Modelling perception updating of travel times
To build a pedagogically appropriate model, this chapter introduces an Agent-based Demand and Assignment Model (ADAM), extending Zhang and Levinson (2004), which addresses the destination choice and route choice problems with consideration of congestion.
Students have the opportunity to work with the ADAM model for several exercises.
The traditional four-step travel demand model, often referred to as the trip-based approach, takes individual trips as the elementary subjects and considers aggregate travel choices in four steps: trip generation, trip distribution, modal split, and route assignment.
This sequential travel demand modeling paradigm, which originated in the 1950s when limited data, computational power, and algorithms were available, ignores the diversity across individuals and does not have solid foundation in travel behavior theory.
The process will repeat until either all travelers have found jobs (chosen a destination) or some maximum number of iterations are reached.
The key components of the agent-based model are introduced in turn below.
This is in part because it is also more realistic, in that it can be formulated to capture the process by which travelers make decisions, and because it is tracking individuals, can be internally consistent (so that a given traveler has a particular set of constraints (like income, obligations, and time available) There are several elements in an agent-based model: An agent-based model evolves by itself once those micro-level elements are specified.
Historically, agent-based models come from different fields such as genetics, artificial intelligence, cognitive science, social science.The advantage of using them in transportation begins first with the intuition they provide.It makes more sense to people to think of individual travelers behaving rather than flows.To overcome these inadequacies of conventional four-step modeling, activity-based models have been applied in travel demand analysis since the 1970s.Activity-based models predict activities and related travel choices by considering time and space constraints as well as individual characteristics.