United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical

Report RMRS-GTR-143

September 2005

Computer Simulation

Modeling of Recreation

Use: Current Status, Case

Studies, and Future

Directions

David N. Cole, Compiler
Contributors _____________________________________________

Suzanne Cable is Wilderness Ranger, USDA Forest Service, Salmon-Challis National Forest, North Fork

Ranger District, North Fork, ID.

Kerri Cahill is Planner, National Park Service, Denver Service Center, Denver, CO.

David N. Cole is Research Biologist, USDA Forest Service, Rocky Mountain Research Station, Aldo

Leopold Wilderness Research Institute, Missoula, MT.

Randy Gimblett is Professor, School of Renewable Natural Resources, University of Arizona, Tucson, AZ.

Jeffrey C. Hallo is Graduate Research Assistant, Rubenstein School of Environment and Natural

Resources, University of Vermont, Burlington, VT.

Marilyn Hof is Planner (retired), USDI National Park Service, Denver Service Center, Denver, CO.

Robert M. Itami is Director of GeoDimensions Pty Ltd, Sorrento, Victoria, Australia.

Ann Mayo Kiely is Resource Management Specialist, Isle Royal National Park, Houghton, MI.

Steven R. Lawson is Assistant Professor, Department of Forestry, Virginia Polytechnic Institute and State

University, Blacksburg, VA.

Robert E. Manning is Professor, Rubenstein School of Environment and Natural Resources, University

of Vermont, Burlington, VT.

Peter Newman is Assistant Professor, Department of Natural Resource Recreation and Tourism,

Colorado State University, Fort Collins, CO.

William A. Valliere is Research Specialist, Rubenstein School of Environment and Natural Resources,

University of Vermont, Burlington, VT.

Mark E. Vande Kamp is Research Associate, Protected Area Social Research Unit, College of Forest

Resources, University of Washington, Seattle, WA.

Jan W. van Wagtendonk is Research Forester, U.S. Geological Survey, Yosemite Field Station, El Portal, CA.

Benjamin Wang is Graduate Research Assistant, Rubenstein School of Environment and Natural

Resources, University of Vermont, Burlington, VT.

Abstract _________________________________________________

Cole, David N., comp. 2005. Computer simulation modeling of recreation use: current status, case

studies, and future directions. Gen. Tech. Rep. RMRS-GTR-143. Fort Collins, CO: U.S. Department

of Agriculture, Forest Service, Rocky Mountain Research Station. 75 p.

This report compiles information about recent progress in the application of computer simulation

modeling to planning and management of recreation use, particularly in parks and wilderness. Early

modeling efforts are described in a chapter that provides an historical perspective. Another chapter

provides an overview of modeling options, common data input requirements, and useful model outputs.

The bulk of the report consists of case studies that illustrate a broad array of recreational situations and

management applications for simulation modeling. A final chapter describes some future directions for

modeling work. Although simulation of recreation use is already a tool for planning and management, its

utility could be greatly enhanced with further work in software development, increased understanding of

appropriate methodologies, and greater attention to model verification and validation.

___________________

Keywords: computer simulation, park planning, recreation management, simulation modeling

Cover captions: (1) Use levels of campsite clusters in a portion of the John Muir Wilderness, based on

output from a computer simulation model. (2) Computer-edited photograph of hikers on the trail to

Yosemite Falls, used to obtain visitor evaluations of hiker density. (3) Schematic diagram of the top layer

of a simulation model developed for Glacier Point, Yosemite National Park.
Preface

This General Technical Report was made possible by a generous grant from the Washington Office

of the U.S. Department of the Interior, National Park Service. The purpose of the grant was to further

development of computer simulation modeling of recreation use as a tool for park and wilderness

management. The grant was used to bring together people who had worked or were working on

simulation of recreation use in two workshops: to support a collaborative project in the John Muir

Wilderness, California, and to support data gathering related to development of a General Management Plan for Saguaro National Monument, Arizona (work that is not reported here).

The first workshop was held in Tucson, AZ, March 19 to 21, 2003, to examine the utility of visitor

simulation modeling to park planning and management. The School of Renewable Natural Resources at the University of Arizona, and the Aldo Leopold Wilderness Research Institute hosted the

workshop, which was attended by a selected group of academics and agency personnel from the

USDA Forest Service, USDI Bureau of Land Management, National Park Service, U.S. Geological

Survey, and Parks Canada. Major topics discussed at the workshop included the historical context

for simulation modeling; two current modeling approaches—Extend and RBSim; other simulation

modeling approaches; data collection needs and issues; and identification of possible applications

for planning and management. A second workshop was held in September 2003 at the National Park

Service’s Denver Service Center to inform Park Service planners about the opportunities and

benefits of using visitor monitoring and simulation in the development and evaluation of general

management plans. This 1-day workshop was attended by approximately 40 planners and designers.

The collaborative simulation project used the two most prominent modeling approaches (Extend and

RBSim) to analyze a single dataset developed for Humphrey’s Basin in the John Muir Wilderness. It

received funding from the Rocky Mountain Cooperative Ecosystem Studies Unit and the Aldo Leopold

Wilderness Research Institute, as well as the National Park Service. This effort was originally

conceived as a comparison of the two approaches. Rapidly it became apparent that even more value

could be derived from lessons learned through collaboration between the two efforts and approaches.

Definitional issues surfaced and were resolved. Unforeseen problems surfaced and were dealt with.

Some of the most prominent lessons learned dealt with appropriate run lengths for steady-state

simulations, development of output statistics, and validation statistics.

After the first workshop, the group decided that a General Technical Report would be the best way

to communicate progress to date on computer simulation modeling of recreation use. Case studies of

recent modeling efforts were compiled, and chapters were written that provide an overview of how

modeling works, its historical development, and issues for the future. Funding for writing and for the

work presented in the case studies was provided by the Aldo Leopold Wilderness Research Institute,

Pacific Northwest Cooperative Ecosystem Studies Unit, the U.S. Geological Survey, University of

Arizona, University of Vermont, and several National Park Service and Forest Service units. Publication costs were provided by the Aldo Leopold Wilderness Research Institute, University of Arizona, and

University of Vermont.

David N. Cole
The use of trade or firm names in this publication is for reader information and does not

imply endorsement by the U.S. Department of Agriculture of any product or service

Pesticide Precautionary Statement

This publication reports research involving pesticides. It does not

contain recommendations for their use, nor does it imply that the uses

discussed here have been registered. All uses of pesticides must be

registered by appropriate State and/or Federal agencies before they can

be recommended.

CAUTION: Pesticides can be injurious to humans, domestic animals,

desirable plants, and fish or other wildlife—if they are not handled or

applied properly. Use all pesticides selectively and carefully. Follow

recommended practices for the disposal of surplus pesticides and

pesticide containers.

CAUTION:

PESTICIDES
Contents

Chapter 1: Why Model Recreation Use?

David N. Cole, Kerri Cahill, and Marilyn Hof ............................................................................................... 1

Planners Need Better Data ............................................................................................................................ 1

What Is Simulation Modeling and What Can It be Used For? ........................................................................ 1

Past and Present Simulation Modeling Efforts ............................................................................................... 2

Overview of Report ........................................................................................................................................ 2

References ..................................................................................................................................................... 2

Chapter 2: Historical Development of Simulation Models

of Recreation Use

Jan W. van Wagtendonk and David N. Cole .............................................................................................. 3

Wilderness Use Simulation Model ................................................................................................................. 3

Desolation Wilderness Application ............................................................................................................. 4

Yosemite National Park Application ............................................................................................................ 5

Applications on Rivers ................................................................................................................................ 6

Application on the Appalachian Trail .......................................................................................................... 7

Beyond the Wilderness Use Simulation Model .............................................................................................. 8

Conclusion ..................................................................................................................................................... 8

References ..................................................................................................................................................... 9

Chapter 3: Overview of Computer Simulation Modeling

Approaches and Methods

Robert E. Manning, Robert M. Itami, David N. Cole, and Randy Gimblett ............................................... 11

Capturing Behavior of the System ............................................................................................................... 11

Modeling Approaches and Software ............................................................................................................ 11

Trace, Probabilistic, and Rule-Based Agent Simulations ......................................................................... 11

Terminating and Steady-State Simulations .............................................................................................. 12

General Purpose Simulation Software and Special Purpose Simulators ................................................. 12

Model Inputs................................................................................................................................................. 12

Travel Network .......................................................................................................................................... 13

Environmental Data .................................................................................................................................. 13

Visitor Characteristics ............................................................................................................................... 13

Decision Rules .......................................................................................................................................... 13

Model Outputs .............................................................................................................................................. 14

Model Validation........................................................................................................................................... 14

Using the Simulator ...................................................................................................................................... 15

References ................................................................................................................................................... 15

Computer Simulation Modeling of

Recreation Use: Current Status,

Case Studies, and Future

Directions

David N. Cole

iii
Chapter 4: Case Studies of Simulation Models of Recreation Use

David N. Cole ............................................................................................................................................ 17

Recreation Visitation and Impacts in the Bighorn Crags Portion of the

Frank Church—River of No Return Wilderness

Randy Gimblett, Suzanne Cable, David N. Cole, and Robert M. Itami ..................................................... 18

Recreation Visitation in Misty Fjords National Monument in the Tongass National Forest

Randy Gimblett, Robert M. Itami, and Suzanne Cable ............................................................................. 22

Simulation of Recreation Use Along the Colorado River in Grand Canyon National Park

Randy Gimblett ......................................................................................................................................... 27

John Muir Wilderness: Describing the Spatial Distribution of Backpacking Use

on a System of Trails and Campsites

Steven R. Lawson, Robert M. Itami, Randy Gimblett, and Robert E. Manning ........................................ 31

Frontcountry Trails and Attraction Sites in Yosemite National Park: Estimating

the Maximum Use That Can Be Accommodated Without Violating Standards

of Quality

Robert E. Manning, William A. Valliere, Benjamin Wang, Steven R. Lawson,

and Peter Newman ................................................................................................................................... 36

Alcatraz Island: Estimating the Maximum Use That Can Be Accommodated

Without Violating Standards of Quality

William A. Valliere, Robert E. Manning, and Benjamin Wang .................................................................. 39

Arches National Park: Describing Visitor Use Patterns and Predicting the Effects

of Alternative Transportation Systems

Steven R. Lawson, Robert E. Manning, William A. Valliere, and Benjamin Wang ................................... 42

Isle Royale National Park: Estimating the Effectiveness of Alternatives for Managing

Crowding at Wilderness Campsites

Steven R. Lawson, Robert E. Manning, and Ann Mayo Kiely ................................................................... 47

Acadia National Park Carriage Roads: Estimating the Effect of Increasing Use

on Crowding-Related Variables

Robert E. Manning and Benjamin Wang .................................................................................................. 50

Acadia National Park Scenic Roads: Estimating the Relationship Between

Increasing Use and Potential Standards of Quality

Jeffrey C. Hallo, Robert E. Manning, and William A. Valliere ................................................................... 55

Port Campbell National Park, Australia: Predicting the Effects of Changes in

Park Infrastructure and Increasing Use

Robert M. Itami ......................................................................................................................................... 57

Mount Rainier National Park: Collecting Data to Model Visitor Use on a

Complex Frontcountry Trail System

Mark E. Vande Kamp ................................................................................................................................ 64

Chapter 5: Future Directions for Simulation of Recreation Use

David N. Cole ............................................................................................................................................ 71

Introduction .................................................................................................................................................. 71

Software Development ................................................................................................................................. 71

Improved Data Collection ............................................................................................................................. 72

Improved Model Outputs .............................................................................................................................. 73

Validation ..................................................................................................................................................... 73

Linkage to Transportation Models ................................................................................................................ 74

Linkage to Biophysical Impacts .................................................................................................................... 74

Conclusions.................................................................................................................................................. 75

References ................................................................................................................................................... 75

iv
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 1

Planners Need Better Data ________

As the demographics of public land recreational visitors change, planners and managers of public lands face

the challenge of protecting resources while providing

high quality visitor experiences. Because our political

environment demands ever more reliance on scientific

data and transparent decisionmaking, planners and

managers need better tools to help them understand

current visitor use, analyze potential alternatives for

future use, and communicate the implications of various alternative decisions in ways that are meaningful

to the public.

An understanding of the temporal and spatial distribution of visitor use is fundamental to many of the

questions that planners and managers ask. Are existing use patterns sustainable and appropriate to resource and experience goals? Are existing spatio-temporal distributions optimal for visitor experience,

resource protection, and efficient operations? Such

questions cannot be answered without knowledge of

the kinds, amount, and distribution of visitor use.

Particularly in large areas and in areas with complicated access and circulation patterns, planning staff

may only have anecdotal information about use concentrations, lengths of stay in various areas, crowding, underused or overused facilities, and other factors. Further, staff and public perceptions of use

patterns are often at odds.

Public land planners often have little information to

help them assess the likely success and efficiency of

alternative approaches to visitor use management.

Impact analysis often consists of educated guesses by

planners and managers. For example, if changes to

access and circulation patterns are needed in one place,

how can planners assess how those changes will affect

use patterns and associated impacts in other places?

How can planners assess possible effects of management decisions on adjacent lands? How can planners

identify the management strategies (area closures, use

limits, additional facilities or access, for example) that

Chapter 1:

Why Model Recreation Use?

David N. Cole

Kerri Cahill

Marilyn Hof

could be most effective in achieving desired modifications in visitor behavior and use patterns?

Another difficulty in planning for management of

public lands is that many of the impacts and tradeoffs

associated with various planning alternatives are qualitative, value laden, and difficult to demonstrate. Yet

we ask the public to “buy in” to future conditions that

can profoundly effect their visitation and experiences,

sometimes without clear understanding of the implications of various choices. How might planning alternatives affect a visitor’s ability to move about at his or

her own pace? How might the alternatives affect

visitors’ chances of coming to the area whenever they

want? What kinds of tradeoffs or sacrifices (using a

reservation system or permit system, for example, or

riding a shuttle bus) might we ask the public to make

to protect resources? Are the goals “worth it” to the

public?

To answer these sorts of questions, more tools are

needed to help understand and monitor baseline conditions, estimate appropriate use levels, describe the

consequences of management alternatives, and more

effectively communicate these consequences to the

public. Increasingly, visitor use simulation modeling

is gaining recognition as a critically important tool for

professional planning and management of recreation

on public lands.

What Is Simulation Modeling and

What Can It be Used For? ________

Visitor use simulation models replicate visitor use

patterns as they relate to an area’s natural and developed environments. These computer models allow

managers to better understand the spatial and temporal visitation patterns and “experiment” with different management strategies. Specifically, simulation

models can be used to

• Better understand the baseline spatial and

temporal patterns of visitor use.
2 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

• Predict how distributions of visitor use are

likely to change in response to both management actions and factors not subject to managerial control.

• Test the feasibility and effectiveness of management plan alternatives.

• Monitor hard-to-measure parameters (such

as people at one time at a certain attraction or

walking on particular trails) by using easily

measured indicators (such as number of cars

entering the area or parking at a trailhead).

• Support the planning and management of

visitor use in situations where monitoring and

predicting visitor flow are difficult.

• Improve communication of implications of management prescriptions to the public.

Past and Present Simulation

Modeling Efforts ________________

As detailed in Chapter 2 of this report, the potential

utility of simulation modeling as a public land and

wilderness management tool has been recognized for

decades. In a major developmental effort in the 1970s,

International Business Machines (IBM), Resources

for the Future, and the Forest Service collaborated to

develop a travel simulation model for wilderness—the

Wilderness Use Simulation Model. This model was

successfully applied in several wilderness areas, and

was adapted to river recreation (McCool and others

1977) and a long-distance trail (Potter and Manning

1984). On the Colorado River in Grand Canyon National Park, Arizona, Underhill and others (1986) used

the model to evaluate the effect of upstream dam

operations on downriver whitewater boating patterns.

Despite this promising beginning, the cost and difficulties of running computer simulations in the 1970s

and early 1980s were simply too great. Simulations

often had to be run on remote mainframe computers,

with individual simulations costing $1,000. With the

advent of the personal computer, however, costs and

difficulties have declined dramatically. Consequently,

interest in recreation travel simulation modeling is

now increasing.

Overview of Report ______________

The intent of this report is to describe the current

status of simulation modeling of recreation behavior,

illustrate its utility, and comment on its future. Chapter 2 provides an historical perspective on work to

date, particularly the pioneering work conducted in

the 1970s and 1980s. Chapter 3 provides an overview

of modeling options, common data input requirements,

and useful model outputs.

Chapter 4 presents case studies drawn primarily

from the work of the two groups of people who have

been most active in this arena: Dr. Robert Manning

and his associates at the University of Vermont (particularly Dr. Steven Lawson, now at Virginia Tech),

Dr. Randy Gimblett, University of Arizona, and Dr.

Robert Itami, Geodimensions Pty Ltd. Manning and

his associates have taken a commercially available

general-purpose simulation package (Extend 1996),

designed to simulate manufacturing and business

systems, and used it to model recreation systems.

Their case studies demonstrate the wide variety of

situations that can be modeled. The utility of model

output to addressing questions about appropriate use

levels and the consequences of alternative management scenarios are clear in these examples.

Gimblett and Itami developed a special purpose

simulator (RBSim), designed specifically to model

recreation behavior. RBSim is integrated with GIS

technology and allows for rule-based agent simulations (see Chapter 3) in addition to the probabilistic

simulations used in the Wilderness Use Simulation

Model and in the applications using Extend that

Manning, Lawson, and others have conducted. More

recently Manning, Lawson, and others have been exploring GIS linkages and rule-based simulation using

Extend.

Chapter 5 discusses future directions in recreation

simulation modeling.

References _____________________

Extend. 1996. 3.2.1. San Jose, CA: Imagine That, Inc.

McCool, S. F.; Lime, D. W.; Anderson, D. H. 1977. Simulation

modeling as a tool for managing river recreation. In: Proceedings:

river recreation, management and research symposium; 1977

January 24–27; Minneapolis, MN. Gen. Tech. Rep. NC-28. St.

Paul, MN: U.S. Department of Agriculture, Forest Service, North

Central Forest Experiment Station: 304–311.

Potter, F. I.; Manning, R. E. 1984. Application of the wilderness

travel simulation model to the Appalachian Trail in Vermont.

Environmental Management. 8: 543–550.

Underhill, A. H.; Xaba, A. B.; Borkan, R. E. 1986. The wilderness

simulation model applied to Colorado River boating in Grand

Canyon National Park, USA. Environmental Management. 10:

367–374.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 3

The potential utility of modeling as a park and

wilderness management tool has been recognized for

decades. Romesburg (1974) explored how mathematical decision modeling could be used to improve decisions about regulation of wilderness use. Cesario (1975)

described a computer simulation modeling approach

that utilized GPSS (General Purpose Systems Simulator), a simulation language designed to deal with

scheduling problems. He identified a number of potential uses and the advantages of using simulation

instead of trial and error. In this chapter, we review

many of the most important applications of computer

simulation modeling to recreation use, and the major

lessons learned during each application.

Wilderness Use Simulation

Model _________________________

In the early 1970s, International Business Machines

(IBM), Resources for the Future, and the Forest Service began to collaborate in the development of a

wilderness travel simulation model. The Wilderness

Use Simulation Model was stimulated by Stankey’s

(1972) hypothesis that a visitor’s satisfaction with a

wilderness experience is inversely related to the number of encounters with members of other parties.

