  Public transit systems are a critical component of major metropolitan areas.
However, in the face of increasing demand, most of these systems are operating
close to capacity. Under normal operating conditions, station crowding and
boarding denial are becoming a major concern for transit agencies. As such, any
disruption in service will have even more severe consequences, affecting huge
number of passengers. Considering the aging infrastructure of many large
cities, such as New York and London, these disruptions are to be expected,
amplifying the need for better demand management and strategies to deal with
congested transit facilities. Opportunistic sensors such as smart cards (AFC),
automatic vehicle location systems (AVL), GPS, etc. provide a wealth of
information about system's performance and passengers' trip making patterns. We
develop a hybrid data/model-driven decision support system, using real-time
predictive models, to help transit operators manage and respond proactively to
disruptions and mitigate consequences in a timely fashion. These models include
station arrival and origin-destination predictions in real-time to help transit
agencies, and predictive information systems for assisting passengers' trip
making decisions.
