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The spatial and temporal distribution of built space supply plays an important role in shaping urban form and thus the general travel pattern in an urban area. Within an integrated framework, we are interested in modeling the decisions of a builder in terms of when, where, what type, and how much built space to build. We present a multidimensional discrete-continuous model formulation for the built space supply decisions that are based on expected profit maximization. The framework is applied to estimate a model for supply of new office space in the greater Toronto area (GTA) for the 1986 to 2006 period. To our knowledge, this work is the first that models the where, when, how much, and what type of office space to build in a single econometric framework at a fairly disaggregate spatial zoning system. The results indicate risk taker behavior on the builders' part, while market conditions and supply of resources (labor, construction cost, etc.) are also found to be important factors in decision making.

In the context of integrated transportation and other urban engineering infrastructure systems, there are many examples of markets, where consumers exhibit price-taking behavior. While this behavior is ubiquitous, the underlying mechanism can be captured in a single framework. Here, we present a microsimulation framework of a price-taker market that recognizes this generality and develop efficient algorithms for the associated market-clearing problem. By abstracting the problem as a specific graph theoretic problem (i.e., maximum weighted bipartite graph), we are first able to exploit algorithms that are developed in graph theory. We then explore their appropriateness in terms of large-scale integrated urban microsimulations. Based on this, we further develop a generic and efficient clearing algorithm that takes advantage of the features specific to urban price-taker markets. This clearing solution is then used to operationalize two price-taker markets, from two different contexts, within a microsimulation of urban systems. The initial validation of results against the observed data generally shows a close match.