Based on this notion, Fisher and Krutilla (1972) suggested that the optimum use of a wilderness area

should be the level at which the incremental benefit of

an additional party is offset by the decrease in satisfaction resulting from encountering additional parties.

To define this optimum level, one had to establish an

empirical relationship between the benefits enjoyed

during an outing and the number of parties encountered, making it necessary to quantify encounters.

Chapter 2:

Historical Development

of Simulation Models of

Recreation Use

Jan W. van Wagtendonk

David N. Cole

Numerous sociological studies were launched to elicit

the relationship between benefits and encounters,

but, other than laborious field work, no means existed

for enumerating encounters.

To overcome this obstacle, researchers from Resources for the Future began to develop a computer

model that would simulate travel behavior in a wilderness and track encounters between parties. They soon

found that the programming expertise needed far

exceeded their capabilities, so they approached IBM

for assistance. The result was a simulation program

written by Heck and Webster (1973) in the General

Purpose Simulation System (GPSS) language running on an IBM mainframe computer. The model was

dynamic, stochastic, and discrete, meaning that it

represented a system that evolves over time, incorporates random components, and changes in state at

discrete points in time (Law and Kelton 2000).

The model included a replica of an area’s travel

network, its entry points, trails, cross-country routes,

and campsites. It distinguished between the travel

patterns of different kinds of users (different group

sizes and modes of travel) and of groups arriving at

various times (different weeks, different days of the

week, and different times of the day). Each simulation involved generating groups of different kinds

and different travel patterns arriving at various

entry points where they are assigned a specific travel

route (set of trail segments and campsites). The

groups move along their route, overtaking and passing other groups, encountering groups moving in the

opposite direction. They stay at campsites, where

they also may encounter other groups. By keeping

track of parties, the model recorded the number of

encounters for each party, with whom each encounter

occurred, the location of those encounters, and the

types of encounters (meeting, overtaking, or camp).
4 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

Output from the model included numerous tables

showing encounters by party type, location, trip

length, and total use level.

The data required to build the model included information about both the area and wilderness visitors.

Area information consisted primarily of information

about the network of trail segments and overnight

campsites. Visitor data included weekly, daily, and

hourly distributions of use, party size distributions,

and mode of travel mix. For example, small parties on

horseback were distinguished from large hiking parties. The various routes that visitors took (combinations of trail segments and campsites) were enumerated along with their probability of being selected.

Finally, the model required information about the

time it took parties of different sizes to hike or ride

each trail segment in each direction.

The prototype model was tested in the Spanish

Peaks Primitive Area (Smith and Krutilla 1976) and

the Adirondack Forest Reserve (Smith and Headly

1975). The Spanish Peaks Primitive Area, now a

25,320-ha unit of the Lee Metcalf Wilderness Area, is

located in the southwest corner of Montana, USA, just

northwest of Yellowstone National Park. Data collected by the Forest Service in 1970 and 1971 were

used to initialize the model and develop different

simulation scenarios. Examination of U.S. Geological

Service and Forest Service maps identified eight

trailheads, 79 trail segments, and 34 campsites. Diaries and sketch maps from some 400 parties were used

to determine arrival patterns, party sizes, modes of

travel, routes, and route selection probabilities by

mode of travel. One hundred and four unique routes of

various lengths were identified, with up to 6 nights of

stay. Segment travel times were derived by applying

results from a previous study (Cunningham 1971) and

through discussions with users and wilderness staff.

The base case simulation was run with 177 hiking

parties and 48 riding parties entering during a 4-week

period (Smith and Krutilla 1976). Hiking parties had

a total of 390 encounters with other hiking parties and

112 trail encounters with riding parties, while riders

recorded an additional 32 encounters with other riding

parties. Hikers had 60 total camp encounters, while

riders had 20 camp encounters.

Smith and Krutilla (1976) validated the model by

having managers who were familiar with the Spanish

Peaks judge the reasonableness of the inputs and

outputs by looking at the variance of the outputs.

Sensitivity analyses using 10 replications each of nine

different scenarios showed that the model was relatively insensitive to variation in travel times, that use

levels were directly related to encounters, and that

evenly distributing arrival patterns reduced encounters. The authors thus considered the model to be

valid, but recommended further testing.

As a result of the Spanish Peaks experiment, Smith

and Krutilla (1976) suggested that a large-scale field

test be conducted. A workshop was convened to recommend changes to the simulation model. Suggested

modifications included the ability to track visible encounters that occur when two parties are close enough

to see each other but are not occupying the same trail

or campsite, additional output tables on camp and

trail use levels, the ability to simulate large numbers

of parties for extended periods of time in complex trail

networks, and the ability to set probabilities for

trailhead selections before routes are selected (van

Wagtendonk 2003). Subsequently, a second-generation model was developed by Resources for the Future,

under contract with the Forest Service, to accommodate a wider range of situations and provide additional

outputs (Schecter 1975).

Desolation Wilderness Application

This new model was demonstrated in the Desolation Wilderness in California (Shechter and Lucas

1978) and in the complex of wilderness areas surrounding and including Yosemite National Park (van

Wagtendonk 1979). The Desolation Wilderness is

located in the Sierra Nevada Mountains of California

east of Lake Tahoe. The 25,390-ha wilderness was

originally established as a primitive area in 1930 and

was designated as a wilderness area in 1969. Visitor

use exceeded a quarter million visitor-days in 1975,

and the wilderness continues to be heavily used

today. Data were gathered from mandatory wilderness permits, trip maps and diaries returned by 4,400

visitors, and new field surveys (Shechter and Lucas

1978). These sources provided information on arrival

patterns, hiker-rider ratios, party sizes, trails and

campsites, routes, travel times, and trailhead and

route selection distributions. Ninety-nine percent of

parties were hiking parties, and more parties arrived

on Friday or Saturday than other days of the week. A

review of existing maps showed 16 trailheads feeding

a network of 178 km of trails, 286 trail segments, and

125 campsites. Out of this network, the groups used

797 unique routes. All of these data were laboriously

encoded on punch cards and incorporated into the

model deck that was then taken to the U.S. Mint

computer in San Francisco to be run at night (van

Wagtendonk 2003).

Thirteen different scenarios were run on the model

depicting various use levels and trailhead allocation

patterns (Shechter and Lucas 1978). The base case

simulated 1,400 hiking parties per week using arrival

patterns and route selections as recorded in the visitor

diaries and travel times from the field survey. The

average number of trail encounters per party-day for

these parties was 10.8, and the average number of
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 5

camp encounters per party-night was 6.4. When use

was increased or decreased by 25 percent and 50

percent, both types of encounters changed proportionally; for example, a 50 percent increase in use resulted

in roughly a 50 percent increase in trail and camp

encounters. Regressing camp encounters per partynight over party-nights yielded the following equation:

Encounters/Party-Night = –0.14 + 0.024(Party

Nights).

Total use for eight scenarios that dealt with different trailhead selection patterns ranged from 1,278

parties to 667 parties. The highest use occurred when

trailhead quotas were implemented for only the five

most heavily used trailheads as prescribed in the

wilderness management plan. The lowest use occurred

when the heavily used trailheads were limited to 10

parties per day and the lightly used trailheads to five

parties per day. Trail encounters for these two scenarios ranged from 9.1 to 3.5 per party-day, while

camp encounters ranged from 5.6 to 3.1 per partynight. The scenario that allowed 10 parties per day to

enter all trailheads had 10.8 camp encounters per

night even though total use was only 1,120 parties.

This resulted from an increase in longer trips being

taken from lightly used trailheads and a decrease in

short trips taken from heavily used trailheads.

Shechter and Lucas (1978) concluded that the simulator had great potential for application to actual management situations. The combination of managers and

scientists on a team to gather the data and develop and

test scenarios proved useful and realistic. Output from

the simulator provided an accurate picture of use and

encounters that could not be obtained by other means,

replacing guesses and intuition. In addition, it was

thought that an indirect benefit of the simulator was

the acquisition of the information required to run it;

data about the area and its use would be valuable for

making management planning decisions.

Yosemite National Park Application

Simultaneous with the effort to apply the simulator

to the Desolation Wilderness, scientists and managers at Yosemite National Park began assembling the

necessary information to run the simulator (van

Wagtendonk 1979). The Yosemite Wilderness was

designated in 1984 and encompasses 281,855 ha of

the park. Contiguous wilderness areas include the

44,891-ha Emigrant Wilderness on the Stanislaus

National Forest, the 19,440-ha Hoover Wilderness on

the Toiyabe and Inyo National Forests, and the 37,583-ha

Ansel Adams Wilderness on the Inyo and Sierra

National Forests. There are 55 trailheads that lead to

1,112 km of trail and 375 traditional campsites in the

Yosemite Wilderness. An additional 46 trailheads

feed 666 km of trail and 197 campsites on Forest

Service wilderness areas adjacent to the park. Use

peaked in the Yosemite Wilderness in 1975 when

nearly 219,000 visitor-nights were recorded (van

Wagtendonk 1981). Approximately 4 percent of the

use in Yosemite originates on adjacent Forest Service

wilderness. Wilderness use in the Yosemite complex

has been regulated through the use of wilderness

permits since 1971.

The Yosemite application was unique in its use of

permit data as the primary source of visitor information (van Wagtendonk 1978). Party size, mode of

travel, arrival patterns, and the zones through which

a party plans to travel are all easily obtained from the

permit. Zone information was converted into routes

using methods described by van Wagtendonk (1978).

Permits avoided the costs associated with visitor surveys, and allowed all routes actually recorded to be

simulated rather than just a sample of possible routes.

The validity of the information on the permits and the

travel behavior of parties who do not get permits were

also determined. In Yosemite, van Wagtendonk and

Benedict (1980a) found that 92 percent of the parties

had permits and that 62 percent of them made changes

to their trips. The average trip was shortened by onehalf day, and spatial changes were common.

A special study was conducted in Yosemite to determine trail travel times for parties on 1-mile trail segments (van Wagtendonk and Benedict 1980b). It took

an average of 34.8 minutes for backpacking parties,

36.4 minutes for day hiking parties, and 27.3 minutes

for horse riding parties to travel the sample trail segments. Party size was not significant for all three types

of parties, and slope-direction class was significant for

only backpacking parties. For these parties, average

times for uphill travel were greater than downhill

travel, and time increased as slope increased. These

data were used as input to the simulator.

Modifications to the simulator made for the Desolation Wilderness allowed the Yosemite study to focus

on trailheads, campsite encounters, and campsite use

levels (van Wagtendonk 2003). The decision to concentrate on campsites was based on work by Absher and

Lee (1981), which indicated that the sociological effect

of trail encounters depended more on the behavior of

the encountered party and the location of the encounter than on the number of encounters. A single encounter with an ill-behaving party could have much more

impact than meeting numerous parties exhibiting

acceptable behavior. The impact of an encounter was

less in areas where people expected to meet others

than in areas where encounters were not expected.

Managers preferred trailhead quotas for rationing use

because external controls allowed maximum freedom

to visitors consistent with wilderness experience and

resource constraints (van Wagtendonk and Coho 1986).
6 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

The 20,000 wilderness permits issued in 1973 were

used for the base case simulation because travel behavior that year was not limited; use in subsequent

years might have been affected after use limits were

imposed (van Wagtendonk 1981). Two use levels and

two trailhead allocation patterns were examined and

compared to the base case. The use levels were a 50

percent increase from the base case and a 50 percent

decrease. The first trailhead allocation scenario was

based on daily entry quotas derived from a computer

program called QUOTA (van Wagtendonk and Coho

1986). The program compared actual levels of use in

zones to desired levels, and reallocated entries until no

zone exceeded its limit. Desired zone-use limits were

based on van Wagtendonk (1986). The second trailhead

scenario rounded the daily quotas up to the nearest

number divisible by 5.

As at Desolation Wilderness, there was a linear

relationship between amount of use and number of

campsite encounters (van Wagtendonk 2003). Across

all runs, the relationship between camp encounters

per party-night and party-nights yielded the following

equation:

Encounters/Party-Night = –0.02 + 0.011(PartyNights).

However, the number of campsite encounters per

unit of use was less than half that predicted for the

Desolation Wilderness. There are at least two potential reasons for this difference. First, a greater number

of trailheads gives visitors more opportunities to disperse and, consequently, have fewer encounters per

party-night. Second, the wilderness permits provided

thousands of potential routes compared to only hundreds from the diaries used for the Desolation Wilderness. This diversity of routes dispersed parties during

the simulations, resulting in fewer encounters per

party-night.

Trailhead entries for the base case scenario ranged

from one person per day through the most lightly used

trailheads to over 100 people per day through three of

the most popular trailheads (van Wagtendonk 2003).

Scenarios based on trailhead quotas reduced the peaks

both temporally and spatially, but increased encounter

levels in the more sparsely used areas. These results

were similar to the results from the Desolation Wilderness, as would be expected when use is dispersed.

Combined with the trailhead quota program, the

simulation results provided the information needed

by managers to implement quotas for the Yosemite

complex of wilderness areas. In that sense, the simulator was a success. However, the cost of running

simulations on a remote mainframe computer exceeded U.S. $1,000 per scenario, limiting the feasibility of further experiments.

Applications on Rivers

Modification of the model for river settings allowed

it to be applied to the Green and Yampa Rivers in

Dinosaur National Monument (Lime and others 1978)

and to the Colorado River in Grand Canyon National

Park (Underhill and others 1986). Rivers present

unique situations for simulating wilderness use. A

river represents a single trail with only a few entry and

exit points, there is only one direction of travel, and

travel times are similar because they are largely

determined by the flow.

The Green River runs for 85 km through Dinosaur

Monument and is joined there by a 69-km segment of

the Yampa River. Each river has one primary launch

site, and there are three access points below their

confluence. Twelve developed campgrounds and 14

primitive campsites are designated along the rivers.

Parties wishing to float the rivers apply for reservations and are assigned launch dates and campsites

(McCool and others 1977). In 1973, a seasonal use

limit of 17,000 people was implemented. Most of the

information necessary to run the simulator was available from records kept by the National Park Service.

Diaries from sample parties provided information on

travel times and routes for private and commercial

trips by group size, and details on travel behavior

including lunch stops, stops to scout rapids, and hikes

up side canyons.

A 1-week period in June 1975, when 44 parties

launched trips, was chosen for the simulation. In

addition to the base case, six different scenarios were

run that increased the number of parties, redistributed launches over the days of the week, and added or

eliminated campsites (Lime and others 1978). Occupancy rates at one heavily used campsite and overall

encounter rates in camp and on the river were the

focus of the experiments. Increasing use had a proportional effect on both camp encounters and river encounters, and reduced the number of days and nights

without encounters. Redistributing daily launches

increased use at the heavily used site slightly but did

not appreciably change encounter rates. Adding new

campgrounds and closing others had little effect on

encounters, but did shift use from the heavily used site

to the new sites.

Lime and others (1978) concluded that the simulator

was useful as an aid to river planning and management. In particular, simulating the effect of different

launch dates and times allowed managers who have

control over access points the ability to see the effects

of those actions before implementing them. Lime and

others (1978) recommended that efforts be made to

monitor and evaluate the resulting use patterns if the

model is to be used to test management policies.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 7

Underhill and others (1986) adapted the wilderness

use simulation model for application to the Colorado

River. The Colorado River runs through Grand Canyon National Park in Arizona for 360 km from Lees

Ferry in Utah to Diamond Creek in Arizona. They

used National Park Service records, trip logs kept by

rafters, river patrol records, and their own records to

develop the input data for the model. Trip itineraries

from 1984 for oar boats and motor boats were based on

actual frequencies of use for the 199 river segments,

110 stopping points, and 141 campsites. A computer

program took these data, calculated routes, and coded

them for input to the simulator. Like the Yosemite

example, this method provided a myriad of possible

routes rather than a limited set based on trip diaries.

Forty-eight routes were generated for the 29 parties

that launched each week of the 5-week simulation

period. Of these parties, 18 were commercial motor

trips, six were commercial oar trips, and five were

private oar trips.

Use and encounter levels were evaluated for the

base case and five scenarios that varied the mix

between oar boats and motor boats, the total number

of boats, and the launch schedule (Underhill and

others 1986). Because the Park Service was considering phasing out motor boats, two of the scenarios were

for different number of oar boats only. Two more

scenarios increased use for both oar boats and motor

boats and changed the ratio between the two types of

boats. The fifth scenario evenly distributed launches

over days of the week and hours of the day. The

relationship between number of parties per week and

encounters was linear, with each party averaging

approximately 0.5 encounters per day. Changing

launch days and times to an even schedule decreased

encounters by 25 percent. The scenarios with only oar

boats resulted in more visitor days of use, of course,

because these boats took longer to float the canyon.

The authors felt that the model was useful for predicting changes in the use of sensitive areas and the

encounter rates between parties. Their modification

for deriving itineraries provided a realistic suite of

routes at a reduced cost.

Borkan and Underhill (1989) used the simulator to

study the impacts of flow releases from the Glen

Canyon Dam on Colorado River raft trips in the

Grand Canyon. In this case they modified the time it

would take to float the various segments on the river

given different flow releases. Flow rates were determined by the Streamflow Synthesis and Reservoir

Regulation Model developed by the U.S. Bureau of

Reclamation. Oar boat and motor boat parties had

their travel times changed by flows in two ways: the

time it would take to float a segment and the delay

time at rapids due to low water. Five flow alternatives

were tested with the model: (1) variable releases from

month to month with no daily or weekly fluctuations,

(2) wide fluctuations consistent with maximum power

production, (3) higher minimum and lower maximum

flows than alternative two, (4) steady flows during

the rafting season with fluctuations the rest of the

year, and (5) low winter flows and higher summer

flows with moderate fluctuations.

Borkan and Underhill (1989) concluded that higher

flows allowed more time at attraction sites, that low

flows increased delays at rapids, and that an increase in the number of parties increased the encounter rates. This study showed that the simulator

was useful for evaluating management alternatives

that cannot cheaply be evaluated through trial and

error.

Application on the Appalachian Trail

Evaluation of alternative management scenarios

was the focus of an application of the model to a trail

(Potter and Manning 1984). The Appalachian National Scenic Trail traverses 3,456 km in 14 States

from Georgia to Maine. From a simulation standpoint,

a linear trail system is similar to a river except that

movement is in two directions rather than one. Potter

and Manning (1984) applied the simulator to a heavily

used 100-km section of the Appalachian Trail in Vermont. Access to this section is through five roads and

10 maintained side trails. There are three heavily

used camp areas by ponds and 16 primitive shelters.

Data for the simulator were obtained in the summer of

1979 from a sample of hiking parties stratified by

trailhead use levels. A questionnaire and a map diary

were used to determine party characteristics, entry

points, arrival and departure patterns, and routes

including campsites and rest stops.

Simulation of actual use of 550 parties during a

2-week period resulted in an average of 3.3 trail

encounters per party-day and 2.3 camp encounters per

party-night. Management scenarios included evenly

distributing entries over access points, evenly distributing entries over days of the week, and reducing use

by 100 parties. Potter and Manning (1984) felt that

temporal and spatial redistributions of use were more

effective than decreasing overall use for reducing trail

and camp encounters. They also concluded that camp

encounters appeared to be a more limiting factor than

trail encounters.

