Urban traffic congestion imposes a severe and measurable cost on emergency response, delaying ambulances, fire trucks, and police vehicles at precisely the moments when minutes determine patient outcomes. Existing traffic-signal infrastructure is predominantly static or locally reactive and provides no mechanism for city-scale, real-time emergency prioritization, hospital pre-arrival awareness, or verifiable, role-based control over who may exercise such priority. This paper presents the design of NexGen, a software-only intelligent transportation system that combines real-time GPS tracking, machine-learning-assisted route optimization, and dynamic, temporary-corridor traffic-signal control across three coordinated interfaces — an emergency-vehicle application, a hospital dashboard, and a traffic-control-center dashboard with a dedicated field-officer mode. The design places particular emphasis on the identity-and-access layer: a shared, accessible, light-themed UI spans login, role-specific registration, verification, and a formal administrator account-approval workflow with step-up re-authentication and multi-factor authentication for high-privilege roles, ensuring that emergency-priority capability is granted only to verified, auditable accounts. Every external dependency required by this interface layer is selected from free-tier or key-less providers, preserving a zero-recurring-cost, software-only architecture suitable for academic and municipal pilot deployment alike. Design targets include a 30-40% reduction in ambulance response time, GPS-tracking accuracy within 5-10 meters, and signal-priority activation within 2-3 seconds of vehicle detection, to be validated through simulation and structured testing.

Keywords — Intelligent Transportation Systems; Emergency Vehicle Priority; Dynamic Traffic Signal Control; GPS Tracking; Machine Learning; Route Optimization; Role-Based Access Control; Account-Approval Workflow; Multi-Factor Authentication; Accessible UI/UX Design; Hospital Integration.