Long-Term Simulator

The long-term (LT) simulator of SimMobility models the behaviors of agents in the housing market, and ultimately the commercial real estate market and the job market, in order to simulate the yearly and longer term impacts of alternative future mobility scenarios on residential and workplace locations; vehicle ownership; the density, land use distribution, and value of the built environment. Figure 3 shows the framework of the LT simulator. In general, the LT simulator is responsible for the generation and updating of a population of agents and their corresponding demographic and locational attributes. In the beginning, a two-stage data synthesis methodology is employed for construction of a synthetic population of households and firm establishments at building scale. The approach is designed to accommodate the need for spatially disaggregated details in a manner that can be readily adjusted and rerun to incorporate new data sources, changed time frames, and updated relationships and hierarchies across overlapping datasets. Long-term behaviors of agents and their effects on urban form, markets and other agents are implemented by a group of behavioral models that are connected in a sequential/event-based framework.
Figure . Framework of the Long-Term Simulator
These behavioral models take account of demographic and economic factors of agents, locational amenities and the regulatory variables translated from exogenously specified policies. The LT simulator centers on a real estate market module, which emulates the dynamic interaction process between demand and supply in the market. The market module include a series of models that simulate (a) ‘awakening’ of households who begin searching for new housing, (b) eligibility, affordability, and screening constraints, (c) daily housing market bidding, and (d) modeling developer behavior regarding when, where, what type, and how much built space to construct by taking into account market cycle and uncertainty. Changes in residential location then trigger a household’s re-assessment of private vehicle ownership and possible re-assignment of workers (students) to jobs (schools).
The long-term simulator is integrated with the MT simulator via built-in functions facilitating the exchanges of data that characterize the status-quo of land use and transportation performance. One set of functions computes accessibility measures for individuals considering alternative residential, work, or school locations, and alternative vehicle ownership conditions. These measures can be computed quickly since they vary the circumstances of only the one individual. Another set of functions allows the LT simulator to pass population (and firm) information with updated residential and job locations as well as vehicle ownership. This information is sent periodically so that the MT simulator can reassess overall activity patterns and accessibility conditions, and the LT simulator can then make choices based on adjusted expectations accessibility. Currently this exchange is done annually. Information on the performance of transportation services and activity-travel participation of agents is fed to the land use module of SimMobility (i.e. the LT simulator) through a utility-based, behaviorally rigorous accessibility measure, the logsum. It is the expected maximum utility of a person in a series of activity related choice situations.
 In SimMobility, the logsum measure reflects the range of choices in destinations and modes, the scarcity of time and money, and accounts for the heterogeneous preferences among agents. Therefore, it is a link between the MT simulator and the LT simulator which ensures the behavioral consistency of agents by encapsulating agents’ day-to-day activity and travel considerations into their long-term location and vehicle ownership choices. However, because the logsum measure is individual specific and not directly comparable across agents, it is first converted to cost (dollars) before being aggregated in the LT simulator to model household-level choices.