Manning and Potter (1984) used the Wilderness Use

Simulation Model as a teaching tool in a recreation

class at the University of Vermont. Their experience

showed that the model reduced the complexity of the

system under study, allowed students to devise and

test various management strategies, and provided the

opportunity for students to become familiar with actual parks and wilderness areas.
8 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

Beyond the Wilderness Use

Simulation Model _______________

The Wilderness Use Simulation Model proved its

usefulness in applications from simple, linear river

systems to large, heavily used wilderness areas. All of

these studies showed that trail and camp encounters

are directly related to total use level, and that management alternatives that reduce use will lead to reduced

encounter levels. In addition, the model was effective

in evaluating the temporal and spatial effects of various trailhead allocation patterns that were then used

to establish trailhead use quotas for a complex of

wilderness areas in California. Those trailhead quotas

are still in use 27 years later. Equally effective was a

test of the impact of fluctuating dam releases on

encounters and use levels in the Grand Canyon.

Despite this promising beginning, however, the

Wilderness Use Simulation Model never lived up to its

original promise and fell into disuse. Much of this can

be blamed on the cost and difficulties of running

computer simulations in the 1970s and early 1980s.

Simulations often had to be run on remote mainframe

computers, with individual simulations costing U.S.

$1,000. With the advent of the personal computer, all

this has changed. By the mid-1980s, Rowell (1986)

reported that he had modified the Wilderness Use

Simulation Model so that it could be run on a personal

computer. He also built in the capability to graphically

represent output data in map form, making it spatially

explicit. However, there was little effort to encourage

use of this model, and land managers apparently have

never used it.

Renewed interest in recreation travel simulation

modeling developed in the 1990s. Two different groups

of people, utilizing two different approaches, have

been most active. Manning and his associates at the

University of Vermont have used a commercially developed general-purpose simulation software package

designed to simulate manufacturing processes and

business systems. They used the software package

Extend (1996), although there are a number of other

general-purpose simulation software packages that

could also have been used. They adapted the software

to build simulation models for use in their “carrying

capacity” research in several National Parks. Their

models have much in common with the Wilderness

Use Simulation Models developed in the 1970s, but

can be run inexpensively on personal computers. In

particular, this approach uses probabilistic simulation models in which simulated groups are assigned

entire travel routes. As described in other chapters of

this report and elsewhere, these models have been

used to simulate numerous situations, including

frontcountry hiking (Lawson and others 2002);

backcountry camping (Lawson and Manning 2003a,b);

bicyclers, hikers, and horses on multiuse roads (Wang

and Manning 1999); and public transportation systems in parks (Budruk and others 2001). Limited

validity testing suggests the models may provide a

reasonably accurate representation of the system.

Moreover, these studies have been exemplary in illustrating the varied management applications of the

models.

Gimblett and Itami have developed a special purpose simulator (RBSim) designed specifically to build

simulations of recreation behavior (Gimblett 2002;

Itami and others 2002). Their simulator is integrated

with GIS technology and currently allows for both

probabilistic simulations (like the Wilderness Use

Simulation Model and models using Extend) and rulebased agent simulations. With rule-based simulations, instead of assigning groups entire travel routes,

autonomous agents make decisions, on the basis of

behavioral “rules” along the way, responding to what

is encountered (Gimblett and others 2000, 2001, 2002).

Rules can be developed based on observation, interviews, visitor surveys, and common sense. The possibility of employing rule-based simulations, in addition

to probabilistic simulations, provides the opportunity

to use modeling in even more diverse situations and

for more diverse purposes. RBSim also provides spatially explicit visualization capabilities that can be

helpful in gaining insight into the behavior of

recreationists, as well as the spatial pattern of use.

Interestingly, RBSim models have been developed for

portions of the Sierra Nevada and for the Colorado

River in the Grand Canyon (Daniel and Gimblett

2000), two of the places where the original Wilderness

Use Simulation Model was applied. Limited validity

testing suggests that the models may provide a reasonably accurate representation of the system.

Conclusion_____________________

Computer simulation modeling is a well-established

technology. Even for recreation applications, it has

been sporadically used for more than 30 years. Initial

applications demonstrated the validity and utility of

modeling, but further progress was hampered by the

primitive state of technology and the expense of building and running the models. Although vast improvements in technology over the past 30 years have

greatly reduced the cost of simulating recreation use,

there are still costs associated with data collection,

model building, and validation. To us, these costs seem

small in relation to the benefits that can be gained by

routinely applying computer simulation modeling to

improve visitor management programs.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 9

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USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 11

Capturing Behavior of

the System _____________________

The field of simulation modeling has grown greatly

with recent advances in computer hardware and software. Much of this work has involved large scientific

and industrial applications for which substantial financial resources are available. However, advances in

object-oriented programming and simulation methodology, concurrent with dramatic increases in computer capabilities and reductions in computer hardware costs, have meant that the benefits of simulation

can be extended to areas that previously have been

impractical. This includes recreation management.

The challenge of simulation modeling is to capture

the essential behavior of the system being modeled. In

outdoor recreation, this means capturing and representing the characteristics of the physical environment (for example, a system of trails, roads, waterways, and/or facilities) and modeling the behavior of

visitors as they interact with the environment and

with each other. In the most basic sense, models have

three components: (1) input variables that describe

the system being modeled, (2) software and associated

modeling approaches designed to process these input

variables, and (3) output variables that are useful to

planners, managers, and scientists. This chapter outlines these components for recent modeling efforts in

park and wilderness management. Chapter 4 provides

a series of case studies designed to illustrate these

basic components of simulation modeling and demonstrate their potential usefulness.

Chapter 3:

Overview of Computer

Simulation Modeling

Approaches and Methods

Robert E. Manning

Robert M. Itami

David N. Cole

Randy Gimblett

Modeling Approaches

and Software ___________________

Simulation modeling software is needed to process

input variables, generate data analyses, and produce

output. Three approaches to modeling and simulation

of relevance to recreation travel simulations are trace,

probabilistic, and rule-based agent models.

Trace, Probabilistic, and Rule-Based

Agent Simulations

Trace simulations directly simulate travel itineraries collected in the field. Visitor arrival, trip itineraries,

and duration of stay at destinations are simulated

directly from survey data rather than using probability distributions or random numbers. These simulations are useful for examining existing pattern of use,

and are often used to validate probabilistic and rulebased simulation models that are derived from the

same data. Probabilistic simulation models are based

on a representative sample of visitor trip itineraries.

Visitors’ trips are then modeled based on the probability of a visitor selecting a single trip itinerary out of the

entire sample, or alternatively, the probability of selecting the next destination based on the probability

distribution of all destinations originating from the

current destination. Probability models are the standard method for modeling baseline conditions. Probability distributions for either trip itineraries or origindestination pairs are a convenient way to “ramp up”

numbers of visitors (increase visitor use levels) in a

simulation, since a standard assumption is that as the
12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

number of visitors increase, the distribution of trip

itineraries will remain the same.

Probabilistic simulation assumes that the distribution of trip itineraries in the future will remain similar

to the distribution today, regardless of how the system

changes. This may be an inappropriate assumption for

a system that is changing dramatically. Consequently,

probabilistic simulation may not be an appropriate

way to model behavior in new recreation settings or in

existing settings where management policies may

introduce new travel networks, delete existing travel

networks, or where behavior may change due to

changes in recreation mode or mix of recreation

types. For these situations, rule-based simulation

may be more appropriate (Itami and others 2004).

Rule-based simulations use autonomous agents. The

agents are autonomous because once they are programmed, they can move about their environment,

gathering information and using it to make decisions

and alter their behavior according to specific environmental circumstances generated by the simulation.

Each individual agent has its own physical mobility,

sensory, and cognitive capabilities. Because autonomous agents have their own reasoning system for

navigating a travel network, the travel network must

be attributed with properties to which the agents

respond. These attributes may be in the form of attractions such as scenic views, interpretive centers, picnic

areas, or playfields, and detractors such as hazardous

areas, extreme weather events, or other environmental factors that would constrain movement or cause

visitors to avoid an area. It is these attributes and the

attributes of other agents that determine agent rules.

Terminating and Steady-State Simulations

A second important choice in simulation modeling

approaches is whether to design simulations to be

terminating or nonterminating (steady-state). Terminating simulations model events that have a specified

length, while a steady-state simulation models situations in which there is no natural event to specify the

length of a simulation run (Law and Kelton 2000). The

choice between these two should be made on the basis

of the situation being modeled and the desired modeling outputs. A terminating simulation has a known

initial state (usually zero) and a known ending state.

For day use issues, it is clearly appropriate to use

terminating simulations to describe what happens

over a given day, based on data representing the

typical arrival sequence for a day.

When the situation of interest involves people on

multiday trips, modeling individual days makes little

sense. Nor does it make sense to model the entire year

or season of use. What we are usually interested in

understanding is how the system operates when at its

full operating level (often at its maximum or peak).

This type of situation might be modeled using a steadystate simulation. A simulation is called steady-state

because the simulation, after an initial “warm up”

period, is designed to replicate system behavior over

the long run at a given level of production or capacity.

It is currently unclear whether it is more appropriate to model multiday backpacking trips using terminating or steady-state simulations (see for example

the John Muir Wilderness case study in Chapter 4).

This situation has some characteristics that seem best

handled with steady-state simulations and some that

seem best handled with terminating simulations. Regardless of appropriateness, steady-state simulations

are more challenging to conduct and analyze. They

must be run over long periods to get a reliable average

measure of system behavior that is not biased by

short-term effects of random variables and auto-correlation. The results of steady-state simulations must be

carefully interpreted since they can overestimate parameters if the actual duration of steady-state conditions in the field is relatively short in comparison to

the run-lengths required to get valid simulation results (Law and Kelton 2000). Currently, we do not

have a good understanding of how to use steady-state

simulations in recreational contexts.

General Purpose Simulation Software

and Special Purpose Simulators

Commercially available general purpose simulation software packages are usually developed with

business, industry, and government applications in

mind. However, it is possible to use this general

software to model outdoor recreation behavior. For

example, several of the case studies described in the

next chapter have adapted the simulation software,

Extend, developed by Imagine That, Inc., to recreational applications. Special purpose simulators,

however, are developed specifically to handle specialized applications. Several of the case studies described

in the next chapter have used RBSim, developed by

GeoDimensions Pty Ltd. This is a special-purpose

simulator designed to build simulations of recreation

behavior on linear networks. Special-purpose simulators will have more automated features specific to

the application of concern. General-purpose simulation software can also be modified to include automated features specific to the application of concern

(modeling outdoor recreation behavior).

Model Inputs ___________________

Simulation models require several types of input

data that can be obtained from several sources. Principal types of input include data on the travel network,
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 13

the environment, visitor characteristics, and, in some

cases, decision rules.

Travel Network

In all of the applications of simulation modeling to

park and wilderness management to date, recreation

use is constrained to linear travel networks. The

travel network may be represented by a road or trail

system, river, or flight path. Travel networks are

described as a series of connected links that are terminated or joined by nodes. In lay terms, links are

referred to as trail, road, or river segments. Nodes are

points that terminate a link or join three or more links

at an intersection, or may be points that mark a

destination along a single link (for example, a campsite or an attraction site). Travel networks are complete if there is a path from any node in the network to

any other node in the network. All simulations described in Chapter 4 require complete travel networks.

Links and nodes can have properties or attributes

assigned to them. For links, associated attributes

might include name, maximum travel speed, average

travel time, number of travel lanes, surface type and

condition, steepness, length, and width. Node attributes

might include name, a list of one or more facilities,

visitor capacity, or average visit duration.

Travel networks can be derived from existing maps,

or collected using traditional land survey techniques,

downloaded from Geographic Information Systems (GIS),

or collected using Global Positioning Systems (GPS).

Environmental Data

Environmental data may be required for specific

models depending on the nature of the simulation. For

example, a Digital Elevation Model (DEM) may be

required if visual encounters are recorded or if pedestrian speeds are to be affected by uphill and downhill

travel directions. Visual encounters occur when two

groups see each other only from a distance. The DEM

is represented by an evenly spaced grid of elevations.

It is normally imported from a GIS system in a standard exchange format.

If simulation is used for facilities management (for

example, to size parking lots, campgrounds, viewing

platforms, or other facilities), additional information

on each facility must also be collected, including the

location and capacity of the facility and the typical

duration of stay. Also, if queuing behavior at a facility

is to be simulated, information about the service times

and maximum queue length might also be required.

Visitor Characteristics

In its simplest form, for probabilistic models, a

simulation model of recreation behavior requires

information about travel mode characteristics (for

example, foot, car, bus, or horse), travel speed, and a

trip itinerary. In all cases, data collected must be in

the form of a census or a representative sample. The

sampling period must be appropriate to the needs of

the simulation. For terminating simulations, the

sample should be over the complete day or other

period of interest. For steady-state simulations of

peak use, sampling should be done during the peak

period of use.

Visitor characteristics are generally collected using

either direct observation or survey techniques. The

trip itinerary is a list of destinations and visit durations (or “delays”) at destinations. The trip itinerary

will usually have the following:

• An entry node (trailhead, park entry, and so on)

• A series of one or more destination nodes.

• An exit node (may be the same as the entry

node for round trips).

• Arrival time (the date and/or time the visitor

arrives). Often the arrival time is represented

by an arrival curve or arrival sequence in

which the number of visitors arriving per hour

or day is provided.

• Visit duration (or “delays”) at nodes. This may

be represented by a mean duration or a statistical distribution.

• Overall trip duration.

In a single simulation there may be many different

itineraries (as many as one itinerary per visitor) or

itineraries may be generalized as a set of probabilities

for moving from one destination to another. Probabilistic itineraries are generated using statistical analysis of groups of itineraries, with probabilities generated from one destination to another.

Travel itineraries can be obtained from reservation

systems where trip itineraries are specified (such as

backcountry permits), or can be collected from trip diaries, GPS tracking, race timing equipment, video monitoring, or self-administered trip recording. Chapter 4

includes a case study in which waypoint signs were used

to establish travel itineraries for a complex frontcountry

trail system at Mount Rainier National Park.

Arrival times can be collected from traffic counters,

survey data, pedestrian counting systems such as

turnstiles, pressure-sensitive pads, or infrared

counters. Delay times at facilities and destinations

can be obtained by trip diaries, onsite observation,

video monitoring, GPS tracking, race timing equipment, or directional pedestrian counters.

Decision Rules

Decision rules are required for rule-based simulation models. In this type of simulation, trip itineraries

may not be known because the travel network may not
14 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

yet be developed or because management conditions

have changed (such as trail or road closures), which

alter existing itineraries. Decision rules may also be

required where the behavior or itinerary of a visitor

may change based on conditions that are generated

through the course of the simulation, such as parking

areas becoming full, crowded conditions at visitation

sites, weather changes, or other events that may alter

onsite behavior.

Agent rules are a set of user-defined behaviors using

a stimulus/response or event/action framework. Rules

are determined by observing onsite behavior, surveys

or interviews, or “walkthroughs” whereby visitors or

people familiar with the pattern of use are asked to

systematically trace a trip on a map and identify

decision points, destinations and attractions, site detractors, or other visitor- or site-related conditions

that would alter path selection. This process is repeated for each visitor type or itinerary. Generally the

rules must be tested, by simulating one agent at a

time, to fine-tune the rule conditions, the sequence of

rules, and the complexity of rules needed to achieve

the desired path selection behavior. A rule of thumb is

to keep rules as few and simple as possible.

Boolean logic can be used to combine two or more

stimuli to create complex conditions for behavior.

Broolean logic organizes concepts in sets that are

controlled by the operators OR, AND, and NOT. An

example of a rule is:

If (TravelMode = ‘Car’ AND Locale = ‘Twelve

Apostles’ AND LocaleEntry = True) THEN Find

Carpark

In this example, the rule would only apply to agents

arriving in a car at the entry to the Twelve Apostles

Locale. If these three conditions are met, then the

agent is directed to find a carpark. This directive then

triggers the agent to execute its complex wayfinding

logic to create a new itinerary.

It is also possible to have probabilistic rules. For

example, we may know from count data that 60 percent of all visitors stop at the visitors center. We could

construct a probabilistic rule by assigning the rule a

probability of 0.60. When the agent triggers a probabilistic rule, it generates a random number in the range

of 0 to 1. If the number generated is less than or equal

to the probability assigned to the rule, then the rule is

executed. If it is greater than the probability assigned

to the rule, then the rule is ignored. Refer to the Port

Campbell National Park (Twelve Apostles) case study

for more detailed information on rule-based agent

modeling.

Model Outputs __________________

Simulation models can generate a great variety of

output variables. These variables should be specified

prior to model design and development, and should

be formulated on the basis of their potential usefulness to park and wilderness managers. Commonly

used output variables include use density, encounter, and queuing time measures. Use density measures report the number of visitors related to space or

time. For example, a simulation model of a backcountry trail and campsite network could report the number of visitors (hikers, bikers, horseback riders) that

traverse each trail segment per day or the number of

visitor groups at each campsite per night. Other

related output variables include people-at-one-time

(PAOT) at attraction sites and people-per-viewscape

(PPV) along heavily used trails. A simulation model

of a trail and campsite network can also report the

number of times visitor groups encounter one another along trails or at campsites. Encounters can be

recorded by type of visitor (hikers, bikers, horseback

riders), by type of encounter (meeting, overtaking,

visual), by place, and by unit of time. Encounter

estimates are a particularly important output. Because encounter type and number can influence visitor experience, standards are often written for encounters, which are difficult to directly monitor.

Finally, models can output queuing or waiting times

for visitor facilities or services. A variety of output

variables are included in the case studies described

in Chapter 4.

Since models are driven by random samples from

probability distributions, the output from two different simulation runs can be quite divergent. Consequently, it is important to incorporate replication into

simulation modeling. Replication can involve either

running many different simulations or, for steadystate simulations, running a single long simulation

that is divided into “batches” that serve as replicates

(Law and Kelton 2000). In either case, outputs should

be reported as means with confidence intervals.

Model Validation ________________

An oft-neglected step in the model-building process

is validation of the model. Validation is the process of

making certain that the simulation model provides an

accurate representation of the system being modeled.

As Law and Kelton (2000) note, if the model is valid,

decisions made using the model would be similar to

those that would be made if it were possible to physically experiment with the system. There are at least
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 15

three important steps in model validation (Law and

Kelton 2000; Naylor and Finger 1967; Schechter and

Lucas 1978). First, the model should be checked for

face validity. That is, it should work in ways that seem

reasonable to those who know how the system should

operate. Second, the operating assumptions of the

model should be empirically tested. Sensitivity analyses can be used to make certain that outputs change in

predicted ways when important model variables are

changed. Finally, it is important to assess how closely

model output data resemble those that might be obtained from field data.

Schechter and Lucas (1978) discuss various ways

they assessed the validity of the Wilderness Use Simulation Model. A common validation technique involves

comparing model output to field observations. Simulation models are normally developed initially to describe

current or “baseline” conditions in a park or wilderness

area. For example, input variables on visitor use levels

and patterns for a trail and campsite network might be

used to develop a model designed to estimate trail and

campsite encounters. Field observations of trail and

campsite encounters might be collected through direct

observation or self-reports of visitors, and these data

compared to model estimates to test the degree to which

model estimates are accurate or “valid.”

In the case studies reported in Chapter 4, face

validity has generally been assessed and sensitivity

analyses have been conducted. However, comparisons

of model output to field data have either not been

conducted or have been based on limited field data.

Therefore, our confidence in the validity of models is

limited, as is our knowledge of the precision of results.

