Transportation engineering and transportation planning are two sides of the same coin aiming at the design of an efficient infrastructure and service to meet our needs for accessibility and mobility. Many well-designed transport systems that meet these needs are based on good understanding of human behavior. For example, in ergonomics — the science for designing tools for humans — models of action-reaction are used to make sure that machines (e.g., cars) are designed to match human characteristics. In a simila|r way, when we plan cities and the services provided to their inhabitants, understanding human behavior is a key ingredient that drives most decisions in planning and operations. Since transportation systems are the backbone connecting the vital parts of a city, in-depth understanding of human nature is essential to the planning, design, and operational analysis of transportation systems.
Analytical methods that help us understand and predict human (travel) behavior have only partially been developed in the past few decades. In this area, basic and applied research has been very active in quantitative methods since the 1970s, with one notable development: the disaggregate demand models. However, it is only recently that we experienced the benefits in practice from these methods. Unavoidably, assimilation of research findings into decision making for public policy is very slow. Analyses and explanations for this, but not justifications, abound.