Intelligent Bus Scheduling System Using IoT: Improving Efficiency, Safety, and Passenger Experience

Current public transportation systems facesignificant challenges, including inefficiencies inscheduling, overcrowding, and safety concerns.This paper introduces a novel, IoT-based busscheduling automation system designed to addressthese issues. By integrating GPS tracking, real-timeTicketing data captured by devices handled by busconductors, and Machine Learning to analyze andpredict crowd strength, our system dynamicallyadjusts bus schedules to match passenger demand.As new passengers board and tickets are issued, theticketing device sends real-time logs to a CentralServer. The server aggregates this data from allbuses across the city, continuously updating recordsof passenger counts, boarding locations, and travelpatterns. This collected data is then processed andanalyzed using machine learning models, whichidentify trends, predict demand fluctuations, andgenerate insights for optimizing bus schedules. Thesystem considers historical trends, external factorssuch as holidays and peak hours, and predefinedoperational constraints to recommend adjustmentsin bus allocation.This approach not only optimizes resourceallocation and reducing unnecessary fuelconsumption but also enhances safety byminimizing overcrowding, which can help preventpetty crimes and harassment, ensuring a safe space.With the potential to improve passenger experienceand trust in public transportation, this system offersa scalable solution, especially for urban mobilitychallenges. Future applications could includeintegration with smart city initiatives to promoteefficient transportation, routemanagement and safety.Keywords: IoT-based bus scheduling automationsystem, GPS Tracking, Ticketing Device, ResourceOptimization