Where limited field data were collected, it is not

possible to conduct a more rigorous validation than to

simply compare observations with outputs and conclude whether they seem similar enough to have

confidence in the model. With a sufficient sample of

field observations it is possible to rigorously estimate

the accuracy of the model. Freese (1960) shows how

standard chi-square tests can be used to estimate the

accuracy of the model.

Using the Simulator _____________

As noted above, the simulator can be used to describe the existing spatio-temporal distribution of use.

Output from the simulation can be valuable to management, given the difficulty of obtaining such information in any other way. The simulator can also be

used to monitor crowding-related indicators, such as

number of encounters, persons-at-one-time or persons-per-viewscape, either to describe the current

situation or to determine whether standards for such

indicators are being violated. The simulator provides

a much more cost-efficient way to gather such data

than monitoring these indicators directly.

The simulator can also be used for predictive purposes. For example, it can be used to predict the

maximum amount of use that can be sustained without exceeding some crowding-related standard, such

as trail encounters per day, by running scenarios with

various use levels until the use level that barely

complies with the standard is identified. A wide variety of alternative management scenarios can be simulated to predict the outcome of such actions as changes

in the timing and distribution of use, changes in the

travel network, and changes in facility type, location,

or capacity. Alternatives are evaluated by running the

simulator after making changes in visitor data or

travel network data. Outputs from several different

scenarios can be compared to identify those most

closely aligned with desired future conditions. Many of

these uses of simulation are illustrated in the case

studies in Chapter 4.

References _____________________

Freese, F. 1960. Testing accuracy. Forest Science. 6: 139–145.

Itami, R.; Raulings, R.; MacLaren, G.; Hirst, K.; Gimblett, R.;

Zanon, D.; Chladek, P. 2004. RBSim 2: simulating the complex

interactions between human movement and the outdoor recreation environment. Journal of Nature Conservation. 11: 278–286.

Law, A. M.; Kelton, W. D. 2000. Simulation modelling and analysis

3d ed. Boston, MA: McGraw-Hill. 760 p.

Naylor, T. H.; Finger, J. M. 1967. Verification of computer simulation models. Management Science. 14: 92–106.

Shechter, M.; Lucas, R. C. 1978. Simulation of recreational use for

park and wilderness management. Baltimore, MD: Johns Hopkins

University Press. 220 p.
16 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 17

Computer simulation models can be usefully applied to many different outdoor recreation situations.

Model outputs can also be used for a wide variety of

planning and management purposes. The intent of

this chapter is to use a collection of 12 case studies to

illustrate how simulation models have been used in a

wide range of recreation situations and for diverse

planning and management applications.

The types of recreation situations included in these

case studies vary in the size and remoteness of the

area being modeled, as well as in the type of recreation.

Case studies include:

• large backcountry areas: Bighorn Crags in the

Frank Church River of No Return Wilderness,

Misty Fjords National Monument, Colorado

River through Grand Canyon National Park,

Humphrey’s Basin in the John Muir Wilderness, and Isle Royale National Park;

• smaller frontcountry areas: Yosemite Valley

in Yosemite National Park, Alcatraz Island in

Golden Gate National Recreation Area, Arches

National Park, Acadia National Park, Paradise Meadows in Mount Rainier National Park,

and the Twelve Apostles in Port Campbell

National Park, Australia;

• overnight hikers: Bighorn Crags, Humphrey’s

Basin, and Isle Royale;

• day hikers on trails: Yosemite, Arches, Acadia,

and Mount Rainier;

• visitors to facilities: Alcatraz Island and the

Twelve Apostles;

• bicyclers: Acadia National Park;

• whitewater rafters: Grand Canyon National Park;

• visitors on cruise ships and ocean kayakers:

Misty Fjords;

• visitors in automobiles: Acadia.

Perhaps the most basic use of computer simulation

modeling is as a tool for describing current use patterns. The purpose of the first four case studies (Bighorn Crags, Misty Fjords, Grand Canyon, and John

Muir) is to illustrate this use. Simulation can also be

Chapter 4:

Case Studies of Simulation

Models of Recreation Use

David N. Cole

used to monitor crowding-related indicators, such as

number of encounters, persons-at-one-time or persons-per-viewscape, either to describe the current

situation or to determine whether standards for such

indicators are being violated. The Bighorn Crags and

John Muir case studies illustrate the estimation of

encounter rates, while persons-at-one-time or personsper-viewscape measures are estimated in the Yosemite,

Alcatraz Island, Arches and Acadia case studies.

Computer models can also be used for predictive

purposes. In the Isle Royale case study, simulation

was used to help planners identify alternative management actions that would be effective in reducing

campsite sharing. This was accomplished by predicting the effects on campsite sharing of reductions in

amount of use, changes in the spatial and temporal

distribution of use and increases in behavioral restrictions and number of facilities. At Yosemite, Alcatraz

Island, and Arches, simulation was used to predict the

maximum amount of use that can be sustained without

violating crowding-related standards. At Alcatraz and

Arches, the effect of alternative public transportation

systems on maximum allowable use was also predicted.

Simulation can also be used to prepare for the

future. In the Acadia and Twelve Apostles case studies, use is steadily increasing, and future use levels are

forecast to be much higher than they are presently. In

these case studies, simulation models are used to

predict the effect of increased use on crowding-related

variables. In the Twelve Apostles case study, the

effects of changes in park infrastructure are also

predicted. Finally, the Mt. Rainier case study shows

an innovative way to collect data on visitors in a

challenging situation.

The case studies included in this chapter suggest

that there is reason to be enthusiastic about the

potential of computer simulation modeling as a visitor

management tool. However, this enthusiasm must be

tempered with appropriate realism and caution. The

application of simulation to outdoor recreation issues

is still in its infancy, and there is much need for more

learning and development. As with so many things,
18 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

the more we learn, the more we recognize the need to

know even more. The models presented in the case

studies have only been partially validated. Simulation

outputs have not been statistically compared to realworld observations. Numerical estimates and predictions are presented without numerical estimates of

how much confidence one should have in these metrics.

More work is needed to ensure that the models developed are as valid as possible, simulation runs are

conducted correctly and outputs are appropriately

interpreted. However, these case studies demonstrate

the useful outputs that valid models can produce.

Recreation Visitation and Impacts in

the Bighorn Crags Portion of the

Frank Church—River of No Return

Wilderness _____________________

Randy Gimblett

Suzanne Cable

David N. Cole

Robert M. Itami

Purpose

This case study demonstrates the use of agent-based

modeling and simulation to describe recreation use

patterns in a popular portion of the Frank Church—

River of No Return Wilderness in central Idaho. The

case study will demonstrate the data collection methods, modeling, and simulation of backpackers and recreational stock users on multiple day trips. Particular

attention is given to estimating encounter rates in the

interior of this wilderness because encounter rates can

affect the experience of wilderness visitors. Consequently, wilderness managers commonly want to monitor encounter rates and frequently develop standards

for maximum acceptable number of encounters.

Study Area

The Frank Church—River of No Return Wilderness

Area is the largest contiguous wilderness area in the

United States, outside of Alaska. The most popular

portion of this wilderness for backpackers is an area

known as the Bighorn Crags. The area also receives

substantial use by groups traveling with pack and saddle

stock. Rugged and remote, this country offers adventure,

solitude, and breathtaking scenery. Like other popular

wilderness areas, physical impacts from dispersed visitor use are evident throughout the area, and social

impacts to visitor experiences are likely, but currently

not well documented. To develop the information needed

to better manage recreation in the Bighorn Crags, we

conducted inventories of all recreation impacts in the

Crags (official trails, user-built trails, and campsites),

and collected the data needed to build a computer simulation of the distribution of recreation use.

Data Collection Procedures

To build the computer simulation, data were collected on both visitor demographics and site characteristics. Site characteristics include a map of travel

networks and popular destinations.

Visitor Characteristics—Trip diaries were used to

collect the data on visitor itineraries needed to construct the simulation. Visitors were asked to take a

diary with them and record on one side of the diary their

group size, mode of travel, and date and time of entry

and exit. They were also asked a series of attitudinal

questions about trail, campsite, and management conditions. On the other side of the diary, a map was

provided so a group could record route information.

Specific instructions for the map were as follows:

Please indicate on the map where you camp, the

number and type of encounter(s) you have and a notation

at the edge of the map anytime you leave and re-enter the

wilderness area. Please locate each of the campsites you

visit as accurately as possible on the map. Place a ‘C’

beside the campsite and a number that indicates the

night of the trip that you camped at that location (Example C2 denotes the place where you camped on the 2nd

night of trip). In addition to camp locations we would like

you to indicate the number and type of encounters you

have with other parties throughout your trip. Place an ‘E’

to mark any encounters you have along the trail as they

occur or while at camp at the end of the day. Associated

with the ‘E’, provide one or more of the following notations

to denote the type of encounter(s) you had, ‘O’ (Other

Party Camping), ‘P’ (Packstock) or ‘B’ (Other Backpacker)

followed by a number which indicates the number of

people in the group encountered. (Example EP10 means

encounter with a packstock group of 10 people).

To create trip itineraries in the format required by

RBSim, all spatial and relational data from the diary

were entered into an Access database via a Web-based

interface developed specifically to enter both types of

data. Figure 1 provides an example of this interface and

one of the trips that was entered into the database. This

was a 4-day trip into the area. C1, C2, and C3 were the

three locations where the group camped, and DH represents day hikes taken from each of the campsites. To

represent this trip in RBSim, data were transformed

into a sequence of travel routes and destinations (generically referred to as links and nodes). The sequence

was determined by the number on the left side of the

destination notation. For example, the trip in figure 1

camped the first night at Harbor Lake. The next day

began with a day hike to Bird Bill Lake, followed by a

backpack to and camping at Sky High Lake. The next

day began with a day hike to Terrace Lakes, followed by

a backpack to and camping the third night at Reflection

Lake. The fourth day began with a day hike to Lost Lake
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 19

Figure 1—Interface for capturing spatial data about the trip.

and then a return to the trail head. No other visitors

were encountered on the trip. This sequence of links,

nodes, and durations at campsites are conformed into a

data sequence that RBSim reads and uses to replicate

this trip in the trace simulation.

Site Characteristics—Global Positioning System

(GPS) tracks were used to inventory trails and campsites to create a travel route and destination network.

All trails in the study area were inventoried during the

summer of 2003. Link data were then converted into

ArcView format, and then converted into a format that

conforms to standards established for RBSim. In some

cases, it was clear that recreationists traveled offtrail

between destinations. These informal routes were

digitized and represented as part of the trail network.

Campsite inventory data were also collected in the

field. This consisted of creating a photographic record

of the sites, using a GPS to locate recreation sites, and

taking quantitative measures of variables such as soil

erosion, vegetative cover, and amount and type of

disturbance (Cole 1989). Similar to the trail data,

campsite and trailhead nodes were inserted into the

ArcView map. Since most of these campsites were

situated beside lakes, a single node was established

for each lake, and all campsites at the lake were

associated with that node. Consequently, campsite

statistics are provided for lakes rather than individual

campsites. We were not confident that visitors could

identify the exact campsite they used at a lake.

Modeling Characteristics

RBSim, a recreation behavior simulator (Gimblett

and others 2001) was used to describe patterns of

visitor use across the landscape. The modeling approach used in this study is a trace simulation using

baseline fixed itineraries derived from the trip diaries.

A typical trip for the Bighorn Crags simulation is

described by an entry node, an exit node to the network, an arrival curve, a probability distribution of

agent types, a list of destinations, and trip duration.

On execution, the simulator reads all the trip itineraries, schedules all trips, and then an agent representing

the type of trip moves from one destination to the next

across the travel network using the shortest travel

time between the two points. A large set of information

for each agent is collected and stored in an Access

database for later processing.

Simulation Outputs

Simulation output can be used to describe use levels

on trails and at lakes as well as encounter levels on
20 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

trails and at camping areas around lakes. There is

considerable interest in the estimation of encounter

levels because they are a common indicator and are

difficult to measure in the field. To derive encounters

along trails and at campsite destinations, a simulation

was constructed. The simulation was run for a total of

89 days, from July 3 through September 29, 2003. A

total of 75 trips through the Bighorn Crags were

simulated. Average trip length was 3.6 days. Visitors

typically arrived on Friday or Saturday and departed

the wilderness on Sunday or Monday. Peak use occurred in late July and early August. Average group

size was 2.8. Model outputs were visitor use levels and

encounters by trail segment and camping area (lake).

Statistics are the mean of 10 replications of the simulation. Trail encounters varied substantially between

replications, while trail use, camp area use, and campsite encounters did not.

Trail Use—Table 1 shows simulation output for

three different trail segments. Total use is indicated

by the total number of groups that traversed the trail

segment during the simulated sampling period. The

most heavily used trail segments are from the trailhead at Bighorn Crags campground to the trail intersection that branches off to Welcome and Wilson

Lakes. Encounters are recorded when one group overtakes another group in the simulation or where two

groups pass each other in opposite directions. Table 1

shows the total number of encounters that occurred

during the 89 day season, the mean number of encounters per day, as well as the number of days on which

encounters occurred.

There is not a simple linear relationship between

use levels and encounter levels. Nor is there a linear

relationship between the average number of encounters and the days on which encounters occur. This

illustrates the value of the computer simulations.

Segments 64 and 66 have similar encounter levels, but

both use levels and the number of days with encounters are much higher for segment 64. Trail segment 64

is much shorter than segment 66. Apparently, as the

length of trail segments decreases, so does the ratio

between number of encounters and amount of use.

Figure 2 displays the distribution of trail encounters

across the Bighorn Crags. The maximum mean daily

encounter level was just 0.51 encounter per day, and

on many of the trail segments, no encounters occurred.

Table 1—Simulation output on use and encounter levels for selected trail segments.

Trail Total Total Encounters Days with

segment groups encounters per day encounters

61 2 0 0 0

64 104 7 0.08 5.5

66 26 6 0.07 1.4

This suggests that crowding on trails is not a serious

issue in the Bighorn Crags.

Campsite Use

Table 2 provides information about use levels and

encounters between groups at the more popular camping areas in the Bighorn Crags. Camping areas are

entire lakes or other destinations. Ship Island and

Wilson Lakes are the most popular camping destinations in the Bighorn Crags (figure 3). Someone was

camping at these lakes about 30 nights during the 89-

day season. By dividing nights occupied by 89, one can

derive the likelihood of camping with at least one other

group at each camping area.

The simulation also generates output on the total

number of groups that camped in each area over the

season. Dividing total number of groups by 89 generates an estimate of mean groups per night (table 2). If

more than one group is camped in a camping area on

the same night, campsite encounters occur. All groups

camped in the same area were assumed to encounter

all other groups in that area. Encounters are derived

from simulation output as the difference between the

total number of groups that camp in the area and the

number of nights that anyone camps in the area. When

divided by 89, an estimate of mean campsite encounters per night is generated (table 2).

Conclusion

This case study illustrates how a computer simulation model can be used to describe the distribution of

use across a large complex of backcountry trails and

campsites. It can generate estimates of encounter

levels, useful indicators of potential crowding problems that are costly to monitor directly. Simulation

output suggests that crowding in the Bighorn Crags is

typically not a serious problem at present.

References

Cole, D. N. 1989. Wilderness campsite monitoring methods: a

sourcebook. Gen. Tech. Rep. INT-259. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Research

Station. 57 p.

Gimblett, H. R.; Richards, M. T.; Itami, R. M. 2001. RBSim: Geographic simulation of wilderness recreation behavior. Journal of

Forestry. 99(4): 36–42.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 21

Figure 2—Mean trail encounters per day in

the Bighorn Crags over an 89-day season.

Table 2—Simulation output on use and encounters at camping areas.

Nights Likelihood of a Mean groups Mean encounters

Camp area occupied camp encounter per night per night

Ship Island Lake 31 0.35 0.54 0.19

Wilson Lake 29 0.33 0.39 0.07

Airplane Lake 27 0.30 0.30 0

Welcome Lake 24 0.27 0.33 0.05

Reflection Lake 23 0.26 0.33 0.07

Cathedral Lake 18 0.20 0.28 0.08

Birdbill Lake 13 0.15 0.18 0.03

Terrace Lakes 11 0.12 0.15 0.02

Barking Fox Lake 9 0.10 0.11 0.01

Sky High Lake 9 0.10 0.12 0.02

Heart Lake 8 0.09 0.09 0

Wilson Creek—offtrail 6 0.07 0.07 0

South of Cathedral Rock 3 0.03 0.03 0

Cathedral Rock 3 0.03 0.03 0

Gentian Lake 3 0.03 0.03 0

Buck Lake 3 0.03 0.03 0

Ramshorn Lake 2 0.02 0.02 0

Wilson Creek west of Buck Lake 2 0.02 0.02 0
22 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

Recreation Visitation in Misty Fjords

National Monument in the Tongass

National Forest _________________

Randy Gimblett

Robert M. Itami

Suzanne Cable

Purpose

This case study illustrates the utility of agent-based

modeling and simulation in describing recreational

use patterns at Misty Fjords National Monument in

the Tongass National Forest. The case study demonstrates data collection, modeling, and simulation of

multiple travel modes including arrivals and departure of helicopters, floatplanes, and cruise ships on

multiday trips. Particular attention was given to assessing potential impacts on wildlife at certain critical

locations along the coastline.

Figure 3—Nights per year of camping at lakes

in the Bighorn Crags, over an 89-day season.

Study Area

Located in southeast Alaska, Misty Fjords National

Monument is 35 km east of Ketchikan and about 1,100

air km from Seattle. Created in 1978, by Presidential

Proclamation, the Monument encompasses about

920,000 ha within the Tongass National Forest. In

1980, Congress passed the Alaska National Interest

Lands Conservation Act (ANILCA) and designated all

but 60,000 ha as Wilderness. Monument status protects ecological, cultural, geological, historical,

prehistorical, scientific, and wilderness values. Remote

and wild, the Monument is a primarily mountainous,

nearly untouched coastline, characterized by deep saltwater fjords, and is home to mountain goats, brown and

black bears, and a host of fish and marine mammals.

The Behm Canal, a deep, long waterway of the

northeastern Pacific Ocean, leads to the heart of the

Monument. Places such as Walker Cove and Rudyerd

Bay, characterized by rock walls jutting 1,000 m above

the ocean, provide flightseers, cruise ship passengers,
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 23

boaters, and hikers with photographic opportunities.

Commercial services are allowed, under permit, to

provide basic public services in keeping with Monument designations. The Forest Service and the State of

Alaska manage fish and wildlife habitat cooperatively. Very little recreation use actually occurs on the

land due to the steep, inaccessible terrain, but since

there is limited regulation of air or water, encroachment of tourists from anchored ships and commercial

flights on wildlife is of serious concern.

Data Collection Procedures

Data were collected on both visitor demographics

and site characteristics. Site characteristics include a

map of travel networks and popular destinations, as

well as those areas wildlife biologists understand to be

rich estuaries that provide needed forage and protection during particular times of the year for wildlife

populations.

Visitor Characteristics

Trip itineraries for the simulation were constructed

from visitor data acquired from a number of sources

for the 2000 tourist season. These included records

from cruise ship tours, commercial fishing and hunting records, harbor master records of water-based

travel of large cruise ships entering the harbor, and in

particular, privately owned recreational luxury boats

and sailboats. In addition, records were obtained for

floatplane tours and a range of administrative trips

regularly taken by the Forest Service. Information

collected included the type of activity, size of watercraft, name of company, number of visitors on tour,

duration of visit, time of departure from origin of trip,

and time of arrival at selected destinations. Typical

boat, helicopter, and floatplane speeds and common

flying altitudes were also collected. This information

provided enough background to characterize the type

and frequency of trips as well as common destinations.

