasons for accidents in Greater Manchester with an explanation?
### Getting Started
1. Clone this repository and navigate to the project directory
2. Run the Jupyter notebooks in sequential order to generate the [unified dataset](manchester_accident_create_unified_dataset.ipynb), [generate heatmap](manchester_accident_severity1_heatmap.ipynb) and [perform analysis](manchester_accident_analysis.ipynb)
3. Install the required Python libraries: `pip install -r requirements.txt`
4. Set the environment variable OPENAI_API_KEY in the current shell session: `export OPENAI_API_KEY="your_openai_api_key"`
5. Launch the interactive portal by running: `python3 manchester_intelligent_road_safety_hub.py`
### Contributors
Ada x Amine x Harry x Finbarrs x Jill x Jamie x Hakem
> Thank you to our [Department for Transport](https://www.gov.uk/government/organisations/department-for-transport) (DfT), [Google](https://about.google/google-in-uk/) and [PA](https://www.paconsulting.com/) colleagues.
### License
This project is licensed under the [MIT License](./LICENSE).
### Citation
```tex
@misc{aihackathonintransport2024,
author = {Oketunji, AF. et al},
title = {AI Hackathon in Transport 2024 (0.0.1)},
year = 2024,
publisher = {Zenodo},
doi = {10.5281/zenodo.11213634},
url = {https://doi.org/10.5281/zenodo.11213634}
}
```
### Copyright
(c) 2024 DfT - [Department for Transport](https://www.gov.uk/government/organisations/department-for-transport).