This work introduces and explores the potential of using intelligent agent based modeling and simulation as a tool for examining the complex interactions between recreators and the environment, and interactions between recreators as a means to improving the our understanding of the recreational use of wildland settings. In this research the concept of rule driven autonomous agents as surrogates for human visitors is introduced. Agents are designed to represent the actions of the individual recreators (hiking, mountain bike riding, and pink jeep tour outfitters). Behavioural rules are derived from visitor surveys and interviews conducted in Broken Arrow Canyon, Arizona. The autonomous agents can be seen to dynamically move over a GIS based model of the Broken Arrow landscape. Line-of-sight calculations determine whether an individual agent is able to 'see' other agents and are used as method to record 'actual' and 'perceived' encounters with other agents. Using agent location maps combined with the underlying GIS data the agents can be observed moving across the landscape, pausing, changing pace, lingering at a view-point etc. A discussion focuses on analysing the resulting behaviours found in these simulations and additionally to explore the influence of alternative trail alignments on recreator movement, congestion and crowding. Some potential future directions for this research are discussed