While trip logs are an excellent source of data to

characterize the trip, many routes can be taken to any

destination. So in addition to trip itinerary data,

travel companies and commercial floatplane operators were asked to map their preferred and typical

travel routes, stopovers at lakes, and any other information that could be used to accurately describe the

pattern of travel. Anecdotal information was also

collected on events that might cause variations in

typical travel routes. For example, travel patterns in

Alaska are frequently dependent on weather.

Site Characteristics

All the mapped travel route data were entered into

a Geographic Information System (GIS) and used to

construct a network of all possible travel routes. A

network was created for water travel (including cruise

ships, privately owned recreational fishing and luxury

boats, sailboats, administrative water craft), floatplanes

(commercial and administrative; fig. 4), and helicopters

(commercial and administrative). These networks included all major destinations identified in the trip logs

and in interviews with tour operators.

Modeling Characteristics

RBSim, a recreation behavior simulator (Gimblett

and others 2001) was used to examine the seasonal

patterns of visitation that occur in Misty Fjords National Monument. The modeling approach used in this

study is a trace simulation using baseline fixed itineraries. Central to the trip planning behavior of a recreation agent is the typical trip. The concept of a typical

trip is based on the premise that visitors have common

patterns of use. For example, day use visitors arriving

during weekdays will have a different arrival pattern,

a different duration of stay, and perhaps a different

pattern of destinations than a traveler arriving on a

weekend or an overnight visitor.

A typical simulated trip has a global trip plan, which

specifies an intended list of destinations the agent

wishes to visit. A global trip plan consists of total trip

duration, an entry node, an arrival time, an exit node

and a sequence of intermediate nodes (destinations)

between the entry and exit nodes. Each destination

node has a visit duration.

The simulator reads all the trip itineraries and

schedules all trips, then moves each agent from one

destination to the next across the travel network using

shortest travel time between the two points. Information for each agent is collected and stored in a database

for processing.

Simulation Outputs and Management

Recommendations

A total of 2,149 trips were entered into the simulation for the 2000 visitor use season for Misty Fjords

National Monument. The simulator produced output

on encounter levels for all destinations on the network. Encounters are the number of other agents

visible within a specified radius of the destination

categorized by craft type. Figure 5 shows total number

of encounters at the 12 most frequently visited destinations. A number of destinations received very little

use, while others would be considered to be high use

destinations. Rudyerd Bay and associated docking

facilities (1,301 combined encounters) are the most

popular destinations in the Monument. Visitation

peaks in August, generating the greatest potential for

conflict between different modes of travel (fig. 6).
24 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

Figure 4—Floatplane network, including destinations for Misty Fjords.

Figure 5—Number of encounters at 12 frequently visited destinations.

Village Island

Rudyerd Bay

Hyder

Pt. Eva

Portage

Mink Bay

Wrangell

Ketchikan

Pt. Alava

Ella Lake

Alava Bay

Pt. Sykes

Wasp Cove Ward Cove Wilson Arm

Grant Cove

Martin Arm

Pt. Whaley

Behm Canal

Kah Shakes

King Creek

Anchor Pass

Bold Island

Walker Lake

Walker Cove

Mirror Lake

Sargent Bay

Skull Creek Narrow Pass

Halibut Bay

Clear Creek

Edward Pass

Hidden Inlet

Black Island

Verdue Point

Wilson River

Manzanita Bay

Fillmore Lake

Humpback Creek

Fillmore Inlet

Channel Island

Wilson Narrows

Chickamin River

Hugh Smith Lake

Unuk River Post

Valentine Creek

Rudyerd Bay South

Tombstone Rv-Hyder

New Eddystone Rock

Unuk River - mouth

0

100

200

300

400

500

600

700

800

900

Alava Bay

Big Goat

Lake

Hyder

Ketchikan

Manzoni

Lake

Pt.. Alava

Punchbowl

Lake

Punchbowl

Cove

Rudyerd

Dock

Rudyerd

Bay

Unuk River

 - Mouth 

Walker

Cove

Destinations

Number of Encounters
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 25

Figure 6—Monthly use levels for different user types.

To gain insight into potential conflict between recreation and wildlife, a more detailed analysis was performed at both Walker Cove and Rudyerd Bay. Monitoring agents were placed at both areas and the

simulations rerun for the month of August (peak

period of visitation) to determine the temporal distribution of use. Wildlife disturbance can be responsive

to temporal use distributions. We were interested in

peak use days in August, as well as the hours during

those days that visitors are within sight of wildlife.

At Rudyerd Bay, visitation varied from day to day,

increasing slightly as the month progressed (fig. 7).

The number of fixed-wing aircraft frequenting the

area was lower than expected. The hourly assessment

revealed a cyclical pattern of visitation over the period

of a day (fig. 8) with the number of ships increasing

during the evening hours. Many of these cruise ships

anchor in the cove at night and then leave early in the

morning (between 4:00 and 10:00 a.m.).

These daily and hourly patterns of visitation reveal

that there is very little time during the month of

August when there is not a cruise ship or commercial

floatplane in Rudyerd Bay.

Conclusion

This case study illustrates the application of computer simulation modeling in a marine environment

used by many different types of users. The spatial and

temporal explicitness of outputs is helpful in assessing

management concerns about the potential for impacts

on known concentrations of waterfowl, shorebirds,

and harbor seals. Such impacts are highly space and

time dependent.

References

Gimblett, H. R.; Richards, M. T.; Itami, R. M. 2001. RBSim: Geographic simulation of wilderness recreation behavior. Journal of

Forestry. 99: 36–42.

0

10

20

30

40

50

60

70

10 11 12

Month

Number of Visitors

Cruise Ship

fixed-wing

aircraft

Boat

displacement

hull

Kayaks

Boat planning

hull

4 6789 5
26 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

Figure 8—Hourly encounters, for sea kayaks, cruise ships, and fixed-wing aircraft, at North

Arm of Rudyerd Bay, August 2000.

Figure 7—Daily encounters, for cruise ships and fixed-wing aircraft, at North Arm of Rudyerd Bay,

August 2000.

0

5

10

15

20

25

1 3 11 13 15 17 19 21 23

Hour

Number of Encounters

Sea Kayaks

Cruise Ship

fixed-wing

aircraft

579

0

1

2

3

4

5

6

7

8

9

10

8/1/20008/2/20008/3/20008/4/20008/5/20008/6/20008/7/20008/8/20008/9/2000

8/10/20008/11/20008/12/20008/13/20008/14/20008/15/20008/16/20008/17/20008/18/20008/19/20008/20/20008/21/20008/22/20008/23/20008/24/20008/25/20008/26/20008/27/20008/28/20008/29/20008/30/20008/31/2000

Date

Number of Encounters

Cruise Ship

fixed-wing

aircraft
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 27

Simulation of Recreation Use Along

the Colorado River in Grand Canyon

National Park ___________________

Randy Gimblett

Purpose

The purpose of this case study is to demonstrate the

use of agent-based modeling and simulation of

multiday river trips along the Colorado River, trips

that are in high demand and for which crowding is a

major concern. Model outputs not only illustrate use

patterns along the river, but also predict how modifications of trip scheduling can affect use distribution

and crowding.

Study Area

The increasing popularity of whitewater trips on

the Colorado River through Grand Canyon National

Park threaten to impact sensitive, dynamic ecosystems and degrade the quality of experience of human

visitors. Over 23,000 individuals and another 3,700

guides, researchers, and park staff travel through

the Grand Canyon each year. Visitors travel on over

600 commercial or privately organized river trips on

a variety of watercraft powered by oars, paddles, or

motors. Trips range from 6 to 18 days (to Diamond

Creek) in the primary use season, and up to 30 days

at other times. Because of fluctuating water levels

imposed on the river from Glen Canyon dam, trip

speed can vary substantially through particular

reaches of the river. When water levels are low,

progress is slower (especially for oar-powered trips),

and lost time is usually made up by skipping or

reducing the time allocated to attraction sites and/or

by spending more time rowing down the river. High

water results in faster progress, which usually translates to more stops and longer times at both attractions and campsites, resulting in more crowding and

greater environmental impacts.

Many of the major drainages and side canyons

along the 450-km river corridor in Grand Canyon

National Park provide opportunities for hiking and

swimming. Well-known attractions and destinations

are regular stops for nearly every river trip that

passes through the canyon. Crowding and encounter

levels along the river at attraction sites are often

extreme and have been shown to affect the character

and quality of the visitor experience (Shelby and

Nielsen 1976). Simulation modeling was proposed as

a means of better describing existing use patterns

along the river, as well as to predict whether changes

in launch schedules could be used to decrease crowding on the river.

Data Collection Procedures

Data were collected on both visitor characteristics

and site characteristics. Site characteristics include

river travel networks and popular destinations.

Visitor Characteristics

In 1998 and 1999, hundreds of trip leaders from

private and commercial trips were asked to complete

trip reports (fig. 9). These trip reports asked the trip

leader to record the time in and time out for every

reasonable day or overnight stop for some 250 designated sites between the launching spot at Lees Ferry

and Diamond Creek. Other information collected to

characterize the trips included: (1) river flow rates

during the trip, (2) type and size of craft, and whether

oar or motor power, (3) number of passengers on the

craft, (4) experience of the guide, (5) number of encounters with other parties, and (6) planned or actual time

allocated to specific sightseeing, rest, and camping

stops during the trip.

Approximately 500 trip diaries were collected, representing about a 50 percent return rate for the commercial trips and a 30 percent return rate for the

private trips. The trip diaries represent trips of all

lengths and propulsion types (motorized, non-motorized). Itineraries were created from each of the diaries

returned. The trip itineraries were subsequently used

to simulate individual trips moving down the river,

stopping at and starting from designated sites.

In addition, 15 river guides were interviewed. These

guides collectively represented many years of experience running the Colorado River either non-commercially (privately) or as guides for commercial outfitters. They had experience at various river flows and

with all types of watercraft (oars, paddle boats, dories,

and motor boats). The intent of the interviews was to

learn as much as possible about the logic employed by

a river guide when taking a trip down the Colorado

River. Questions were open-ended. For example, to

understand how a guide might choose a campsite we

asked questions such as, “when do you start thinking

about camping for the evening?”, “what campsites do

you like and why?”, “which campsites do you try to

avoid and why?”, and “what factors go into the process

of choosing a campsite, and explain why each factor is

important?” These insights were used to create a list of

possible reasons regarding whether or not a campsite

was likely to be selected.

Site Characteristics

Data for the Colorado River were entered into a

Geographic Information System (GIS) and used to

construct a network for water travel on the river. The

network included launch and take out sites, major
28 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

Figure 9—A portion of the trip diary used to collect spatial and temporal trip

information.

rapids, and attraction sites. In addition, data on over

200 beach sites, obtained from the Grand Canyon

National Park, were included. Beach site data included information on beach size at high water, estimated campsite capacity, and location by river mile

and right or left side of the river.

Modeling Characteristics

The Grand Canyon River Trip Simulator (GCRTS)

used a trace and terminating simulation approach

(Daniel and Gimblett 2000) (refer to chapter 3 for more

detail). Central to the trip planning behavior of a

recreation agent is the typical trip. The concept of a

typical trip is based on the premise that visitors have

common patterns of use. A typical trip has a global trip

plan, which specifies an intended list of destinations

the agent wishes to visit in a given sequence, and

durations. A global trip plan consists of a total trip

duration, an entry node, an arrival time, an exit node,

and a sequence of intermediate nodes (destinations)

between the entry and exit nodes. Each destination

node has a visit duration.

While this is a trace simulation, there is minimal

decisionmaking the agents must do when confronted

with certain situations on the river. While their trip

plan specifies that they are to travel to a specific

campsite, that campsite might be full that night. In

this case, the agent might choose to move to the next

unoccupied site. The simulation uses elements of fuzzy

logic in the decision structure to weigh factors or

variables to provide the agent with some autonomy in

adaptive decisionmaking. For example, when the agent

is choosing a campsite, the current conditions of the

river and the individual trip play a role, as does the

historical popularity of a campsite under consideration.

This fuzzy logic approach takes into account all these

factors and weighs them appropriately so that each

agent’s campsite decision represents a reasonable outcome for that given, particular set of circumstances.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 29

Figure 10—Number of commercial trips per year that stop at selected sites along the Colorado River.

On execution, the simulation engine reads in a launch

schedule (either the current launch schedule or a prospective calendar created by the user), then it creates

and launches the trips from Lees Ferry. These simulated trips execute their days on the river by choosing

attraction sites for hikes or other activities, by stopping

for lunch, and by selecting an appropriate campsite

each night. Certain trips must be at given locations on

certain times (for example, some trips exchange passengers at Phantom Ranch). The trips are managed by

the simulator to meet these fixed points as scheduled.

Moreover, a planning algorithm helps each simulated

trip plan out an optimal schedule that will, for example,

include stops at the key attraction sites and ensure that

campsite selections are appropriate. A record is kept for

each simulated trip—where and when it encounters

other trips, where it chooses to engage in an activity or

to stop to camp, how long it stays, and so on. A large set

of information for each trip is collected and stored in a

database for processing.

Simulation Outputs

Figure 10 shows the popularity of selected attraction

sites along the river with commercial groups. Similar

outputs can be produced for noncommercial groups, as

well as for campsites along the river. None of the sites

along the river are used more than 60 nights during

the primary use season.

To explore how different management options might

affect crowding levels, a scenario was developed to test

how changes in the launch schedule would affect the

distribution of visitors and number of contacts along the

Colorado River. Current management policy directive

states that during the primary use season there should

be an 80 percent probability that a party will make

contact with seven parties or less per day. There should

be an 80 percent probability that contacts will not

exceed 90 minutes per day and 125 people per day. A

baseline simulation was run using the data collected on

river trips launching between July 1, 1998, and August

31, 1998 (peak season), and tested against a second

simulation where launch restrictions were imposed on

the trips. All original trips that launched before noon

were randomly assigned a time before 10:00 a.m. and all

trips that previously had launched after 12:00 were

randomly assigned a launch time after 2:00 p.m. We

wanted to test whether this fixed launch schedule

would more evenly disperse trips along the river, leading to an increase in the percentage of trips that met

management objectives for contacts.

Activity Site Visits for Commercial Trips Along the Colorado 

River - Grand Canyon 1998

0

10

20

30

40

50

60

11 R, SOAP

19.9 L, 20 MILE

23.7 L, LONE

33.6 L, BELOW

39 R, REDBUD

43.8 L, PRES

51.2 L, 

53.2 R, LWR

59 R, BELOW

65.5 R, LAVA

71 L,

75.6 L,

84.4 L, ABOVE92.3 L, ABOVE

96.1 L, SCHIST

102.8 R, NEW

109.4 R, 110

119.2 R,

120.1 R, LWR

124.3 L, ABOVE

131.8 R,133.9 R,

136.3 L, OC'S

138.4 L, 

144.2 R,

148.5 L,

158.2 R, 158

164.8 L,

168 R, FERN

174.4 R, LWR

179.7 R,182.8 R,

185.5 R,

188.2 R, LWR

194.1 L,

206.6 R, INDIAN

211.5 R, UPR

219.9 R,

223.4 L, 224-

235 L, BRIDGE

241.5 L, 

Commercial

Trips
30 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

Figure 11—Number of contacts per river mile for scenarios with and without launch restrictions.

Of the trips launched between July 1, 1998, and

August 31, 1998, only 9 percent actually met the

management objective of coming into contact with

seven or fewer parties, and 14 percent of those trips

exceeded the maximum duration of contact. Altering

the launch schedule to impose restrictions after early

morning and not allowing departures until early afternoon on a daily basis during the same time period did

improve the situation: 24 percent of trips met the

management objective of coming into contact with

seven or fewer parties, with only 8 percent of those

trips exceeding the maximum duration of contact.

Comparing the two simulations illustrates that, by

imposing restrictions on launch times, managers can

increase the number of trips that meet management

objectives.

Figure 11 shows the total number of river contacts

by river mile for both simulation runs for the period

July 1, 1998, through August 31, 1998. This provides

the manager with an estimate of the total number of

the contacts and where those contacts occur. Imposing

launch restrictions reduces the total number of contacts along the river. At miles 34, 58.6, and 160, imposing launch restrictions reduces the number of river

contacts by nearly half. Imposing launch restrictions

has a surprising effect on river contacts in the middle

section of the river, but has little effect towards the end

of the trip.

More work still needs to be done to calibrate and

validate the model. However, as this case study suggests, trace, fixed itinerary, terminating simulations

as described in this paper, can provide useful information for park managers on the spatial and dynamic

distribution of river trips. Simulating baseline conditions and comparing them to simulated scenarios can

provide ways to test and derive more meaningful

management objectives and standards for visitor contacts and capacity. Altering launch schedules, discontinuing exchanges, and limiting use at camp and

activity sites are all conscious management actions,

one of which has been shown here to significantly

reduce numbers of contacts.

References

Daniel, T. C.; Gimblett, H. R. 2000. Autonomous agent model to

support river trip management decisions in Grand Canyon National Park. International Journal of Wilderness. 6(3): 39–42.

Shelby, Bo; Nielsen, J. M. 1976. Use levels and crowding in the

Grand Canyon. Tech. Rep. 3. Colorado River Research Program

Report Series. Washington, DC: U.S. Department of the Interior,

National Park Service.

0

10

20

30

40

50

60

70

08

27.230.4

34

40.7

41

47.358.661.565.767.574.387.8

103108121131136145157160183186204210220223233246256277

River Mile

Total Number of River Contacts During Peak 

Season

No

Restrictions

Launch

Restrictions
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 31

John Muir Wilderness: Describing

the Spatial Distribution of

Backpacking Use on a System

of Trails and Campsites __________

Steven R. Lawson

Robert M. Itami

Randy Gimblett

Robert E. Manning

Purpose

This case study demonstrates the utility of a computer simulation model in describing the spatial distribution of visitor use and encounters between groups

in a dispersed recreation setting. A simulation model

of backcountry hiking and camping use was developed

for a portion of the John Muir Wilderness in the Inyo

National Forest, California (Lawson and others 2004).

The John Muir Wilderness covers 235,000 ha in the

Sierra Nevada Mountains. The area is characterized

by snowcapped mountains with hundreds of lakes and

streams and lush meadows. The simulation model

estimated hiking and camping use and encounters by

trail segment and camping location. Outputs from the

simulation model were imported into a Geographic

Information System (GIS) database and mapped. This

case study should be considered exploratory and illustrative. There were weaknesses in the data collection

phase that suggest results do not accurately represent

current use distribution in the study area. For example, response rate to the visitor survey was low, and

respondents were not necessarily representative of all

visitors to the area. However, since these weaknesses

can be easily corrected, the study illustrates the potential value of this tool.

Data Collection

Visitor Characteristics—During the 1999 visitor

use season, diary-like questionnaires were distributed to backcountry visitors in a portion of the John

Muir Wilderness. Questionnaires were distributed at

trailhead self-registration stations and at ranger stations when visitors picked up their agency-issued

permit. Randomly selected self-registration stations

were periodically attended by data collectors who

distributed diaries to visitor groups and collected

completed questionnaires from groups as they finished their trips. In addition, questionnaires were

distributed by commercial packstock outfitters, following instructions given by the research team.

