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Demand for the public transit systems 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. Another development is the rapid pace of technological innovations. Smartphones have enabled companies to interact with their customers directly and seamlessly, providing personalized information in real-time. The prevalence of such applications has raised the expectation among passengers to receive a similar type of information about their expected trip conditions. This presents a unique opportunity for transit system operators. Real-time information can potentially influence passengers' trip making decisions. Some users might decide to shift their arrival time as to avoid overcrowded stations. Similarly, some passengers might decide to wait for a train after the one that is currently arriving at a station, if they are informed that this action will result in a better trip experience (such as less crowding on the train). This dissertation proposes and implements a predictive decision support platform that addresses both operations control and customer information needs. The capabilities of this platform can be used to foresee expected overcrowded platforms and trains. Real-time predictive control measures can be implemented to prevent such conditions from arising. Second, it generates information on the expected probability of being able to board upcoming trains, which can be communicated to passengers both inside the system and those about to travel. Using this information, passengers can make better informed decisions as to which train to board for maximizing their utility. The decision support comprises two major components: a demand prediction module, and an on-line simulation. The first module provides short-term prediction of the number of passengers arriving at each station, and their final destinations.