Rail Madad with AI-Powered Complaint Management System

Abstract

Rail Madad is an essential platform for passenger grievance redressal in Indian Railways, but its current manual categorization and routing system often causes delays, inefficiencies, and inaccurate resolutions. These challenges are more critical when passengers submit complaints in the form of photos, videos, or audio, which require significant manual effort to interpret. To address this, the proposed project introduces an AI-powered Complaint Management System that integrates automation, intelligence, and predictive analytics into Rail Madad. Using computer vision, the system analyzes images and videos to automatically categorize complaints such as cleanliness, staff behavior, or infrastructure damage, while urgency detection models prioritize critical issues like safety hazards. Optical Character Recognition (OCR) extracts relevant text from visual data, and metadata analysis enhances context with time and location details. AI chatbots provide instant acknowledgments, gather additional inputs, and reduce response time. Smart routing algorithms ensure accurate forwarding of complaints to the right departments for quick action. Predictive maintenance models further analyze complaint trends to identify recurring issues and suggest proactive interventions. Sentiment analysis of passenger feedback and AI-driven performance monitoring ensure continuous improvement. By automating classification, prioritization, and routing, the system enhances resolution speed, accuracy, scalability, and passenger satisfaction, ultimately transforming Rail Madad into a faster, smarter, and more reliable grievance redressal system.