The diary-like questionnaire included a series of

questions concerning group and trip characteristics

and a map of trails and natural features. Respondents

were instructed to record their route of travel during

their visit, including the trailhead(s) where they started

and ended their trip, and their camping location on

each night. Respondents were also asked to report the

duration of their visit, the number of people in their

party, and their mode of travel. The response rate was

32 percent, resulting in a total of 324 competed diaries.

The low response rate is a primary reason we question

the validity of model outputs for other than illustrative purposes.

Site Characteristics—Data on the trail network

within the study area were provided by the Inyo

National Forest as a GIS overlay. The data included

all trail segments and intersections within the study

area. These data were supplemented with information

from a campground inventory completed in the summers of 1999 and 2000. Campsite “clusters” were

created from the visitor surveys by grouping camping

locations based on proximity and common access. A

single campsite cluster was comprised of all reported

camping locations that were within a subjectively

determined reasonable distance of each other. The

campsite clusters were used to determine camping

encounters within the travel simulation model. Specifically, groups camping at locations within the same

campsite cluster were considered to be within close

enough proximity to be within sight and/or sound of

each other.

Travel Simulation Model Design and

Analysis

The travel simulation model was developed using

the commercially available general-purpose simulation software, Extend, developed by Imagine That,

Inc., and is a probabilistic, steady-state simulation

(see Chapter 3 for information about probabilistic and

steady-state simulations). Two simplifying assumptions worth noting were built into the model. First,

only data for overnight hikers were included in the

model; packstock trips and day trips were excluded.

Second, the travel simulation model was only applied

to a small portion of the larger study area. The section

of the study area for which the model was developed is

referred to as the Desolation Lake Locale (fig. 12).

The majority of visitor use occurs during the summer months. Therefore, the computer simulation model

was designed to focus on the “peak” period of the

visitor use season, July 1 through September 30, 1999.

The simplifying assumptions described in the previous paragraph, coupled with the decision to focus on

the summer months of the visitor use season, resulted

in a total of 190 usable trip itineraries included as

inputs into the travel simulation model.

Outputs—The simulation model was designed to

generate four outputs concerning visitor use densities
32 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

Figure 12—Larger study area, with the Desolation Lake locale inside the box.

and hiking and camping encounters. First, average

hiking use per day is calculated for each trail segment

by summing the number of groups that pass through

each trail segment during the course of the simulation

and dividing by the total number of days simulated.

Second, average hiking encounters per group per

day are calculated for each trail segment on each day

that at least one group passes along the trail segment.

Two types of hiking encounters were calculated

(Schechter and Lucas 1978). “Overtaking encounters”

are defined as one group passing another group while

traveling in the same direction along the trail while

“Meeting encounters” are defined as two groups passing each other while traveling in opposite directions.

The average number of hiking encounters per group

per day is calculated for each trail segment by summing the total number of hiking encounters along the

trail segment throughout the simulation and dividing

by the total number of groups that hiked the trail

segment during the simulation.

Third, average camping use per night is calculated

for each campsite cluster by summing the number of

groups that camp at the campsite cluster during the

course of the simulation and dividing by the total

number of nights simulated. Fourth, average camping

encounters per group per night are calculated for each

night that a campsite cluster is occupied by one or

more parties. The number of camping encounters a

group has on a given night is equal to the number of

other groups camping in the same campsite cluster on

the same simulated night. The average number of

camping encounters per group per night is calculated

for each campsite cluster by summing the total number of campsite encounters throughout the simulation

and dividing by the total number of groups that camped

at the campsite cluster during the simulation.

Baseline Simulation—The simulation reported

here was designed to generate the outputs described

above for baseline visitor use levels and existing

management practices, where the baseline level of

visitor use is assumed to be equal to the number of

groups that completed the diary questionnaire during

the sampling period. The reader is cautioned that only

about one-third of the groups completed the questionnaires. Therefore, the outputs reported underestimate

use substantially—perhaps by a factor of about three.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 33

Model Verification—Several model verification

techniques were conducted to ensure that the simulation model was implemented correctly. First, individual components of the simulation model were developed and debugged to ensure that they functioned

properly. Modules for the campsite clusters, trail segments, trail junctions, and trailheads were developed

and tested individually before being combined in the

full model. Second, the authors conducted a structured

“walk-through” of the simulation model to review the

logic and operation of the simulation model. Third,

Extend’s built-in debugging functions were used to

detect and correct data coding errors in the full model

that resulted in incorrect routing of simulated trips.

Fourth, Extend’s animation function was used to visually verify that the model did not contain errors. Fifth,

the trip itinerary data reported by respondents to the

diary questionnaire were used as the basis for a

quantitative verification technique. Specifically, the

proportion of camping use for each campsite cluster

was calculated for the diary trip itinerary data and

compared to the proportion of camping use for each

campsite cluster. This technique provided a method

for comparing the output of the model with known

analytical results (Schecter and Lucas 1978).

The most powerful technique for validating a computer simulation model is to compare model output

data to data from the actual system the model is

designed to replicate (Law and Kelton 2000). Due to a

lack of data from the actual system (for example,

hiking encounters per group and hiking use per trailhead in the area), this quantitative technique could

not be used to assess the validity of the computer

simulation model. However, validation techniques

based on intuitive judgement were used to assess the

content and face validity of the simulation model by

examining the reasonableness of the inputs and outputs. Furthermore, sensitivity analyses were conducted to test whether the model outputs change in

the expected direction in response to changes in selected input parameters (for example, total use).

Results

Table 3 reports mean camping use per night and

mean camping encounters per group per night for each

campsite cluster. The map in figure 13 portrays the

spatial distribution of camping use within the study

area. Table 4 reports mean hiking use per day and

mean hiking encounters per group per day, by trail

segment. The map in figure 14 portrays the spatial

distribution of hiking use within the study area.

The results reported in table 5 suggest that the

computer simulation model has been constructed

correctly and that operating errors have been eliminated through the debugging and verification techniques described earlier in this paper. Specifically, as

Table 3—Mean camping use and encounters, by campsite

cluster.

Mean number of Mean camping

Campsite camping groups encounters per

clusters per night group per night

7 0.86 0.90

36 0.12 0.14

37 0.74 0.75

38 0.05 0.06

39 0.15 0.12

40 0.05 0.03

41 0.26 0.22

42 0.32 0.33

44 0.44 0.43

45 0.13 0.12

46 0.48 0.51

47 0.31 0.25

48 0.14 0.14

49 0.04 0.00

50 0.12 0.15

51 0.07 0.04

52 0.02 0.00

53 0.04 0.10

56 0.10 0.09

57 0.14 0.13

80 0.11 0.09

81 0.07 0.02

indicated in table 5, there is no substantive difference

between the distribution of campsite cluster use

reported in the diary survey and the simulated trips.

Due to the low response rate to the questionnaire,

the content validity of the model, which is concerned

with the reasonableness of the model inputs, is low.

Furthermore, statistical comparisons of model outputs to actual system data are not possible due to a

lack of “ground-truthing” data. However, results of a

series of simulations conducted with the model support the internal and face validity of the model. Specifically, as the total simulated use of the study area is

“ramped up,” estimates of hiking and camping use and

encounters increase or remain unchanged for all trail

segments and campsite clusters, respectively.

Conclusions

This study illustrates the potential usefulness of

computer-based simulation modeling in describing

the spatial distribution of recreational use in backcountry and wilderness landscapes. Dispersed recreation in such areas is inherently difficult to observe

directly. However, the study findings suggest that by

collecting representative data by means of trailhead

counts and a diary survey of a sample of visitor groups,

it is possible to estimate levels and patterns of visitor

use. The model can also estimate encounters between
34 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

Figure 13—Use levels of campsite clusters in the Desolation Lake

locale.

Figure 14—Use levels of trail

segments in the Desolation Lake

locale.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 35

Table 4—Mean hiking use and encounters, by trail segment.

Mean number of Mean hiking

Campsite hiking groups encounters per

clusters per day group per day

2 3.51 0.20

3 0.08 0.00

4 3.51 0.11

5 3.43 0.34

6 0.58 0.03

7 0.14 0.03

8 0.04 0.00

9 3.35 0.11

10 3.28 0.10

11 3.20 0.05

12 0.12 0.00

13 0.20 0.01

14 0.80 0.04

15 2.95 0.20

16 1.10 0.02

17 2.47 0.11

18 2.41 0.05

19 0.15 0.01

20 0.99 0.01

21 0.90 0.03

22 0.77 0.06

23 0.09 0.00

24 0.13 0.01

25 2.31 0.07

26 0.15 0.02

27 1.08 0.06

28 0.15 0.01

29 0.45 0.02

30 1.29 0.01

31 0.68 0.03

32 0.63 0.05

33 0.04 0.00

34 1.87 0.09

35 0.07 0.00

36 1.43 0.08

37 0.29 0.02

38 0.88 0.06

39 1.29 0.21

40 0.22 0.01

41 1.25 0.07

132 0.06 0.00

Table 5—Percentage of total camping use in each cluster

based on survey data and model output.

Campsite cluster Survey data Model output

7 0.18 0.18

36 0.02 0.02

37 0.16 0.16

38 0.01 0.01

39 0.03 0.03

40 0.01 0.01

41 0.05 0.05

42 0.07 0.08

44 0.09 0.10

45 0.04 0.03

46 0.10 0.10

47 0.06 0.06

48 0.03 0.03

49 0.01 0.01

50 0.03 0.02

51 0.01 0.01

52 0.00 0.00

53 0.01 0.01

56 0.02 0.02

57 0.03 0.03

80 0.02 0.02

81 0.01 0.01

groups, which is particularly difficult to monitor directly. This information can be used for several purposes, including monitoring indicator variables to ensure that standards are not violated, identifying

potential bottlenecks or congested sites, scheduling

maintenance and patrol activities, and educating visitors about the conditions they are likely to experience.

Finally, the results of this study demonstrate that,

by integrating computer simulation and GIS technologies, it is possible to map existing recreational use

patterns. This capability provides managers with a

tool to communicate more effectively with the public

regarding existing visitor use management issues. By

importing computer simulation results into a GIS

database, it is possible to conduct overlay analyses

with resource data to examine relationships between

natural resource characteristics (for example, resource

fragility, resource impacts, and so forth) and existing

visitor use patterns. Furthermore, integrating GIS

and computer simulation technologies provides managers with a means to illustrate with maps the potential effect of alternative visitor use policy decisions and

management practices on visitor use patterns and

natural resources within a dispersed, backcountry

setting.

References

Law, A.; Kelton, W. 2000. Simulation modeling and analysis. 3d ed.

Boston, MA: McGraw Hill.

Lawson, S.; Itami, B.; Gimblett, R.; Manning, R. 2004. Monitoring

and managing recreational use in backcountry landscapes using

computer-based simulation modeling. In: Policies, methods and

tools for visitor management: proceedings of the second international conference on monitoring and management of visitor flows

in recreational and protected areas. Rovaniemi, Finland: Finnish

Forest Research Institute: 107–113.

Shechter, M.; Lucas, R. 1978. Simulation of recreational use for park

and wilderness management. Washington, DC: Resources for the

Future.
36 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

Figure 15—Study photograph of the trail to

Yosemite Falls, representative of moderate

use levels.

Frontcountry Trails and Attraction

Sites in Yosemite National Park:

Estimating the Maximum Use That

Can Be Accommodated Without

Violating Standards of Quality _____

Robert E. Manning

William A. Valliere

Benjamin Wang

Steven R. Lawson

Peter Newman

Purpose

Many park managers are concerned about overuse

of trails and attraction sites resulting in low quality

visitor experiences. Using contemporary planning

frameworks, such as Limits of Acceptable Change

and Visitor Experience and Resource Protection, they

often formulate indicators and standards of quality.

Indicators of quality are measurable, manageable

variables that reflect the quality of the visitor experience. Standards of quality define the minimum

acceptable conditions of indicator variables. Once

indicators and standards of quality have been formulated, management actions can be implemented to

ensure that standards of quality are maintained.

Computer simulation modeling can be used to estimate the maximum level of visitor use that can be

accommodated along trails and at attraction sites

without violating crowding-related standards of quality. This case study provides an illustration of the

utility of computer simulation modeling for this purpose at heavily used frontcountry trails and attraction sites in Yosemite National Park (Manning and

others 2003).

Description of Study Area

Yosemite Valley is the scenic heart of Yosemite

National Park, arguably the first National Park in the

United States, and certainly one of America’s best

known and most popular National Parks. Yosemite

Valley is a glacially carved area of approximately 20

km2 and features sheer granite walls of up to 5,000 feet

and several of the world’s highest waterfalls. Yosemite

National Park draws over 4 million visits annually,

and carrying capacity has been a long-standing and

controversial issue. Several specific visitor attraction

sites were chosen for this study: the trail to Vernal

Fall, Yosemite Falls (including the base of the falls and

the trail leading to it), Bridalveil Fall (including the

base of the fall and the trail leading to it), Glacier

Point, and the trail to Mirror Lake.

Potential Standards

The Yosemite Valley project involved two phases. The

first used a visitor survey to generate a range of potential

standards for maximum people-at-one-time (PAOT) at

attraction sites and persons-per-viewscape (PPV) along

trails. Using computer-edited photographs illustrating a

range of PAOT and PPV at study sites, respondents were

asked about the level of use they (1) prefer, (2) consider

acceptable, (3) consider so unacceptable they would no

longer visit, and (4) think the National Park Service

should allow (fig. 15). Mean responses for each of these

four evaluative domains (denoted as preference, acceptability, tolerance, and management action, respectively)

provide a range of potential standards from the perspective of current visitors. Reponses were compiled for

visitors on four trail segments and at three popular

viewpoints (table 6).

Computer Simulation Modeling

The second phase, the primary subject of this case

study, involved using computer simulation to predict

maximum use levels that could be sustained without

violating these potential standards. For this purpose,

detailed counts and observations of visitor use were

collected at each of the seven study sites. Variables

included length of trails, length of typical trail

viewscapes (how far along the trail a hiker can typically see), number of visitors arriving per hour, visitor

group size, length of time visitors stop at attractions,

and the speed at which visitors hike trails.

Using the input data described above, simulation

models were developed for each of the study sites. The

models were built using the commercial simulation

package, Extend, by Imagine That, Inc. Extend is an

object-oriented, dynamic simulation package that has
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 37

Table 6—Alternative crowding-related standards of quality for all study sites.

Trail to Trail to Base of Trail to Base of Glacier Trail to

Normative standard Vernal Fall Yosemite Falls Yosemite Falls Bridalveil Fall Bridalveil Fall Point Mirror Lake

of quality (PPV) (PPV) (PAOT) (PPV) (PAOT) (PAOT) (PPV)

Preference 11 18 43 7 8 19 10

Acceptability 26 40 92 18 20 42 24

Management action 30 46 100 20 19 49 26

Tolerance 39 60 126 26 25 61 34

Figure 16—Schematic diagram of computer simulation model of Glacier Point.

been used extensively in business, manufacturing, and

electronics applications to improve quality and efficiency. The object orientation makes code-writing unnecessary, and the programming algorithm can easily

be expressed by the graphic display of objects and

connections. For example, figure 16 shows the top layer

of the simulation model developed for Glacier Point.

The model was built with a series of “blocks” to represent components of this study site. The block labeled

“Hiker Generator” in the top left section of the model is

where simulated visitors are generated. Connected to it

are groups of blocks that provide data concerning empirical visitor arrival rates and group sizes. (These data

are derived from field observation.) The block “Total

Use” allows the researcher to specify the daily total visit

use level to be simulated. The simulated visitors then

spend a randomly assigned length of time (based on

field observations) at the “Viewing Area” block that

represents the Glacier Point visitor attraction. The

associated blocks (those underneath the Viewing area

block) measure the number of people-at-one-time

(PAOT) at the Glacier Point viewing area. The simulation models developed in this project were probabilistic

(based on probabilities of simulated visitors selecting

available travel itineraries) and were terminating (they

modeled an entire day).

Model output could be generated in several graphic

and numerical forms. For example, figure 17 traces

minute-by-minute PPV levels along the trail to

Bridalveil Fall over the duration of a day. This particular model run was generated using an average summer day total use level of 1,415 visitors (derived from

the counts of visitor use taken to help construct the

model). To predict the maximum total daily use level

that could be accommodated at each study site and

each evaluative domain (preference, acceptability,
38 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

Figure 17—Person-per-viewscape (PPV) along the trail to Bridalveil Fall.

management action, and tolerance), models were run

multiple times. If standards were violated, models

were rerun at successively lower use levels until standards were no longer violated. In addition, all models

were run multiple times to “average out” the randomness associated with each individual model run. The

range of estimated daily maximum use levels for each

study site and evaluative domain is shown in Table 7.

Conclusion

This project illustrates the utility of computer simulation modeling related to planning and management

of trails and attraction sites in the front country of

parks. Specifically, it illustrates an important applicaTable 7—Range of daily maximum use levels at all study sitesa

.

Trail to Trail to Base of Trail to Base of Glacier Trail to

Normative standard Vernal Fall Yosemite Falls Yosemite Falls Bridalveil Fall Bridalveil Fall Point Mirror Lake

Preference 2,100 4,500 3,000 1,200 700 1,500 1,800

Acceptability 6,000 9,500 5,500 3,200 1,700 4,000 5,000

Management action 7,000 11,000 5,900 3,500 1,700 4,800 5,500

Tolerance 9,300 13,000 7,300 4,800 2,300 6,300 7,700

a

 Daily maximum use levels were calculated to allow standards to be exceeded a maximum of 10 percent of the time.

tion of modeling for estimating the maximum use

levels that can be sustained without violating standards related to crowding. Planners can use this information to evaluate the consequences of deciding among

alternative standards. Such predictions can also be

linked to transportation planning. For example, once

standards are selected, a visitor transit system could

be designed for Yosemite Valley to deliver the “right”

number of visitors to the “right” places at the “right”

intervals.

References

Manning, R.; Valliere, W.; Wang, B.; Lawson, S.; Newman, P. 2003.

Estimating day use social carrying capacity in Yosemite National

Park. Leisure: The Journal of the Canadian Association for

Leisure Studies. 27(1–2): 77–102.

0

5

10

15

20

25

30

35

40

45

Time of Simulation Day

PPV

7:00 am 8:00 pm
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 39

Alcatraz Island: Estimating the

Maximum Use That Can Be

Accommodated Without Violating

Standards of Quality _____________

William A. Valliere

Robert E. Manning

Benjamin Wang

Purpose

The purpose of this case study is to illustrate how a

computer simulation model can be used to estimate

the maximum number of people that can visit Alcatraz

Island without violating crowding-related standards

of quality for the cellhouse on the Island (Manning and

others 2002). The model acts as a translator that takes

simple inputs (such as the number of visitors per ferry

boat servicing the island and the frequency of boat

arrivals) and estimates the persons-at-one-time

(PAOT) on Michigan Avenue (as representative of the

whole cellhouse). This approach provides an efficient

and economical way of monitoring an indicator of

quality to measure against standards of quality.

Description of Study Area

Alcatraz Island is an historic site within Golden

Gate National Recreation Area, a unit of the USDI

National Park System, and is a heavily visited tourist

attraction. The island, located in San Francisco Bay, is

widely known for its history as a Federal prison for

incorrigible criminals. This history has been romanticized and popularized in several books and movies;

consequently, the demand to visit Alcatraz is high.

Visitation now exceeds several hundred thousand

annually, and continues to grow rapidly.

