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Innovations in Travel Demand Modeling, Volume 2: Papers (2008)

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Suggested Citation:"T57054 txt_004.pdf." National Academies of Sciences, Engineering, and Medicine. 2008. Innovations in Travel Demand Modeling, Volume 2: Papers. Washington, DC: The National Academies Press. doi: 10.17226/13678.
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• The solution point can have certain properties that allow theoretical extensions or reinterpretation of results, such as socially desirable allocations of resources in welfare economics analysis, pricing strategies and second- best approaches, non- Walresian or reduced assumptions, contestable markets, and the property that all used paths have the same minimum cost with trans- port networks in certain forms of equilibrium; • The solution point can have certain properties that make it relatively easy (or quick) to find (such as with the Frank–Wolfe algorithm); • The defined equilibrium state generally does not exist in reality for most systems of interest, except with more generalized and extended (perhaps sometimes even tortured) definitions of equilibrium, such as spatially or temporally dynamic equilibrium that may also give up something related to the benefits of the properties already listed, including the potential instability and nonuniqueness of equilibrium points; and • Failure to reach the defined equilibrium point within a sufficient tolerance can lead to difficulties when results are being interpreted, particularly when results are being compared for different input conditions, lead- ing to the potential for large calculation burdens such that iteration “recipes” are a poor compromise. For the process simulation approach, • It provides a more direct match with actual system mechanics; • It generally can draw on a wider range of under- standing and appreciation of the elements of behavior involved; • It does not require the definition of an equilibrium state or even rely on the concept of equilibrium; • It incorporates path dependencies that complicate understanding and evaluation; • It can display emergent aggregate behavior, leading to a greater appreciation of system dynamics; • It typically involves random elements in its calcula- tion processes (by using Monte Carlo techniques) with the implication that the calculated output values also have random components (sometimes called simulation error or microsimulation error) with distributions that vary with level of aggregation and often are not well understood; and • The calculation of expectations for outputs in gen- eral requires multiple simulation runs, leading to the potential for large calculation burdens. These two approaches have their proponents, and the debates that arise about the approaches’ relative merits can sometimes be heated. This is hardly surprising, as these two approaches arise from different viewpoints, and the strength and even the relevance of the advan- tages and disadvantages vary according to theoretical perspective and modeling context more generally. In essence, the equilibrium approach facilitates a more wide- ranging theoretical consideration of the cross- sectional tendencies of the system, whereas the process simulation approach allows a more empirical explo- ration of the actual dynamic behavior of the system. Two common misconceptions (among many potential ones) are that 1. The iterations used in a calculation process to find the equilibrium solution in some way mimic the real- world behavior of the system, which would be the case only by coincidence, and 2. The simulation error in some way mimics the vari- ation in system behavior even when the random elements involved in the calculation process reflect analyst uncer- tainty rather than variation in system behavior, which again would be the case only by coincidence. DEGREE OF AGGREGATE CONSTRAINT AND LEVEL OF DISAGGREGATION The equilibrium approach and the process simulation approach are two points (or perhaps regions) on a con- tinuum of the degree of aggregate constraint on the mod- eling system. At one end is a complete lack of any aggregate constraints or restrictions on the system, and at the other is a full set of such constraints. Specific model- ing approaches can be placed along this continuum, with the recognition of a range of levels of such constraints and even of types of equilibrium as different forms of such constraint. There is a similar continuum in relation to the level of representation of the individual behavioral agents in the system and the distributions of their interac- tions, from the explicit treatment of each agent as a unique object to the handling of aggregate quantities rep- resenting groups or flows of agents as specific entities. Specific modeling approaches can be placed jointly along these two continua in a two- dimensional plane. Figure 1 shows these placements for a selection of mod- eling approaches. Figure 1 also shows regions with aggregate behavior that is chaotic, emergent, or both. This representation is based on the recognition that chaotic behavior tends to arise when there are comparatively fewer agents— consistent with the idea that a larger number of individ- ual objects with a comparatively wide distribution of responses results in a dissipation of impact that dampens the system. It is also based on the recognition that emer- gent aggregate behavior arises in a meaningful sense only when there are enough individual agents to allow for the interactions among the agents to develop into something beyond what they explicitly specify. 4 INNOVATIONS IN TRAVEL DEMAND MODELING, VOLUME 2

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TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 2: Papers includes the papers that were presented at a May 21-23, 2006, conference that examined advances in travel demand modeling, explored the opportunities and the challenges associated with the implementation of advanced travel models, and reviewed the skills and training necessary to apply new modeling techniques. TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 1: Session Summaries is available online.

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