Welcome to the Traffic Accident Prediction project! This project utilizes machine learning techniques and historical data (e.g., accident records, weather conditions, traffic volume, road characteristics) to predict the likelihood of traffic accidents. Our aim is to provide insights into high-risk areas and conditions, enabling local authorities to implement targeted safety measures and improve traffic management strategies.
- Overview
- Features
- Installation
- Usage
- Dataset
- Model Architecture
- Evaluation
- Results
- Future Improvements
- Contributing
- License
Traffic accidents can have severe consequences, both human and economic. This project aims to leverage deep learning to predict accident risks based on various features:
- 📅 Date & Time
- 🌦️ Weather Conditions
- 🚗 Traffic Volume
- 🛣️ Road Characteristics
By identifying high-risk situations, the model helps authorities and traffic planners reduce accident occurrences and improve public safety.
- ✅ Accurate Predictions: Provides reliable accident risk assessments based on multiple inputs.
- 📊 Data Visualization: Visualizes accident patterns with charts and graphs for better insights.
- 🚧 Model Evaluation: Uses evaluation metrics like accuracy, precision, recall, and F1-score.
- 🌐 Scalable: Easily extendable to include more features like traffic camera data.
To get started, clone this repository and install the required dependencies:
git clone https://github.com/alo7lika/traffic-accident-prediction.git
cd traffic-accident-prediction
pip install -r requirements.txtEnsure you have Python 3.8+ installed along with the necessary libraries (e.g., pandas, numpy, scikit-learn, tensorflow).
To run the model, follow these steps:
- Preprocess the Dataset:
python preprocess_data.py
- Train the Model:
python train_model.py
- Evaluate the Model:
python evaluate_model.py
Make sure the dataset is correctly placed in the data/ directory. You can adjust the hyperparameters in config.yaml.
| Feature | Description |
|---|---|
date_time |
Date and time of the incident |
weather |
Weather conditions at the time |
traffic_volume |
Number of vehicles passing per hour |
road_type |
Type of road (highway, city road) |
The dataset is stored as a CSV file in the data/ folder. If you have new data, update the file accordingly.
The model consists of a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) layers for feature extraction and time-series analysis. The architecture includes:
- Input Layer: Processes the input features (e.g., weather, traffic volume).
- CNN Layers: Extracts spatial features.
- LSTM Layers: Captures temporal dependencies.
- Dense Layers: Combines extracted features and outputs the prediction.
The model’s performance is evaluated using the following metrics:
| Metric | Description |
|---|---|
| Accuracy | The overall correctness of the model |
| Precision | Ratio of correctly predicted positive observations |
| Recall | Ratio of correctly predicted positive observations to the actual positives |
| F1-Score | Harmonic mean of precision and recall |
The model achieved the following performance on the test dataset:
| Metric | Value |
|---|---|
| Accuracy | 92% |
| Precision | 89% |
| Recall | 90% |
| F1-Score | 89.5% |
You can visualize the model's predictions using the visualize_results.py script.
- 🔄 Real-time data integration: Incorporate live traffic and weather data for real-time accident risk assessment.
- 🛰️ Satellite data: Integrate satellite imagery for more precise road condition analysis.
- 🧠 Model Optimization: Fine-tune hyperparameters and try other neural network architectures.
Contributions are welcome! Follow these steps to contribute:
- Fork the repository.
- Create a new branch (
git checkout -b feature-branch). - Commit your changes (
git commit -m "Add feature"). - Push to the branch (
git push origin feature-branch). - Open a Pull Request.
Please read the CONTRIBUTING.md for more details.
This project is licensed under the MIT License. See the LICENSE file for more details.