Rail Madad: Enhancing Passenger Support Through AI

Abstract

Rail Madad, an official grievance redressal platform introduced by Indian Railways, plays a vital role in addressing passenger issues and ensuring service quality. However, the current complaint management process is largely manual, which often leads to delays, inefficiencies, and inconsistencies in complaint categorization and routing. With the increasing volume and variety of complaints, particularly those submitted as multimedia files such as images, videos, and audio recordings, the system faces significant challenges in timely and accurate processing [1], [2]. To overcome these challenges, this project proposes an Artificial Intelligence (AI)-based automated complaint management framework for Rail Madad. The proposed solution leverages advanced AI techniques such as image and video recognition, Optical Character Recognition (OCR), and Natural Language Processing (NLP) to automatically categorize and prioritize complaints based on content [1], [3]. Furthermore, machine learning models are utilized for predictive maintenance by analyzing recurring complaint trends, while AI chatbots provide instant acknowledgment and collect additional user information. Sentimentan alysisandmetadataextractionfurtherenhancecomplaintunderstandingandroutingefficiency [5]. TheimplementationofthisAIdrivensystemisexpectedtosignificantlyimprovetheaccuracy,speed,andtransparencyofcomplaintresolution.Itwillalsoreducemanualworkload,enableproactivemaintenance,andenhanceusersatisfaction through intelligent automation. Overall, this project aims to revolutionize the traditional Rail Madad complaint-handling process, transforming it into a smart, scalable, and data-driven grievance management system for the future of railway services.

Introduction

The paper proposes an AI-powered complaint management system for the Indian Railways Rail Madad platform to overcome delays and inefficiencies caused by manual complaint handling. The current system struggles with slow routing, human error, and inability to process multimedia complaints (images, audio, video).

The proposed solution introduces a multimodal AI framework that automatically processes and classifies complaints submitted as text, images, audio, or video. It integrates multiple AI techniques:

• Text processing: SetFit-based embedding model for complaint classification
• Image processing: CLIP-based zero-shot classification using vision transformers
• Audio processing: Faster-Whisper speech-to-text followed by NLP analysis
• Video processing: Frame sampling with CLIP-based classification and severity aggregation
• Sentiment + urgency detection: RoBERTa-based sentiment analysis combined with rule-based urgency scoring

The system follows a multi-layer architecture including input collection, preprocessing, ML-based analysis, intelligent routing, resolution tracking, and dashboard visualization. Complaints are automatically categorized, prioritized (Low to Very High), and routed to the correct railway department.

Results show that the system improves classification accuracy, response speed, and operational efficiency, while significantly reducing manual workload. Multimodal AI enables faster detection of safety-critical issues and better prioritization of urgent complaints.

Conclusion

ThisprojectsuccessfullyestablishesaproposedAI-basedcomplaintclassificationandresolutionframeworkforRailMadad represents a significant step toward modernizing railway grievance management. By automating complaint categorization, prioritization, and routing,thesystemreducesprocessing time,improvesaccuracy, and enhances the passenger experience. Furthermore, predictive maintenance and sentiment analysis capabilities ensure continuous system improvement and proactive problem solving. The integration ofAI technologies within the existing Rail Madad infrastructure will lead to a more responsive, efficient, and data-driven complaint management ecosystem strengthening trust and satisfaction among passengers while supporting railway authorities in achieving operational excellence.

References

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Copyright

Copyright © 2026 Jatin Mishra, Adarsh Pandey, Priyanshu Tripathi, Saniket Kudoo. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.