@dharshiniramamoorthy677

MetroMind is an AI-driven decision-support platform for Kochi Metro Rail Limited (KMRL) that helps operations staff plan train induction and daily scheduling. It forecasts passenger demand by station, direction, time window and weather using an XGBoost regression model served through a Python FastAPI service. Based on these forecasts, it generates conflict-free timetables that enforce safety headway floors, turnaround buffers and platform capacity limits, and assigns available trainsets from the 25-train fleet while excluding those under maintenance. The platform also includes fleet and maintenance management, with corrective job cards, Certificate of Fitness (CoF) verification and withdrawal of defective trains. A role-based governance workflow gives Administrators, Operations Controllers, Schedule Approvers (SADA) and Maintenance Supervisors (MDS) dedicated dashboards, and every schedule proposal needs human approval before it becomes operational. An alert engine evaluates thresholds every 30 seconds and classifies alerts by severity, and authorised roles can export PDF reports. The backend uses Java 17 and Spring Boot microservices with Netflix Eureka for service discovery, Spring Cloud Gateway for routing and JWT authentication. Data is stored in Oracle Database and MongoDB, and the frontend is built in React 18. MetroMind is a planning tool only. It does not control trains or signalling.

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