Demand for public transport has witnessed a steady growth over the last decade in many densely populated cities around the world. However, capacity has not always matched this increased demand. As such, passengers experience long waiting times and are denied boarding during the peak hours. Crowded platforms and the subsequent customer dissatisfaction and safety issues have become a serious concern. The COVID-19 pandemic has dramatically reduced passengers’ willingness to board crowded trains, causing a surge in demand for real-time crowding information. In this paper, we propose a real-time predictive decision support platform which addresses both, operations control and customer information needs. The system provides crowding predictions on trains and platforms, communicates this information to passengers, and takes into account their response to it. It is demonstrated through a case study that providing predictive information to passengers can potentially reduce denied boarding and lead to better utilization of train capacity.