Data Collection Procedures

Data collection consisted of two phases. During the

first phase, visitors were asked questions regarding

the acceptability of different levels of use in the cell

house (fig. 18). Using computer-edited photographs,

respondents were asked about the level of use they

(1) prefer, (2) consider acceptable, and (3) think the

National Park Service should allow. Mean responses

for each of these three evaluative domains (denoted as

preference, acceptability, and management action,

respectively) were 25, 44, and 44 persons-at-one-time

(POT). These values provide a range of potential

standards from the perspective of current visitors.

Data collection for the second component of the

research consisted of gathering the information necessary to construct the computer simulation model.

Figure 18—Study photograph of Michigan Avenue in the prison cellhouse, representative of

a moderate use level.

Inputs gathered to build the basic model included

current numbers of visitors per ferry boat, current

boat schedule, the lengths of time between debarkation and arrival into the cellhouse audio tour ticket

line, and the length of time that visitors take to go

through the audio tour. Boat schedules and the number of visitors per boat were easily obtained from Park

records. The time between debarking and arrival at

the audio tour and the time it takes visitors to finish

the audio tour were determined by research staff over

a 3-day period in October 1998. On October 27 through

29, three researchers stationed at the dock and various points in the cellhouse handed time assessment

cards to randomly selected visitors. The researchers

recorded on the cards the times of the day when the

visitors arrived and departed from selected places. A

total of 179 time assessment cards were collected

during the 3-day period.

Model Characteristics

The software package selected for the simulation

was Extend by Imagine That, Inc. Figure 19 is the top

layer of the model, and is what a researcher or manager would encounter after opening the model. The

prison cellhouse model was built with “blocks” that

represented specific components of the Alcatraz Island visitor system. The block labeled “Dock” in the

top-left section of the model is where debarking simulated visitors are created. Double clicking on it would

reveal an interface that allows the researcher or manager to specify the boat schedules and boat loads to be

simulated. The block labeled “Up the Hill” represents

the areas where the visitors are between the time they

debark and the time that they line up for the audio

tour. The amount of time that the simulated visitors

are delayed here was determined by the 3 days of
40 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

Figure 19—The Alcatraz cell house visitor research model.

empirical time assessment exercises described above.

The average time taken was 32 minutes, with a standard deviation of 19 minutes. The blocks labeled

“Audio tour line” and “Cashier” simulate the queue

that visitors wait in to pay for their audio tours.

The next series of blocks simulate portions of the

audio tour in the cell house. Each simulated visitor

travels through the blocks labeled “Tour beginning,”

“Tour middle,” and “Michigan Ave.” Both the audio

tour program and the estimated time that visitors

paused the cassette during their tour determine the

amount of time visitors take to move through the

system. The length of time that visitors took to travel

through the entire system was measured during the

3 days of time assessments. The average time taken

was 44 minutes with 13 minutes standard deviation.

When compared with the cassette running time of

approximately 35 minutes, these results indicate

that visitors frequently pause their cassette players

during the tour. The ratio between these times was

used to estimate that the total time each visitor spent

out in the open on Michigan Avenue was about 5.4

minutes.

Model Outputs

A typical simulation model run with current summer boatloads and schedule yields a line graph showing the numbers of simulated visitors on Michigan

Avenue through an entire simulated day (fig. 20).

Figure 20 shows that PAOT on Michigan Avenue at

any one time can fluctuate substantially, anywhere

from about 50 people to about 90 people. Figure 21

provides a different look at this fluctuation in the form

of a histogram. Each bar represents a range of PAOT

conditions on Michigan Avenue through the same

simulated day.

The average condition on Michigan Avenue during

10 computer runs was about 70 PAOT. The model

estimates that visitors encounter more than 70 PAOT

for about 44 percent of their tour time. They encountered more than 80 PAOT for about 15 percent of the time

and more than 90 PAOT for about 3 percent of the time.

The model was run multiple times (to “average out”

the randomness associated with each individual model

run) to estimate the maximum total daily use levels

that could be accommodated on the Island without

violating each of the potential crowding-related standards more than 10 percent of the time (table 8). For
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 41

Figure 21—Simulated people-at-one-time (PAOT) conditions on Michigan Avenue.

Figure 20—People-at-one-time (PAOT) in Michigan Avenue over the minutes of a

simulated day.

0

10

20

30

40

50

60

70

80

90

100

110

Time of Simulation Day

PAOT

9:30 am 5:30 pm

0

5

10

15

20

25

0 to 55

to 10

10 to 15

15 to 20

20 to 25

25 to 30

30 to 35

35 to 40

40 to 45

45 to 50

50 to 55

55 to 60

60 to 65

65 to 70

70 to 75

75 to 80

80 to 85

85 to 90

90 to 95

95 to 100

100 to 105

105 to 110

PAOT Ranges

Percentage of Time PAOT Range Occurred
42 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

this range of potential standards, maximum allowable

use on Alcatraz Island could range between approximately 2,500 and 4,800 visitors per day. A daily use

limit for Alcatraz Island could be implemented relatively easily through management of the ferry system

serving the Island.

A final analytical approach used in this study employed the computer simulation model of visitor use to

explore the effect of alternative ferry schedules. Currently, ferries depart San Francisco for Alcatraz Island every half hour from 9:30 a.m. to 5:30 p.m. If ferry

departures were reduced to every hour, maximum

allowable use would also be substantially reduced.

Relatively large numbers of visitors arriving at the

same time, however, would result in many visitors

seeing relatively large PAOT in Michigan Avenue.

For example, with the current ferry schedule, approximately 4,800 visitors per day can be accommodated on

Alcatraz Island without exceeding 44 PAOT more than

10 percent of the time (table 8). However, if ferries were

to depart only every hour, the computer simulation

model estimates that only 3,200 visitors per day could be

accommodated without exceeding 44 PAOT more than

10 percent of the time. Increasing the frequency of ferry

service would not increase maximum allowable use

levels substantially. For example, increasing the frequency of departures to every 15 minutes would increase

the maximum use level that would not exceed 44 PAOT

more than 10 percent of the time from approximately

4,800 visitors per day (under existing ferry service) to

approximately 4,900 visitors per day.

Conclusions

This case study illustrated the utility of a computer

simulation model in dealing with crowding-related

issues in an urban park. Specifically, it provided a

means of predicting maximum use levels that could be

accommodated without violating specified standards.

In addition, it provided insight into how varying public

transportation schedules might influence maximum

use levels.

References

Manning, R.; Wang, B.; Valliere, W.; Lawson, S.; Newman, P. 2002.

Research to estimate and manage carrying capacity of a tourist

attraction: a study of Alcatraz Island. Journal of Sustainable

Tourism. 10: 388–404.

Table 8—Alternative daily maximum use levels at Alcatraz

Island.

Standards of quality Maximum daily use level

Preference (25 PAOT) 2,560

Acceptability (44 PAOT) 4,800

Management action (44 PAOT) 4,800

Arches National Park: Describing

Visitor Use Patterns and Predicting

the Effects of Alternative

Transportation Systems __________

Steven R. Lawson

Robert E. Manning

William A. Valliere

Benjamin Wang

Purpose

The purpose of this case study is to illustrate how a

computer simulation model can describe daily visitor

use patterns and levels throughout Arches National

Park’s road and trail network and at selected attraction sites. The simulation model provides managers

with estimates of “minute-by-minute” vehicle traffic

levels along all road segments and at all parking areas

within the park on a “typical” peak season day, as well

as the number of visitors along selected trail segments

and at significant park attractions (for example, Delicate Arch). Further, the model is used to predict the

likely effects of alternative transportation systems on

crowding and congestion. Specifically, simulation was

used to predict the effect of a mandatory shuttle bus

system on the number of visitors that can be accommodated at Delicate Arch.

Arches National Park

Arches National Park is located in southeastern

Utah, 8 km north of the town of Moab. The park is

approximately 30,000 ha in size and is known for its

unique sandstone features, including Delicate Arch,

The Windows, and Devil's Garden. The predominant

recreation uses of the park include hiking, photography, and scenic driving, and the majority of recreation

use is concentrated along trails and at attraction sites

adjacent to or near the park’s road network. Most

visitors enter the park through an entrance gate at the

southern end and access the interior via the park’s

road system. Currently, there are no restrictions on

the number of vehicles allowed to enter the park, and

there is no required alternative transportation system. The park is undergoing an alternative transportation planning process, however, and a shuttle bus

system is among the ideas being discussed by park

staff and the public.

Data Collection Procedures

A variety of methods were employed to gather the

data used to build the simulation model of visitor use.

A traffic counter at the entrance to the park recorded
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 43

the number of vehicles entering and the time each

vehicle entered. These traffic data were collected during the 7-day period August 19 to 25, 1997. Total daily

entries for these 7 days averaged 1,346 vehicles.

Data on visitor characteristics and travel patterns

within Arches National Park were collected through a

series of onsite surveys administered to visitors during the summers of 1997 and 1998. Vehicle travel

route questionnaires were administered to 426 visitor

groups in private automobiles and 160 tour bus drivers as they were exiting the park. Respondents were

asked to report their group’s size, the amount of time

they had spent traveling on the park roads, and where

and how long they paused during the visit (for at least

5 minutes). Finally, with the aid of the interviewer,

they were asked to retrace the route of their trip on a

map of the park. The vehicle travel route questionnaires were administered to visitor groups on 6 days

during the period August 14 to 30, 1997, and to tour

bus drivers on 42 days between July 9 and October 22,

1998. For safety concerns, each sampling day started

at 7:00 a.m. and ended at dusk.

A second questionnaire was administered during the

summer of 1997 to a total of 180 visitor groups returning from hikes to Delicate Arch. One visitor from each

group was asked to report the group’s size, the amount

of time they had spent on the trail and at the Arch, and

where and how long they paused during the hike (for at

least of 5 minutes). These questionnaires were administered on 3 days during the period August 15 to 24,

starting at 7:00 a.m. and ending at 10:00 p.m.

Hiking questionnaires were also administered during the summer of 1998 at The Windows and Devil’s

Garden sections of the park. Similar to the hiking

questionnaire administered at Delicate Arch, visitor

groups at The Windows and Devil’s Garden areas were

asked to report information about their group size, the

route they hiked, and the places and amount of time

they paused during the hike. A total of 245 questionnaires were completed by visitors returning from their

hikes around The Windows on 5 days during the

period July 18 to August 3, and 320 questionnaires

were administered to hikers returning from their

hikes in the Devil’s Garden on 5 days during the period

July 5 to August 6. Surveys in both locations started at

7:00 a.m. and ended at 10:00 p.m.

The sampling period for the visitor surveys was

designed to ensure that an adequate number and

diversity of vehicle and hiking routes were sampled.

For example, a greater number of sampling days was

allocated to collecting tour bus routes than personal

vehicle routes because fewer tour buses enter the park

each day than personal vehicles. In addition, the

sampling period was selected to occur during the peak

period of the visitor use season. Lastly, while the

sampling occurred over a 2-year period, there is no

reason to believe that the distribution of visitor travel

routes changed substantively from the first year to the

second year as there were no changes to park infrastructure, such as roads and trails.

Data needed to validate the output of the travel

simulation model were gathered through a series of

vehicle counts conducted at selected parking lots in

the park. The number of vehicles in the Delicate Arch,

The Windows, and Devil’s Garden parking lots (the

park’s three major attraction areas) were counted 11

times a day between 6:00 a.m. and 10:00 p.m. on 4 days

during the period from August 19 to 25, 1997. The total

number of vehicles entering the park was recorded

with traffic counters on each of the days that parking

lot counts were conducted.

Model Characteristics

Using the data inputs described above, a probabilistic, terminating simulation model of park use was

developed (refer to Chapter 3 for more information

about terminating and probabilistic simulation modeling). A terminating simulation has a known initial

state (usually zero) and a known ending state, which

is well suited for replicating a system’s behavior over

a discrete period of time, such as a single day. Since the

primary use of the simulation model developed in this

project was to describe recreation use patterns and

levels on a “typical” peak-season day, terminating

simulation was adopted.

Simulation Model Runs

Use Levels and Patterns—To estimate existing

recreation use levels and patterns (vehicle and pedestrian), a series of runs was conducted to simulate

current average “peak” daily use. Twelve replications

of the current daily use simulation were conducted to

account for stochastic variation of model inputs and

outputs. As noted earlier, safety concerns prevented

vehicle and tour bus travel route surveys from being

administered after dark. Therefore, each run simulated a single day of park use from 5:00 a.m. to 4:00 p.m.

Effect of Alternative Transportation—Previous research has led to establishment of selected

indicators and standards of quality for major attractions within Arches National Park (Manning and

others 1995, 1996a,b; National Park Service 1995).

For example, to avoid unacceptable levels of crowding,

the number of people-at-one-time (PAOT) at Delicate

Arch should not exceed 30 more than 10 percent of the

time. An initial set of simulations was conducted with

the Arches computer simulation model to estimate the

maximum number of visitors that can be allowed to

hike to Delicate Arch before this standard of quality as

violated (referred to in the remainder of this paper is

maximum allowable use at Delicate Arch). Next, a

series of simulations were conducted to predict whether
44 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

implementing a mandatory shuttle bus system would

increase the maximum allowable use of Delicate Arch

and, if so, by how much.

To estimate maximum allowable use for Delicate

Arch without a shuttle bus system, a series of simulations was run in which the total number of visitors

hiking to the Arch was varied. The average percent of

time that PAOT at Delicate Arch exceeded 30 (in

other words, the maximum acceptable level of PAOT

at Delicate Arch) was recorded for each use level

modeled. An iterative process of increasing or decreasing the daily number of visitors hiking to the

Arch was followed until PAOT at Delicate Arch exceeded 30 an average of 10 percent of the time. For

example, a series of 12 simulations was run for a

selected level of visitor use and the average percent of

time that PAOT exceeded 30 was calculated from the

simulation results. If PAOT exceeded 30 an average

of more than 10 percent of the time, the next set of 12

simulations was run at a lower use level, while if

PAOT exceeded 30 an average of less than 10 percent

of the time, the next set of 12 simulations was run at

a higher level of visitor use.

A series of model runs was conducted to simulate the

operation of a shuttle bus system designed to deliver

visitors to the Delicate Arch parking lot at regularly

scheduled time intervals. Separate model runs were

conducted to simulate alternative shuttle bus schedules designed to arrive at Delicate Arch every 15, 30,

and 60 minutes. For each shuttle bus system simulated, the number of visitors riding the shuttle bus and

hiking to the Arch was varied to estimate the maximum allowable use of Delicate Arch. The shuttle bus

simulations were based on the assumption that the

amount of time visitors spend hiking to the Arch and

at the Arch itself would not change as a result of

implementing a shuttle bus system. Although it would

have been possible to alter the amount of time visitors

spend at the Arch in response to a shuttle bus system,

there is no reason to believe that this would be more

valid than assuming no change. Because the delivery

schedule was fixed for each shuttle bus system considered, the simulations were deterministic (in other

words, the results of multiple simulations of a given

shuttle bus delivery schedule would always be the

same).

Model Validation—A series of 48 model runs was

conducted to validate the simulation model output.

The number of vehicles entering the park was varied

to match the number of vehicles entering the park on

the 4 days that parking lot counts were conducted. The

model runs were repeated 12 times for each of the four

use levels to capture stochastic variation. For each of

the four total use levels modeled, the average number

of vehicles in selected parking lots was calculated and

compared to the actual parking lot counts.

Model Output

Use Levels and Patterns—Estimates of the number of people at one time (PAOT) at Delicate Arch

throughout the hours of a “typical” peak season day

are presented in figure 22. Estimates of the number of

vehicles along a selected road segment and in the

Delicate Arch parking lot throughout the hours of a

“typical” peak season day are presented in Figures 23

and 24, respectively.

Alternative Transportation System—Without a

mandatory shuttle bus system, the model estimates

that a maximum of 315 people can be allowed to hike

to Delicate Arch between the hours of 5:00 a.m. and

4:00 p.m. without exceeding 30 PAOT at Delicate Arch

more than 10 percent of the time. Results of simulation

runs conducted to test the effect of implementing a

mandatory shuttle bus system are reported in table 9.

The data in the third and fourth columns suggest that

the maximum allowable use of the Arch could be

increased by 29 to 68 percent if visitors were required

to ride shuttle buses to the Delicate Arch parking lot.

For example, the model estimates that a shuttle bus

system designed to deliver visitors to Delicate Arch

every 60 minutes would increase the maximum allowable use of the Arch from 315 hikers between the hours

of 5:00 a.m. and 4:00 p.m. to 407 hikers. Further, the

results suggest that smaller, more frequent shuttle

buses would increase the maximum allowable use of

Delicate Arch to an even greater extent.

Model Validation—Table 10 presents validation

results based on comparisons between actual parking

lot counts and model estimates for three parking lots

and parkwide. The fact that cars were only counted for

4 days precludes statistical comparison of observed and

predicted counts. It is also not possible to assess the

accuracy of model outputs, such as those in table 9.

However, the relatively small difference in means for

observed and predicted counts suggests that the model

performed as expected.

Conclusion

This case study demonstrates how computer simulation modeling can be used to describe visitor use

patterns and levels throughout a dispersed recreation

area. The findings also illustrate the capability of

simulation modeling to conduct a more comprehensive

monitoring program than may be feasible by relying

solely on on-the-ground monitoring techniques. Monitoring indicators such as PAOT by means of on-theground counts can be very time consuming and expensive. However, once a computer simulation model of

visitor use has been developed, PAOT can be estimated relatively easily.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 45

Figure 22—Model estimates of people at one time at Delicate Arch. Number of Vehicles 

0 

10 

20 

6am 8am 10am noon 2pm 4pm

Figure 23—Model estimates of vehicles at one time on a 2.4 km segment of road.
46 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

Table 9—Estimated maximum allowable use of Delicate Arch with mandatory shuttle system.

Arrival interval Estimated maximum Percent increase in

(minutes) Passengers daily use allowable use

60 37 407 29

30 21 462 47

15 12 528 68

Table 10—Parking lot car counts—number observed and number predicted by the

model.

Observed Model

Delicate Arch 29 25

The Windows 25 30

Devil’s Garden 67 69

All parking lots 40 41

Figure 24—Vehicles at one time in the Delicate Arch parking lot.

The findings from this project suggest that requiring visitors to ride a regularly scheduled shuttle bus

to the Delicate Arch parking lot would increase the

maximum allowable use of the Arch. More generally,

this project demonstrates the capacity for computer

simulation modeling to assist managers in assessing

the effectiveness of alternative transportation systems in a manner that is more cost effective and less

politically risky than on-the-ground trial-and-error

approaches.

References

Manning, R.; Lime, D.; Freimund, W.; Pitt, D. 1996a. Crowding

norms at frontcountry sites: a visual approach to setting standards of quality. Leisure Sciences. 18: 39–59.

Manning, R.; Lime, D.; Hof, M. 1996b. Social carrying capacity of

natural areas: theory and application in the National Parks.

National Areas Journal. 16: 118–127.

Manning, R.; Lime, D.; Hof, M.; Freimund, W. 1995. The visitor

experience and resource protection (VERP) process: the application of carrying capacity to Arches National Park. The George

Wright Forum. 12: 41–55.

National Park Service. 1995. The visitor experience and resource

protection implementation plan: Arches National Park. Denver,

CO: U.S. National Park Service, Denver Service Center.

