AIFrontendBackendReal-TimeMachine Learning
Real-Time Bus Tracking System
A real-time transit concept combining GPS tracking, map visualization, Socket.IO communication, and machine-learning-based arrival prediction.
2026In Development
01Project Overview
Overview
A real-time transit experience focused on live vehicle position and predicted arrival information.
02Problem Space
The Problem
Passengers need timely and understandable vehicle location and arrival information instead of static schedules alone.
03Project Objectives
Goals
Visualize live GPS data
Display routes on maps
Explore ML-based arrival prediction
Separate admin and passenger experiences
04System Design
Architecture
01React dashboards
02Socket.IO event layer
03Node.js services
04MongoDB data
05Google Maps visualization
06ML prediction layer
05Engineering Challenges
Challenges
Real-time data synchronization
Mapping live coordinates
Combining prediction with user-facing information
06Implementation
Solutions
Event-driven updates
Map-based visualization
Dedicated prediction layer
07Tech Stack
Technology
React.js
Node.js
MongoDB
Socket.IO
Google Maps API
Machine Learning
08What I Learned
Lessons Learned
Real-time systems require explicit event contracts
UX should make live state easy to understand
09What's Next
Future Improvements
More robust prediction evaluation
Historical route analytics
Driver-facing tools