Smart AI Bus Travel Planner: An Intelligent Crowd Prediction and Comfort-Aware Recommendation System for Inter-City Public Transportation
Abstract
Inter-city buses are an important mode of transportation for millions of daily commuters, especially in
developing countries. However, existing transit systems provide limited information regarding passenger density, seat
availability, and overall travel comfort in real time. This lack of intelligent decision support often forces commuters
to make uncertain travel decisions, resulting in overcrowding, longer waiting times, and uncomfortable travel
experiences. This paper proposes an AI-Powered mobile application called Smart AI Bus Travel Planner that assists
commuters by predicting passenger, crowd level, calculating comfort score, recommending suitable seats, and
generating digital tickets automatically. Which uses machine learning techniques to improve the overall inter-city bus
travel experience. By predicting the number of people on the bus, scoring how comfortable the ride is, recommending
comfortable seats, and automatically generating a digital ticket. The system takes the raw travel logs, route data, and
simulated occupancy data, and feeds it into a custom-built four-module AI pipeline. The system uses a routine
detection algorithm to identify recurring travel patterns and a Random Forest regression model to predict crowd levels
categorized as Green, Yellow, or Red. In addition, based on the bus type and the number of passengers, a custom
formula is developed to calculate comfort scores, and a seat recommendation algorithm is designed that matches the
seat preferences of passengers with the gender-based seating constraints. The application is developed using a stack
of React Native for the front-end, Node.js for the back end, and MongoDB for the data. The app offers these insights
as well as real-time GPS tracking, QR code ticketing and interactive maps. The system was evaluated using 5,200
simulated trip records collected from 12 inter-city routes. The comfort score showed a strong correlation with user
satisfaction (R2= 0.84). And the user acceptance in a pilot study with 45 users is 91%. The proposed system introduces
predictive intelligence into inter-city transportation by providing crowd forecasting, comfort recommendations, and
automated travel planning features.
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Copyright (c) 2026 Umm-e- Laila, Saima Batool, Saifullah Khan, Syed Saad Ali
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