Transportation forecasting is the attempt of estimating the number of vehicles or people that will use a specific transportation facility in the future. For instance, a forecast may estimate the number of vehicles on a planned road or bridge, the ridership on a railway line, the number of passengers visiting an airport, or the number of ships calling on a seaport. Traffic forecasting begins with the collection of data on current traffic. This traffic data is combined with other known data, such as population, employment, trip rates, travel costs, etc., to develop a traffic demand model for the current situation. Feeding it with predicted data for population, employment, etc. results in estimates of future traffic, typically estimated for each segment of the transportation infrastructure in question, e.g., for each roadway segment or railway station.

Until now, establishing traffic forecasts required the development of a complex 4-step model (generation, distribution, choice, assignment). This engineering work was long, complex, required a lot of assumptions and was quickly out of date. With Tyms, we measure existing traffic in real time and then, using our machine learning tools, we create dynamic, constantly updated traffic forecasts. This method is easier, faster and more scalable. From our dynamic traffic forecasts, we can produce dynamic economic assessments as P&L of an operator by example.