Real-Time Capacity Monitoring and Passenger Forecasting P. Venkateswarlu Reddy, Venkata Reddy Medikonda, M. Nagabhaskar, D. Ganesh, Vankudoth Ramesh, Devalla Manogna 2025 IEEE 1st International Conference on Innovations in Engineering and Next Generation Technologies for Sustainability Icinvents 2025, 2025 The real-time knowledge of passenger occupancy is also essential in modern urban transportation systems, so that the commuter experience can be improved and resource allocation can be optimized. The paper presents a proposed smart capacity management tool in the example of the bus capacity management in the world of openly accessible data provided by the trim (ticketing) machines and additional sensors which will be used to estimate the seated and standing occupancy rate dynamically. The system continually optimizes the latest status of capacity by analyzing events of boarding and deboarding at each station and forecasts seating capacity and standing spaces at the next stations. The solution uses historical trends in the flow of passengers and machine learningbased forecasting to predict the load of the passengers on board by the time//they reach the next stations and can give proactive information to the passengers on board and at the bus stops in terms of availability of seats. The prospective solution will merge with the current system of ticketing and enable real-time updates on onboard screens, as well as mobile and passenger information systems. The initial outcomes are the increased level of awareness of passengers, shortened boarding time, and comfort provided by stopping the overcrowding. The method can be referred to as a scalable and efficient approach towards the intelligent governance of public transportation in smart cities.
Prediction of big mart sales using exploratory machine learning techniques International Journal of Advanced Science and Technology, 2020
