Abstract :

The Rapid Growth Of Railway Transportation Systems Has Significantly Increased The Volume Of Passenger Interactions, Including Inquiries Related To Ticket Booking, Train Schedules, Platform Information, Delays, Refunds, Seat Availability, And Onboard Services. Traditional Customer Assistance Mechanisms Such As Manual Helpdesks, Call Centers, And Static FAQ-based Chat Systems Are Increasingly Inadequate Due To High Passenger Traffic, Multilingual Diversity, And The Demand For Real-time Responses. This Research Presents A Train Customer Assistance System Powered By Generative Artificial Intelligence Using Natural Language Processing Techniques To Provide Intelligent, Interactive, And Context-aware Support To Railway Passengers. The Proposed System Leverages Advanced NLP Algorithms And Transformer-based Generative Models To Understand User Intent, Process Natural Language Queries, And Generate Accurate, Human-like Responses In Real Time. Unlike Conventional Rule-based Systems, The Proposed Solution Adapts Dynamically To Varied Passenger Queries, Supports Multiple Languages, And Continuously Improves Through Learning From Interactions. The System Integrates Structured Railway Databases With Unstructured Conversational Data To Ensure Factual Accuracy While Maintaining Conversational Fluency. This Research Aims To Improve Passenger Satisfaction, Reduce Operational Workload, And Enhance The Overall Efficiency Of Railway Customer Service Operations. By Automating Customer Assistance Using Generative AI, The Proposed Model Demonstrates How Intelligent Conversational Agents Can Transform Public Transportation Services Into More Accessible, Responsive, And User-centric Systems.

Published:

01-5-2026

Issue:

Vol. 26 No. 5 (2026)

Page Nos:

18-22

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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