Description:

The Public Transport Delay Prediction System is a data-driven web application that predicts delays in buses, trains, or metro services using historical travel data, live traffic updates, weather conditions, and GPS tracking.
It helps commuters plan their journeys better and transport authorities improve scheduling efficiency.

By applying machine learning models, the system can identify patterns in delays and provide accurate estimated arrival times (ETA) for different routes.

Key Features:

1. Real-Time GPS Tracking – Monitors the live location of buses/trains.

2. Delay Prediction – Uses historical and live data to predict possible delays.

3. Traffic & Weather Integration – Considers real-time congestion and weather effects on delays.

4. Dynamic ETA Updates – Adjusts estimated arrival times automatically based on conditions.

5. Route Performance Analytics – Displays charts showing the most and least reliable routes.

6. User Notifications – Sends SMS/Push alerts for delays and schedule changes.

7. Interactive Map – Shows current vehicle positions and predicted delays visually.

8. Historical Data Dashboard – Allows authorities to study delay trends and improve timetables.

Technology Stack:

• Backend: Node.js / Java / PHP (for APIs, real-time updates)

• Frontend: HTML, CSS, Bootstrap, JavaScript (Google Maps API / Leaflet.js for live tracking)

• Database: MySQL / MongoDB (for storing routes, delays, GPS logs)

• Data Science Layer: Python (pandas, NumPy, scikit-learn, XGBoost for prediction)

• Data Sources: GPS tracking devices, public transport schedules, weather APIs, traffic APIs

Example Use Case:

• In a city bus network, the system collects GPS data from buses every 10 seconds.

• The ML model detects that Route 22 has a high delay probability during rainy mornings.

• When rain starts, the dashboard instantly updates ETAs and sends push notifications to passengers waiting at bus stops.

• Transport authorities adjust the schedule and deploy extra buses to reduce crowding.