Chapter 3: Overview of Computer Simulation Modeling Approaches and Methods Robert E. Manning Robert M. Itami David N. Cole Randy Gimblett Capturing Behavior of Modeling Approaches the System _____________________ and Software ___________________ The field of simulation modeling has grown greatly Simulation modeling software is needed to process with recent advances in computer hardware and soft- input variables, generate data analyses, and produce ware. Much of this work has involved large scientific output. Three approaches to modeling and simulation and industrial applications for which substantial fi- of relevance to recreation travel simulations are trace, nancial resources are available. However, advances in probabilistic, and rule-based agent models. object-oriented programming and simulation method- ology, concurrent with dramatic increases in com- Trace, Probabilistic, and Rule-Based puter capabilities and reductions in computer hard- Agent Simulations ware costs, have meant that the benefits of simulation can be extended to areas that previously have been Trace simulations directly simulate travel itinerar- impractical. This includes recreation management. ies collected in the field. Visitor arrival, trip itineraries, The challenge of simulation modeling is to capture and duration of stay at destinations are simulated the essential behavior of the system being modeled. In directly from survey data rather than using probabil- outdoor recreation, this means capturing and repre- ity distributions or random numbers. These simula- senting the characteristics of the physical environ- tions are useful for examining existing pattern of use, ment (for example, a system of trails, roads, water- and are often used to validate probabilistic and rule- ways, and/or facilities) and modeling the behavior of based simulation models that are derived from the visitors as they interact with the environment and same data. Probabilistic simulation models are based with each other. In the most basic sense, models have on a representative sample of visitor trip itineraries. three components: (1) input variables that describe Visitors’ trips are then modeled based on the probabil- the system being modeled, (2) software and associated ity of a visitor selecting a single trip itinerary out of the modeling approaches designed to process these input entire sample, or alternatively, the probability of se- variables, and (3) output variables that are useful to lecting the next destination based on the probability planners, managers, and scientists. This chapter out- distribution of all destinations originating from the lines these components for recent modeling efforts in current destination. Probability models are the stan- park and wilderness management. Chapter 4 provides dard method for modeling baseline conditions. Prob- a series of case studies designed to illustrate these ability distributions for either trip itineraries or origin- basic components of simulation modeling and demon- destination pairs are a convenient way to “ramp up” strate their potential usefulness. numbers of visitors (increase visitor use levels) in a simulation, since a standard assumption is that as the USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 11 number of visitors increase, the distribution of trip full operating level (often at its maximum or peak). itineraries will remain the same. This type of situation might be modeled using a steady- Probabilistic simulation assumes that the distribu- state simulation. A simulation is called steady-state tion of trip itineraries in the future will remain similar because the simulation, after an initial “warm up” to the distribution today, regardless of how the system period, is designed to replicate system behavior over changes. This may be an inappropriate assumption for the long run at a given level of production or capacity. a system that is changing dramatically. Consequently, It is currently unclear whether it is more appropri- probabilistic simulation may not be an appropriate ate to model multiday backpacking trips using termi- way to model behavior in new recreation settings or in nating or steady-state simulations (see for example existing settings where management policies may the John Muir Wilderness case study in Chapter 4). introduce new travel networks, delete existing travel This situation has some characteristics that seem best networks, or where behavior may change due to handled with steady-state simulations and some that changes in recreation mode or mix of recreation seem best handled with terminating simulations. Re- types. For these situations, rule-based simulation gardless of appropriateness, steady-state simulations may be more appropriate (Itami and others 2004). are more challenging to conduct and analyze. They Rule-based simulations use autonomous agents. The must be run over long periods to get a reliable average agents are autonomous because once they are pro- measure of system behavior that is not biased by grammed, they can move about their environment, short-term effects of random variables and auto-corre- gathering information and using it to make decisions lation. The results of steady-state simulations must be and alter their behavior according to specific environ- carefully interpreted since they can overestimate pa- mental circumstances generated by the simulation. rameters if the actual duration of steady-state condi- Each individual agent has its own physical mobility, tions in the field is relatively short in comparison to sensory, and cognitive capabilities. Because autono- the run-lengths required to get valid simulation re- mous agents have their own reasoning system for sults (Law and Kelton 2000). Currently, we do not navigating a travel network, the travel network must have a good understanding of how to use steady-state be attributed with properties to which the agents simulations in recreational contexts. respond. These attributes may be in the form of attrac- tions such as scenic views, interpretive centers, picnic General Purpose Simulation Software areas, or playfields, and detractors such as hazardous and Special Purpose Simulators areas, extreme weather events, or other environmen- tal factors that would constrain movement or cause Commercially available general purpose simula- visitors to avoid an area. It is these attributes and the tion software packages are usually developed with attributes of other agents that determine agent rules. business, industry, and government applications in mind. However, it is possible to use this general Terminating and Steady-State Simulations software to model outdoor recreation behavior. For example, several of the case studies described in the A second important choice in simulation modeling next chapter have adapted the simulation software, approaches is whether to design simulations to be Extend, developed by Imagine That, Inc., to recre- terminating or nonterminating (steady-state). Termi- ational applications. Special purpose simulators, nating simulations model events that have a specified however, are developed specifically to handle special- length, while a steady-state simulation models situa- ized applications. Several of the case studies described tions in which there is no natural event to specify the in the next chapter have used RBSim, developed by length of a simulation run (Law and Kelton 2000). The GeoDimensions Pty Ltd. This is a special-purpose choice between these two should be made on the basis simulator designed to build simulations of recreation of the situation being modeled and the desired model- behavior on linear networks. Special-purpose simu- ing outputs. A terminating simulation has a known lators will have more automated features specific to initial state (usually zero) and a known ending state. the application of concern. General-purpose simula- For day use issues, it is clearly appropriate to use tion software can also be modified to include auto- terminating simulations to describe what happens mated features specific to the application of concern over a given day, based on data representing the (modeling outdoor recreation behavior). typical arrival sequence for a day. When the situation of interest involves people on multiday trips, modeling individual days makes little Model Inputs ___________________ sense. Nor does it make sense to model the entire year Simulation models require several types of input or season of use. What we are usually interested in data that can be obtained from several sources. Prin- understanding is how the system operates when at its cipal types of input include data on the travel network, 12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 the environment, visitor characteristics, and, in some information about travel mode characteristics (for cases, decision rules. example, foot, car, bus, or horse), travel speed, and a trip itinerary. In all cases, data collected must be in Travel Network the form of a census or a representative sample. The sampling period must be appropriate to the needs of In all of the applications of simulation modeling to the simulation. For terminating simulations, the park and wilderness management to date, recreation sample should be over the complete day or other use is constrained to linear travel networks. The period of interest. For steady-state simulations of travel network may be represented by a road or trail peak use, sampling should be done during the peak system, river, or flight path. Travel networks are period of