With an increasing number of intelligent analytic techniques and increasing networking capabilities, municipal transit authorities can leverage real-time data to estimate transit volume and predict crowding conditions. We introduce a proactive Transit Demand Estimation and Prediction System (TraDEPS) – an approach that has the potential to prevent crowding and improve transit service, by measuring the transit activity (the number of passengers on the individual modes of public transportation and the demand on a route), and estimating crowding levels at a given time. This system utilizes a combination of real-time data streams from multiple sources, a predictive model and data analytics for transit management. The problem of transit crowding is translated into transit activity prediction, as the latter is a straightforward indicator of the former. This thesis delivers the following contributions: (1) A crowding prediction model. (2) A system supporting the methodology. (3) A feature which displays different crowding level conditions of a route on a web map
