https://github.com/magnus0969/traffic-flow-prediction
This project aims to analyze and forecast traffic flow using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The dataset comprises traffic flow data collected from multiple junctions, with a focus on daily vehicle counts at Junction 1.
https://github.com/magnus0969/traffic-flow-prediction
forecasting-time-series python sarima-model time-series-analysis traffic-analysis
Last synced: 27 days ago
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This project aims to analyze and forecast traffic flow using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The dataset comprises traffic flow data collected from multiple junctions, with a focus on daily vehicle counts at Junction 1.
- Host: GitHub
- URL: https://github.com/magnus0969/traffic-flow-prediction
- Owner: Magnus0969
- License: mit
- Created: 2024-06-14T18:05:41.000Z (over 1 year ago)
- Default Branch: data
- Last Pushed: 2025-02-05T14:06:41.000Z (8 months ago)
- Last Synced: 2025-06-24T13:55:13.196Z (3 months ago)
- Topics: forecasting-time-series, python, sarima-model, time-series-analysis, traffic-analysis
- Language: Jupyter Notebook
- Homepage:
- Size: 1.2 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Traffic Flow Prediction
Traffic Flow Prediction is a time-series forecasting project designed to predict traffic congestion using historical data. The project utilizes the SARIMA (Seasonal AutoRegressive Integrated Moving Average) model to analyze traffic patterns and forecast future conditions.
## Table of Contents
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Dataset](#dataset)
- [Model](#model)
- [Results](#results)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)
## Features
- Time-series prediction of traffic flow
- Utilizes SARIMA model for forecasting
- Data preprocessing and feature engineering
- Model evaluation with performance metrics
## Installation
To install and run the project locally, follow these steps:
1. Clone the repository:
```sh
git clone https://github.com/Magnus0969/Traffic-Flow-Prediction.git
cd Traffic-Flow-Prediction
```
2. Create a virtual environment (optional but recommended):
```sh
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
3. Install dependencies:
```sh
pip install -r requirements.txt
```
## Usage
Run the following command to train the model:
```sh
python train.py
```
To make predictions using a trained model:
```sh
python predict.py --input data/sample_input.csv
```
## Dataset
The dataset consists of time-series traffic data, including:
- Timestamps
- Traffic volume
- Weather conditions
- Road conditions
## Model
The project primarily uses the SARIMA model for traffic prediction. The methodology includes:
- Identifying seasonality and trends in traffic data
- Differencing and transformation to achieve stationarity
- Parameter tuning for optimal SARIMA configuration
## Results
Model performance is evaluated using metrics such as:
- Mean Absolute Error (MAE)
- Root Mean Squared Error (RMSE)
- R-squared score (R²)
## Contributing
Contributions are welcome! To contribute:
1. Fork the repository
2. Create a new branch (`git checkout -b feature-branch`)
3. Commit your changes (`git commit -m 'Add new feature'`)
4. Push to the branch (`git push origin feature-branch`)
5. Open a Pull Request
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Contact
For any inquiries, reach out via:
- Email: kmagadi09@gmail.com