Development of an AI-Driven Smart Public Transport Routing and Tracking System

The client struggles with providing accurate real-time public transportation information, optimizing route efficiency, predicting vehicle arrivals with high accuracy, and effectively communicating route changes and traffic updates to residents, leading to reduced commuter satisfaction and operational inefficiencies.

A metropolitan transportation authority seeking to enhance city mobility through intelligent route planning, real-time vehicle tracking, and predictive analytics to improve commuter experience and operational efficiency.

• Deliver real-time public transport route and vehicle tracking information to users via mobile and web platforms.
• Display current fare information for various transport modes.
• Calculate and recommend the most cost-effective or shortest routes for passengers between specified points.
• Automate notifications for route changes, delays, and traffic updates through push messages.
• Provide AI-based predictions of vehicle arrivals with a precision of down to the minute.
• Forecast traffic conditions to optimize scheduled routes and enhance overall system reliability.

• Interactive city map displaying real-time public transport vehicle locations (metro, tram, trolleybus, bus).
• Live tracking of individual vehicles with movement updates as markers on the map.
• AI-powered prediction of vehicle arrival times at stops, updated in real time.
• Management interface for administrators to create, edit, enable, or disable routes and to send targeted push notifications.
• User features including favorite stops, preferred routes, and multi-modal navigation options.
• Algorithm-driven route suggestions from point A to B, considering shortest, fastest, and most economical options.

• GPS and vehicle telematics systems for live vehicle data
• Public transport schedule and fare databases
• Notification services for delivering push updates
• Traffic and weather data APIs for predictive analytics

• System should support concurrent access by thousands of users with minimal latency.
• Real-time data processing with update intervals of less than 5 seconds.
• High availability architecture to ensure 99.9% uptime.
• Secure user authentication and data encryption standards.
• Scalable infrastructure to accommodate city expansion and increased user base over time.

Implementation of the AI-driven smart public transport system aims to enhance commuter experience through accurate real-time information, reduce average waiting times through precise predictions, and improve route efficiency and traffic management. Expected key outcomes include increased user satisfaction, operational cost reductions, and a foundation for expanding intelligent urban mobility solutions in future urban settings.