Transportation Systems Planning
As discussed in the introduction of this handbook, one can identify a mainstream approach to transportation planning and a second that is richer and more concerned with modern issues. This new emergent viewpoint in approaching transportation problems recognizes the presence of complexities, nonlinearities, and uncertainties that were neglected in the past for the sake of simplicity. The nature of policies to be assessed and the realization that interdependent systems need to be studied and modeled in their totality motivates building decision support systems that are increasingly expanded to incorporate processes and ideas from other related fields. For example, the wider acceptance of discrete choice models, which consider the person as a decision unit, motivates the need to provide data about persons. These can be data on demographics (age, gender), economics (employment, income), and social situations and roles (e.g., household type, indicators of the role in the household). Production of these data to be used in forecasting future choices requires one to employ demographic evolutionary methods that produce this information in future years for which an assessment of policy impacts is made. Many more examples, a few of which are included in this handbook, show that we are experiencing an “immigration” of disparate methods from other fields into transportation systems planning. In this way, the resulting model systems
are very often the result of a somewhat haphazard amalgamation of methods that have been designed at different levels of scale (person, community, city), based on different behavioral assumptions (e.g., optimizing, satisficing, adaptive, or opportunistic behavior), and estimated with data from different periods or horizons (e.g., a typical day, a given year defined generically, a census decade, and so forth). For these reasons, different models may not be entirely consistent and interoperable, and their predictions are surrounded by large error bands that provide information that is sometimes sufficient for some type of decision making and other times totally inadequate for any analysis. Many of these models, however, share the same motivation and their ultimate aim is to solve specific
. In this chapter overviews of these problems and of the most recent issues in designing transportation system planning models to solve the problems are provided.