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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM63901

NLP AND ML BASED CHATBOT FOR RAILWAYS

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63901

Volume:

10 |

5 |

May 2026

Pages:

1-3

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

N

Nalina

Master's of Computer Applications

Bapuji Institute Of f Engineering And Technology

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Mentors

RR

Roopa R

Master's of Computer Applications

Bapuji Institute Of f Engineering And Technology

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Abstract

As one of the world’s largest rail networks, Indian Railways handles an incredible volume of daily inquiries regarding bookings, schedules, and live updates. However, relying on traditional phone lines and static websites often results in long wait times and a frustrating experience for travelers. To bridge this gap, we are proposing an **intelligent, AI-powered Chatbot** designed to transform how passengers interact with the railway system. By using Natural Language Processing (NLP) and Machine Learning (ML), this virtual assistant can understand conversational language and provide instant, personalized help.

KEYWORDS

NLP And ML

Cite This Article

Nalina, Roopa R, "NLP AND ML BASED CHATBOT FOR RAILWAYS," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-3, May 2026.

Full Article/Conclusion

This project successfully designed, implemented, and tested a robust, intelligent Chatbot system for the Indian Railways leveraging modern NLP and ML technologies. The core objective of improving passenger service efficiency and quality was achieved, as evidenced by the high self-service rate and rapid response times. By moving away from rigid, rule-based systems, the proposed solution demonstrated superior ability to understand and process natural human language, offering a scalable and personalized conversational experience. The system's modular architecture, encompassing a state-of-the-art ML pipeline for intent and entity recognition, proved highly reliable in the production environment

References

Bender, E. M., & Koller, A. (2017). Climbing Mount Pnini: Characterizing the production of referential descriptions in generative models. Proceedings of the 15th European Workshop on Natural Language Generation. F. Chollet (2017). Python deep learning. A Manning Publication.  J.Devlin,M.W.Chang,K. Lee, & K. Toutanova (2019). BERT: Deep Bidirectional Transformer Language Understanding Pre-training. NAACL 2019 Conference Proceedings. Gunning, D. (2019). XAI: Explainable AI. Defense Advanced Research Projects Agency.  

Editorial & Review Transparency

Publication Date

5/25/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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3

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5

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Conflict of Interest

The authors declare no conflict of interest related to this research work.

Funding Information

No funding information available for this research.

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