Transportation forecasting is the crucial process of estimating future usage for various transport facilities, such as roads, railways, and airports, essential for effective urban planning and infrastructure development. It begins by collecting current traffic and demographic data to build demand models, which then predict future traffic, vital for determining infrastructure capacity, assessing project financial and social viability, and evaluating environmental impacts.

Historically, the sequential "four-step model" emerged in the 1950s, notably at the Detroit Metropolitan Area Traffic Study, as the traditional approach. This model systematically analyzes trip generation (where trips start/end), trip distribution (matching origins to destinations), mode choice (how people travel), and route assignment (which specific path is taken) to understand future travel patterns. Today, advancements leverage dynamic and big data, enabling new algorithms that significantly enhance the predictability and accuracy of these critical future estimations.