Systems and methods are described for constructing predictive models, based on statistical machine learning, that can make forecasts about traffic flows and congestions, based on an abstraction of a traffic system into a set of random variables, including variables that represent the amount of time until there will be congestion at key troublespots and the time until congestions will resolve. Observational data includes traffic flows and dynamics, and other contextual data such as the time of day and day of week, holidays, school status, the timing and nature of major gatherings such as sporting events, weather reports, traffic incident reports, and construction and closure reports. The forecasting methods are used in alerting, the display graphical information about predictions about congestion on desktop on mobile devices, and in offline and real-time automated route recommendations and planning.