https://github.com/teresakae/pathspredict
A web-based application with a machine learning model to predict congestion levels for KRL and Transjakarta in Jakarta, helping commuters choose the least crowded transport option.
https://github.com/teresakae/pathspredict
commuting-analysis commuting-patterns traffic-analysis
Last synced: about 1 month ago
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A web-based application with a machine learning model to predict congestion levels for KRL and Transjakarta in Jakarta, helping commuters choose the least crowded transport option.
- Host: GitHub
- URL: https://github.com/teresakae/pathspredict
- Owner: teresakae
- Created: 2025-08-25T09:09:32.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-08-25T09:32:22.000Z (7 months ago)
- Last Synced: 2025-08-25T11:38:30.584Z (7 months ago)
- Topics: commuting-analysis, commuting-patterns, traffic-analysis
- Language: HTML
- Homepage: https://arkteam.pythonanywhere.com/
- Size: 59.6 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## 🚆 PathsPredict - KRL & TransJakarta Congestion Predictor 🚌
A web-based application with a machine learning model to predict congestion levels for KRL and Transjakarta in Jakarta, helping commuters choose the least crowded transport option.
This project was developed as an End-of-Semester Project for "Sistem Informasi Cerdas" course by ARK (Andrew, Runi, Kae)
## 🚀 Features
* **Congestion Prediction**: Utilizes a Logistic Regression model to predict "TINGGI" (High) or "RENDAH" (Low) congestion levels for KRL and Transjakarta.
* **Predictive Dashboard**: Forecasts congestion for upcoming days based on historical and real-time data.
* **Data Management (CRUD)**: Allows for adding, viewing, updating, and deleting historical ridership data.
* **Data Export**: Exports historical and crowdsourced data to an Excel file for further analysis.
* **Real-time Data Integration**: Continuously updates predictions based on new data to maintain accuracy.
## 🛠 Tech Stack
* **Backend**: Python (Flask, Pandas, scikit-learn), Joblib.
* **Database**: In-memory simulation.
* **Frontend**: HTML, CSS, JavaScript.
* **Data Source**: Satu Data Jakarta (2024-2025).
## 📂 Folder Structure
├── Jumlah_Penumpang_Angkutan_Umum_yang_Terlayani_Perhari.csv
├── app.py
├── categorical_features.pkl
├── index.html
├── logistic_regression_penumpang_pipeline.pkl
├── model_features_with_moda.pkl
├── model_training.py
└── numerical_features.pkl
## 🧪 Getting Started
`git clone
cd PredictJakarta
pip install -r requirements.txt
python app.py
Visit http://localhost:5000 to explore the application.`
## ✅ Future Improvements
* Integrate with a real live database for persistent data storage.
* Implement a more sophisticated machine learning model (e.g., a time-series model like ARIMA) for more accurate long-term predictions.
* Build a more interactive and user-friendly front-end dashboard with data visualizations (e.g., charts and graphs).
* Add a feature to predict congestion for specific routes or times of day, rather than just daily averages.