it is difficult to consider all these choices in one single model, although an integrated model is the final goal. Also, even with today's computing power, an integrated model will inevitably require some strict assumptions that will reduce its application value to local specific problems. The classical way to forecast the results of such a complex choice process is to divide it into simpler subprocesses in a logical and tractable way. Models for these subprocesses are then developed individually, and the hope is that they can eventually be assembled to provide useful predictions for decision makers. The past half-century has witnessed several different methods of disentangling the complex travel decision-making process. Two major approaches have emerged over time: trip-and activity-based approaches. The traditional four-step travel forecasting models are often referred to as trip-based approaches in that they treat individual trips as the elementary subjects. In so doing, the four-step model tends to ignore the diversity among different individuals and considers aggregate travel choices in four steps-trip generation, trip distribution, mode split, and route assignment. Other choices are either treated as exogenous (e.g., land use and automobile ownership) or extremely simplified (e.g., trip scheduling). An up-to-date summary of the achievements in this field can be found in the book by Ortuzar and Willumsen (7 ). There is some disagreement about how to assemble these four subprocesses in travel forecasting. Some researchers are of the opinion that the four steps should be solved in a coherent network equilibrium instead of sequentially. Boyce (8) provides a thorough review of the origin and the recent development of that issue. An important nature of travel demand ignored by trip-based approaches is that travel is a derived demand-travel is desired to participate in other activities, not for its own consumption value. In view of this and other inadequacies of the four-step model, activity analysis has been applied to travel demand analysis since the 1970s. Activitybased approaches describe which activities people pursue, where, when, and for how long given fixed land use, transportation supply, and individual characteristics. A trip is generated to connect two spatially separated sequential activities. In activity-based approaches, every individual is a decision maker who confronts a huge choice set of various activity patterns in the time-space domain. Each combination of activities and their locations, starting points, and durations forms a unique activity pattern. Individuals select (or at least intend to select) the patterns that maximize their utilities by somehow solving a large-scale combinatorial optimization problem conditional on others' decisions. Different from the trip-based models, activity-based approaches deem individuals' decision making as subprocesses of the emergence of travel demand. These subprocesses are typically assembled by microscopic travel simulation to form aggregate travel forecasting. At the current stage of activity-based approaches, route choice and sometimes mode choice are still modeled by external modules Contemporary models of urban passenger travel demand date from the 1950s (1, 2) . Aggregate demand models that relate the consumption of goods to the attributes of the goods, the competing goods, and consumer characteristics were found inappropriate for travel demand modeling because of both their inability to test some important transportation-related policies and the complexity of the transportation system itself. Therefore, a disaggregate or behavioral approach has attracted most of the research interest in the past several decades. Disaggregate travel demand models directly assume the behaviors of real-world decision-making units such as an individual or household. Discrete choice analysis based on random utility theory has been widely adopted, and individuals are assumed to always select the alternative that maximizes their utilities (3). Agent-Based Approach to Travel Demand Modeling Urban travel demand results from a multidimensional hierarchical choice process. A list of such choices includes residential and business location, automobile ownership, and when to make a trip, with whom, from where to where, by which mode, and by which route. Some studies suggest that travelers, by developing heuristics, may only be able to find a feasible, not necessarily global, optimal solution to the choice problem subject to a set of constraints (4-6). However,
Zhang and Levinson REF applied the method to form an agent-based travel demand model for predicting macroscopic travel patterns from microscopic agent travel behaviours.
An agent-based approach to travel demand modeling: An exploratory analysis
In Transportation Research Record: Journal of the Transportation Research Board No.1898, 2004. Athanasios Ziliaskopoulos. Optimum Path Algorithms on Multidimensional Networks: Analysis, Design, Implementation and Computational Experience
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