Overview
The Logistics ETA Prediction System is a machine-learning solution designed to forecast the estimated time of arrival (ETA) for delivery trucks based on real operational data. It analyzes multiple factors such as route distance, traffic patterns, weather, delivery type, historical delays, and driver behavior to generate accurate arrival-time predictions. This model helps logistics teams reduce uncertainty, optimize delivery schedules, and improve customer satisfaction by providing more reliable time windows.
Technologies
- Python, Scikit-learn
- XGBoost / Isolation Forest
- MongoDB Atlas + Firestore Hybrid Architecture
- FastAPI + Docker