Online Chatbot-Based Ticketing System for Bus Transportation

The rapid advancement of artificial intelligence and conversational technologies has transformed digital services, enabling innovative applications in public transportation. This project presents the design and development of an Online Chatbot-Based Ticketing System for Bus Transportation, which leverages conversational AI to modernize ticket booking, route inquiry, and schedule management. The system is built using Python and Streamlit, incorporating both text and voice interactions for improved accessibility, particularly benefiting elderly and visually impaired users. Speech recognition and text-to-speech modules enable hands-free operation, while integration with generative AI models, such as ChatGPT and Google Generative AI, allows natural language query processing and intelligent responses. A CSV-based dataset is employed to simulate bus routes and schedules, with provisions for future upgrades to real-time APIs and secure payment gateways. The system also emphasizes inclusivity through multimodal interaction, a user-friendly design, and dynamic visualization of transport data. Experimental results demonstrate that the chatbot interface can significantly reduce user effort, streamline booking processes, and enhance the overall travel experience. This study highlights the potential of AI-driven virtual assistants in building scalable, accessible, and future-ready smart transport infrastructures.

Authors

Publication Details

Journal
Indian Journal of Computer Science and Technology
Published
2025-09-01
DOI
https://doi.org/10.59256/indjcst.20250403003
Primary Topic
Transportation and Mobility Innovations
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Online Chatbot-Based Ticketing System for Bus Transportation

Muhammad Rizwan, Mohanad Ahmed
Indian Journal of Computer Science and Technology
Transportation and Mobility Innovations
article

Online Chatbot-Based Ticketing System for Bus Transportation

Muhammad Rizwan, Mohanad Ahmed
article en

Abstract

The rapid advancement of artificial intelligence and conversational technologies has transformed digital services, enabling innovative applications in public transportation. This project presents the design and development of an Online Chatbot-Based Ticketing System for Bus Transportation, which leverages conversational AI to modernize ticket booking, route inquiry, and schedule management. The system is built using Python and Streamlit, incorporating both text and voice interactions for improved accessibility, particularly benefiting elderly and visually impaired users. Speech recognition and text-to-speech modules enable hands-free operation, while integration with generative AI models, such as ChatGPT and Google Generative AI, allows natural language query processing and intelligent responses. A CSV-based dataset is employed to simulate bus routes and schedules, with provisions for future upgrades to real-time APIs and secure payment gateways. The system also emphasizes inclusivity through multimodal interaction, a user-friendly design, and dynamic visualization of transport data. Experimental results demonstrate that the chatbot interface can significantly reduce user effort, streamline booking processes, and enhance the overall travel experience. This study highlights the potential of AI-driven virtual assistants in building scalable, accessible, and future-ready smart transport infrastructures.

Indian Journal of Computer Science and Technology
Openalex Percentile: Top 75%
Transportation and Mobility Innovations
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.