Let's Discuss: Real-Time Bus Tracking Application (with ETA Prediction)

[Code-master31]

Select Topic Area

Show & Tell

Feature Area

Projects

Body

Hi everyone! 👋

I'm working on a real-time bus tracking and ETA prediction system, and I’d love to get your thoughts, feedback, and ideas. This is aimed at being a lightweight, scalable, open-source solution that could be used in cities or college campuses to monitor local transportation in real time.

🚀 Project Overview

The app will:

1. Show live locations of buses on a map using GPS data.
2. Estimate arrival times at bus stops using historical and live traffic data.
3. Provide alerts and notifications for delays or schedule changes.
4. Offer an admin panel for managing routes, stops, and fleet.

💡 Tech Stack Ideas

1. Frontend: React.js with Leaflet.js or Google Maps API
2. Backend: Node.js / Express with Socket.IO for real-time communication
3. Database: MongoDB / PostgreSQL
4. ML Model (ETA Prediction): Python (Scikit-learn / XGBoost), trained on route & time-series data
5. Deployment: Docker, Heroku / AWS

🧩 Areas I’d Love Input On

1. Open datasets for historical bus timings and GPS tracks — any recommendations?
2. Best approach to handle real-time location updates at scale (WebSockets vs MQTT?)
3. Suggestions for a simple but effective ETA prediction model that works with noisy data
4. Thoughts on user features: e.g., bookmark bus stops, set alerts, route planning?

🙌 How You Can Contribute

1. Share ideas or similar projects you've seen
2. Suggest libraries or better tools
3. Help build frontend components or backend APIs

💬 Drop your suggestions below ⬇️

Guidelines

• I have read the above statement and can confirm my post is relevant to the GitHub feature areas Issues and/or Projects.

[ARUNAGIRINATHAN-K]

This is a fantastic project! 🚍 I love the scope and real-world impact. Here are some thoughts that might help refine your approach across data, architecture, and ML:

📊 1. Datasets for Bus Timings & GPS Tracks
A few solid public datasets you can explore:

TransitFeeds (https://transitfeeds.com/): Offers GTFS data (schedules + real-time updates) for multiple cities.

NYC MTA: Offers APIs for live bus locations and historical performance data.

TFL (Transport for London): Great for both scheduled and real-time transport datasets.

OpenStreetMap: For geolocation of stops/routes + street networks.

🧠 Tip: If you're simulating for campus use, create synthetic GPS data using Python + Faker + GeoJSON.

🌐 2. Real-Time Location Updates: WebSockets vs MQTT
TL;DR:

✅ Use MQTT if you're scaling to hundreds/thousands of buses, especially on mobile networks. Lightweight, low latency.

✅ Stick with WebSockets for easier setup and browser-based dashboards.

Feature WebSocket MQTT
Protocol TCP TCP (can work over MQTT brokers)
Overhead Higher Very low
Ideal Use Case Web apps, admin tools IoT devices, GPS modules
Broker Needed No Yes (e.g., Mosquitto, EMQX)

🔧 If future scalability is a goal, build a pluggable interface that supports both.

🧠 3. ETA Prediction Model: Light, Interpretable, and Effective
For a balance of speed + accuracy, try:

Baseline model: Historical average ETA per segment + time window

Enhanced model: XGBoost using:

Current speed

Distance to stop

Time of day, day of week

Historical delays

Traffic congestion level (if available via API)

Add fallback logic: if ML fails, use Haversine + average speed approximation.

🛠 You can also prototype using Facebook’s Prophet for time-series ETA forecasting.

✨ 4. Suggested User Features
📍 Bookmark Stops & Routes

🔔 Push Notifications for ETAs & delays (OneSignal or Firebase)

🔄 Live route planner with multi-stop options

🗺️ Heatmap of congestion (based on historical delay clusters)

🕵️‍♂️ Bus occupancy estimates (from speed/dwell time at stops)

Optional: gamify user feedback (e.g., “rate your ride” → improve predictions).

🚀 Bonus Suggestion: Use a Hybrid System
Frontend: React + Leaflet (open, lightweight)

Admin Panel: Separate dashboard using React Admin

Realtime: MQTT (for device <-> backend), WebSocket (for frontend updates)

Backend: Express + Python microservice for ML

DB: PostgreSQL with PostGIS for spatial queries

[Code-master31]

Thankyou for your Contribution