AI-Powered Campus Transit & Micro-Mobility Optimization Engine

### Academic Context & Background Collegiate campuses cover extensive terrain where thousands of students, faculty, and campus staff commute between lectures and research buildings within tight 10-15 minute windows. Current campus bus transit systems lack dynamic telemetry, elevation-aware routing for students with disabilities, and battery station balancing for electric micro-mobility. ### Challenge Requirements Student engineering teams are tasked with designing and deploying a multi-modal route optimization system that: 1. Ingests live telemetry (GPS coordinates, transit bus delays) and campus walking paths. 2. Formulates an algorithm balancing pedestrian comfort, transit arrival times, and carbon offset tracking. 3. Implements an accessible web interface for both desktop and mobile devices. ### Suggested Architecture - Web Frontend: Next.js or React with interactive Leaflet/Mapbox maps - Backend & Database: Supabase PostgreSQL with PostGIS extension for geo-spatial querying - Streaming: WebSockets or MQTT for live bus beacon signals ### Evaluation Criteria - Route optimality and response latency (< 300ms for path calculations) - Accessibility compliance (WCAG 2.1 AA) - Live prototype deployment and documentation quality