AI-Based College Transportation Management

Colleges that run their own buses usually plan transport using fixed routes and estimated student counts. In practice, the number of students who actually travel changes from day to day, so some buses run with many empty seats while others are overcrowded. This paper presents the design of the AI-Based College Transportation Decision Support System, a full-stack web application that collects daily travel confirmations from students and uses rule-based software agents to analyse route-wise demand, check route compatibility, recommend bus allocation, and suggest seat optimization within capacity limits. Every recommendation is accompanied by a plain-language explanation, and no change becomes final until the administrator approves it. The previous allocation stays in force until a replacement plan is approved. The system is built with React.js, Node.js, Express.js and MongoDB, with JWT-based authentication and role-based access control. It uses predefined rules and calculations only; it does not use machine learning, GPS tracking, or external generative AI services. This paper describes the problem, the system architecture, the database design, the rule-based algorithms, and an illustrative worked example. Experimental evaluation on real college data is identified as future work.