25 

50 

4pm

0 

Number of Vehicles 

6am 8am 10am noon 2pm
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 47

Isle Royale National Park:

Estimating the Effectiveness of

Alternatives for Managing Crowding

at Wilderness Campsites _________

Steven R. Lawson

Robert E. Manning

Ann Mayo Kiely

Purpose

The purpose of this case study is to demonstrate how

a computer simulation model can be used to identify

management actions that are effective in bringing

conditions into compliance with standards. The study

was conducted at Isle Royale National Park, located in

the northwest corner of Lake Superior, approximately

120 km from Houghton, Michigan, and 30 km from

Grand Portage, Minnesota (Lawson and Manning

2003a; Lawson and others 2003). Approximately 99

percent of the park’s land base is designated wilderness. The park has a system of 36 campgrounds, with

a total of 244 designated tent and shelter sites dispersed along lakeshores, and a network of 260 km of

trails. Primary recreation activities at the park, which

is open to visitors from mid-April until the end of

October, include hiking, camping, and boating.

In recent years, recreation use of Isle Royale National Park has increased steadily. The park is particularly popular for backpacking and backcountry

camping, and as a result, overnight backcountry use

densities are among the highest in the National Park

System (Farrell and Marion 1998). Isle Royale National Park’s approach to backcountry camping management is designed to maximize public access and

maintain visitors’ sense of spontaneity and freedom.

Visitors interested in backcountry camping at Isle

Royale National Park are required to obtain a permit,

but there is no limit on the number of permits, and

visitors are not required to follow fixed itineraries.

While visitors do have the option to obtain special

permits for offtrail hiking and camping, the vast

majority choose to camp at the designated campground sites (Farrell and Marion 1998). Consequently,

backcountry campground capacities are commonly

exceeded during peak periods, causing visitors to have

to “double-up” at campsites with other groups. Because previous research at the park suggests that for

some visitors, having to “double-up” detracts from the

quality of their experience (Pierskalla and others

1996, 1997), the park has considered establishing

standards for the maximum proportion of backpackers who would have to “double-up” at campsites. Managers needed insight into what actions might have to

be taken to achieve these standards.

The simulation model was designed to achieve three

related objectives: (1) to describe the current spatial

and temporal extent of campsite sharing in the park;

(2) to assist park managers in identifying management actions capable of reducing or eliminating campsite sharing; and (3) to enhance the effectiveness of

public involvement processes by providing managers

with precise, quantitative estimates of the effectiveness of management alternatives.

Data Collection

Backcountry camping permits issued by park staff

during the 2001 season provided the primary source of

data needed to construct the computer simulation

model. Data needed to test whether the simulation

model outputs are valid estimates of on-the-ground

conditions were gathered through a series of campground occupancy observations conducted throughout

the park’s 2001 visitor use season. Specifically, backcountry rangers and campground hosts recorded the

number of groups camping in selected campgrounds

on several nights throughout the visitor use season.

Constructing Model Inputs From Permit

Data

Information from the permits concerning each backcountry camping group’s starting date, camping itinerary, and group size were used as inputs to construct

the simulation model. Starting dates reported on the

backcountry camping permits were used to determine

the total number of trips starting on each day of the

2001 visitor use season. From these data, the average

number of trips starting on weekdays and weekend

days from Rock Harbor, Windigo, and all other locations was computed. Within the model, for each of the

three simulated entry points, an exponential distribution with a mean equal to the mean number of trip

starts for the corresponding location and day of the

week was used to generate simulated backcountry

camping trips.

Information about group sizes reported on the backcountry camping permits was used to compute the

percentage of small (six or fewer people) and large

(more than six people) visitor groups during the 2001

season. Within the simulation model, this information

was used to assign visitors a group size (small or

large).

Trip itineraries recorded on the permits were segmented by trip starting location and group size to

construct a set of six probability distributions of

simulated backcountry camping trips (small and large

group itineraries for each of the three simulated

entry points). After being assigned a group size within

the model, simulated visitor groups were assigned a
48 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

Table 11—Management alternatives quantified, based on simulation model output.

Wilderness Status Permit Fixed Campsite Temporal

values quo quota itineraries construction redistribution

Public Current 22% 30% Current use Current use

access use reduction in increase in (shift 22% of

July/August July/August peak)

use use

Facility No new No new No new 13 new No new

development campsites campsites campsites campsites campsites

Visitor No fixed No fixed Fixed No fixed No fixed

freedom itineraries itineraries itineraries itineraries itineraries

Camping 9% of 5% of <1% of 7% of 5% of

solitude— groups share groups share groups share groups share groups share

July and August sites/night sites/night sites/night sites/night sites/night

Camping 0.4% of 0.4% of <1% of <1% of 1.4% of

solitude— groups share groups share groups share groups share groups share

low-use period sites/night sites/night sites/night sites/night sites/night

camping itinerary from the distribution itineraries

that corresponded to their starting location and group

size.

Computer Simulation Model

Characteristics

Using the data inputs described above, a probabilistic, steady-state simulation model of backcountry

camping use was developed (refer to Chapter 3 for

more information about steady-state and probabilistic simulation modeling). The commercial simulation

software package, Extend, developed by Imagine

That, Inc., was used to develop the simulation model.

The primary use of the simulation model developed

was to replicate backcountry camping use at the peak

of the visitor use season in order to address the

objectives outlined earlier. Consequently, steadystate simulation was adopted for this study because

it is designed to replicate system behavior over the

long run at a given level of production or capacity.

Simulation Model Runs

Simulation runs were conducted to estimate the

extent of campsite sharing in the park under status

quo conditions, and to estimate the effectiveness of

management actions at reducing or eliminating campsite sharing. Management alternatives evaluated with

the simulation model include reducing use through

quotas on permits, requiring visitors to follow prescribed fixed itineraries, and building additional campsites within existing backcountry campgrounds.

A series of simulations was also conducted to validate the simulation model outputs. Data concerning

the average number of groups per night camping in

selected campgrounds were gathered from 20 model

runs. These data were compared with campground

occupancy count data collected by park staff for the

same campgrounds during the 2001 visitor use season. In addition, a workshop was conducted to instruct

park staff how to use and modify the simulation model

to continue meeting their planning needs. The park

staff’s use of the simulation model is ongoing, allowing

them to evaluate management strategies as new ideas

emerge throughout the park’s backcountry and wilderness planning process.

Model Output

Table 11 summarizes the results of simulation runs

conducted to estimate the current extent of campsite

sharing, and to estimate the effectiveness of alternative strategies for reducing or eliminating campsite

sharing. The alternatives outlined in table 11 were

selected for analysis with the simulation model because they reflect a range of management approaches

that emphasize campsite solitude, visitor freedom,

public access, and facility development to varying

degrees.

Park managers have the option of managing backcountry camping to maintain status quo conditions.

Under this alternative, an average of about 39 permits would be issued per day, there would be no new

campsite construction, and visitors would not be

required to follow prescribed itineraries. Park planners were interested in a standard of no more than 5

percent campsite sharing. Simulation results for the

“Status Quo” alternative suggest that under the park’s

current management approach, an average of about
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 49

Table 12—Mean campground occupancy rates, with number

of observations in parentheses, as observed and

predicted by the model.

Campground Observed Model

Daisy Farm—weekend 15.0 (15) 15.8 (16)

Daisy Farm—weekday 15.1 (14) 13.3 (40)

Belle Isle —weekend 3.1 (12) 3.8 (16)

Belle Isle—weekday 3.1 (19) 3.1 (40)

9 percent of groups are required to share campsites

per night during July and August, with 24 percent

sharing during the busiest 2 weeks of this period.

Less than 1 percent of groups are estimated to share

sites during the low-use period of the season.

Simulation runs were conducted to assess the effectiveness of a permit quota at reducing or eliminating

campsite sharing. Under the “Permit Quota” alternative, there would be no new campsite construction and

visitors would not be required to follow prescribed

itineraries. However, the average number of permits

issued per day during July and August would be

reduced to ensure that an average of no more than 5

percent of groups share campsites per night (a standard for campsite sharing that the park is considering). Such an approach would continue to emphasize

visitor freedom and limit facility development in wilderness, while allowing for greater camping solitude

than the status quo for those groups able to obtain a

permit. However, fewer individuals who want to take

a backcountry camping trip during July or August

would be able to. The simulated “Permit Quota” alternative suggests that the park would need to reduce

visitor use during July and August by nearly 25

percent to ensure that an average of no more than 5

percent of groups share campsites per night.

Decisions to limit public use of public lands are

inherently controversial. To avoid this controversy,

park managers could institute a fixed itinerary system, rather than a permit quota, to reduce or eliminate

campsite sharing. Under this approach, visitors would

be assigned to campgrounds that had open campsites,

and no new campsites would be constructed. However,

visitors would have fewer choices of itineraries and

would lose the freedom to spontaneously alter their

camping itinerary during the course of their trip. The

results of the simulated “Fixed Itineraries” alternative suggest that, by requiring visitors to follow prescribed camping itineraries, the park could issue approximately 30 percent more permits than they did

during the 2001 visitor use season, and at the same

time virtually eliminate campsite sharing.

Rather than institute a permit quota or require

visitors to follow prescribed itineraries, managers

could try to reduce or eliminate campsite sharing by

building new campsites. The park’s recently adopted

General Management Plan allows for construction of

up to 13 additional campsites in specific campgrounds.

If this “Campsite Construction” alternative were to be

adopted, the simulation results suggest that, without

instituting any limits on use, the park could reduce

campsite sharing by about 2 percent, resulting in an

average of approximately 7 percent of groups sharing

campsites per night.

As the results of the simulated “Status Quo” alternative indicate, campsite sharing is a problem primarily

during the months of July and August, while there is

virtually no campsite sharing during the low use

period of the season. Further, results of the “Permit

Quota” alternative suggested that park managers

would need to reduce the number of permits issued

during July and August by about 25 percent to

ensure that an average of no more than 5 percent of

groups share sites per night. However, rather than

turning those visitors away completely, park managers could shift “surplus” peak season use to the lowuse period. This “Temporal Redistribution” approach

would allow managers to maintain season-wide visitor use levels, reduce campsite sharing during July

and August, avoid building new campsites, and maintain visitor freedom with respect to camping itineraries. Results of the simulated “Temporal Redistribution” alternative suggest that campsite sharing would

increase from an average of approximately 0.4 percent of groups per night during the low use period of

the season, to just over 1 percent of groups per night.

Another option is to redistribute use spatially.

Simulations conducted to estimate the effect of redistributing visitor use evenly across the two primary

starting locations for backcountry camping trips

(Windigo and Rock Harbor) would not reduce campsite sharing. Finally, redistributing use evenly across

the days of the week also would have no effect on

campsite sharing. The results of these simulations

are not included in table 11.

Results of simulation runs conducted to test the

validity of the model outputs are summarized in

table 12. There were no substantive differences between the observed campground occupancies and the

corresponding model output. This suggests that the

computer simulation model accurately represented

backcountry camping conditions at the park during

the 2001 season.

Park staff’s use of the simulation model is ongoing.

For example, park staff have used the model to

estimate the effect of shifting some use to secondary

entry points, differentially altering the visitation

levels of hikers, paddlers, and powerboaters, and

setting alternative standards for campsite sharing at

different times of the season. In addition, park staff

have used the model to estimate where and how
50 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

many new campsites would need to be added to eliminate campsite sharing during peak season demand.

Using simulation results as a guide, park staff conducted site visits to determine the feasibility and

desirability of campground development needed to

meet peak camping demand, based on considerations

of physical constraints of wetlands, fragile habitats

and topography as well as appropriate size of campgrounds in different areas of the park. The number of

new sites that would be needed to accommodate peak

demand, according to model estimates, is greater than

the number of sites that could be added, given the

constraints listed above. However, the number of

feasible new sites would mitigate campsite sharing

during the peak period of the season to some extent.

Conclusions

Findings from this project demonstrate how simulation modeling can be used to identify effective management actions and avoid those that are less effective.

Furthermore, by providing park managers with precise, quantitative estimates describing the effectiveness of management alternatives, the computer simulation model serves as a communication tool. Park

planners have used simulation model data to better

inform the public of the costs and benefits of different

management options, resulting in more effective public involvement processes. An additional use of the

model data is its incorporation in further research. For

example, the model data generated in this study served

as the basis in designing a visitor survey in which

respondents were asked to evaluate a range of management alternatives (Lawson and Manning 2003b).

The model data provided an important element of

realism to the choices presented to respondents.

References

Farrell, T.; Marion, J. 1998. An evaluation of camping impacts and

their management at Isle Royale National Park. Resources Management Report. U.S. Department of the Interior, National Park

Service, Research.

Lawson, S.; Manning, R. 2003a. Research to inform management of

wilderness camping at Isle Royale National Park: Part I—descriptive research. Journal of Park and Recreation Administration. 21(3): 22–42.

Lawson, S.; Manning, R. 2003b. Research to inform management of

wilderness camping at Isle Royale National Park: Part II—

prescriptive research. Journal of Park and Recreation Administration. 21(3): 43–56.

Lawson, S.; Mayo Kiely, A.; Manning, R. 2003. Integrating social

science into park and wilderness management at Isle Royale

National Park. George Wright Forum. 20(3): 72–82.

Pierskalla, C.; Anderson, D.; Lime, D. 1996. Isle Royale National

Park 1996 visitor survey: final report. Unpublished report on file

at: Cooperative Park Studies Unit, University of Minnesota, St.

Paul, MN.

Pierskalla, C.; Anderson, D.; Lime, D. 1997. Isle Royale National

Park 1997 visitor survey: final report. Unpublished report on file

at: Cooperative Park Studies Unit, University of Minnesota, St.

Paul, MN.

Acadia National Park Carriage

Roads: Estimating the Effect of

Increasing Use on CrowdingRelated Variables _______________

Robert E. Manning

Benjamin Wang

Purpose

The purpose of this project was to create and apply

a computer simulation model to estimate use patterns

along the carriage roads in Acadia National Park in a

way that was meaningful in understanding and managing the quality of the visitor experience. Recreation

use of the park’s carriage road system has increased

dramatically in recent years, and this has caused

concern among both park users and managers. Dominant uses have evolved from horses and carriages (for

which the nearly 80 km of roads were originally constructed in the early part of the twentieth century) to

hiking and, more recently, mountain biking.

Because of the complex, intersecting nature of the

carriage road system and its inherently dispersed use,

direct, systematic observation of recreation use is

difficult. Thus, it is challenging to quantify recreation

use levels and patterns. How many visitors are using

the carriage roads? How often do visitors encounter

one another along the carriage roads? How many

visitors can be accommodated on the carriage roads

before the quality of the recreation experience is diminished to an unacceptable degree? The simulation

model of the carriage road system was designed and

used to help answer these and related questions.

Description of the Study Area

The carriage roads of Acadia National Park, on

Maine’s Atlantic Coast 65 km southeast of Bangor, are

a unique system of more than 80 km of beautifully

designed and highly engineered gravel roads built

under the direction of John D. Rockefeller, Jr., in the

early 1900s. Although the roads were built for horsedrawn carriages, they are now used mainly by bicyclists and walkers, providing a welcome escape from

automobile traffic and access to many undeveloped

areas of the park. Equestrian use is now low and

declining. Longtime observers agree that carriage

road use increased greatly with the rise in popularity

of the mountain bike in the 1980s, although no data on

carriage road use were collected during that time

period. However, the park fielded an increasing number of complaints from visitors and area residents

during that time about “crowding” and “conflict” on

the carriage roads.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 51

Data Collection Procedures

Surveys of carriage road users indicated that the

number of other visitors seen while hiking and biking

was important in defining the quality of the recreation

experience (Jacobi and Manning 1999; Manning and

others 1998). Since use of the carriage roads is relatively high, the concept of persons-per-viewscape (PPV)

was developed as an appropriate measure of carriage

road use. PPV was defined as the number of carriage

road visitors per 100 m of the trail system, the average

distance that can be seen along the generally winding

carriage road system. Thus, the simulation model was

developed to estimate PPV levels across the carriage

road system.

A variety of methods were employed to gather the

baseline data necessary for building the simulation

model (Wang and Manning 1999). These were visitor

census counts, onsite visitor surveys, Geographic Information System (GIS) analysis, a field visit, examination of engineering maps, and computer timing of

visitor arrival patterns. These are described in more

detail below.

Data on where, when, and how many visitors entered the carriage roads system were gathered using

five 1-day visitor censuses. From 9:00 a.m. to 5:00 p.m.

on each of the 5 days, observers stationed at all eight

main entrance points recorded the number of visitors

that entered per hour. Census data suggest that about

3,000 visitors per day use the carriage roads on an

average summer day.

Information on visitor characteristics and travel

patterns were gathered with an onsite survey. A total

of 514 questionnaires were administered at the eight

main exit points. The number of surveys gathered at

each site was proportional to that site’s share of total

exits from the entire system as determined by a census

of visitor use conducted the previous summer. One

visitor from each group was asked about their group

size, their mode of travel (hiking versus biking), the

total amount of time they had spent on the carriage

roads that day, and where and how long they paused

during the visit. Equestrian-related cases were not

included in the study since these uses constitute such

a small percentage of all carriage road use. Finally,

with the aid of a map, respondents were asked to list,

in order, all of the carriage road intersections they

passed during their trip. This produced a total of 381

unique travel routes along the carriage road system.

The lengths of carriage road sections between intersections were calculated from a digital coverage using

GIS analysis. The lengths of each unique route that

respondents traveled were then calculated with a

short ARC Macro Language program.

A field visit to the carriage roads and an examination of engineering maps determined that the length

of a typical viewscape—the length of carriage road

that can be seen at one time—was approximately

100 meters.

The precise timing of visitor arrival patterns was

measured using a Pascal computer program. Data

were gathered over four half-hour periods with similar

use levels near one entrance point on the carriage

roads. Each time a visitor group arrived, a key was hit

on the computer to record the time of arrival (down to

a second) and travel mode (hiking or biking). Sixty-five

data points were recorded. These data were gathered

to verify the use of a decaying exponential distribution to simulate arrival patterns. The use of an

exponential distribution assumes that visitor groups

arrive independently.

Model Characteristics

The simulation model was built using Extend, a

commercial, general simulation software package developed by Imagine That, Inc. The structure of the

model was built with hierarchical blocks that represented specific parts of the carriage road system. The

three main types of hierarchical blocks that comprised

the model were entrance/exit blocks, intersection

blocks, and road section blocks.

The entrance/exit blocks were built to generate the

simulated visitor parties. Visitor parties were generated using an exponential distribution varying around

mean values from the census counts. The parties were

then randomly assigned travel modes (hiking or biking) and group size, both according to probability

distributions derived from the visitor survey. Simulated visitor parties were then randomly assigned

travel speeds according to a lognormal distribution.

The mean and standard deviation of the distribution

were calculated from the travel times reported by

survey respondents and the lengths of their travel

routes. Lastly, visitor parties were randomly assigned

a route identification number according to frequencies

of actual routes reported by survey respondents.

The intersection blocks were built to direct simulated visitor parties in the proper direction when they

arrive at carriage road intersections. Lookup tables

unique for each intersection direct each party toward

the correct next intersection as indicated by their

route identification numbers and how many times, if

any, they have been through that intersection.

The road section blocks were built to serve two

functions. The first was to simulate travel through the

road section by delaying simulated visitor parties for

the appropriate period of time, according to their

assigned travel speeds. The second function was to

gather PPV data. Within each road section block, a

simulated 100-m road segment was built. The number

of visitors traveling on that 100-m segment was counted

and recorded once every simulated minute.
52 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

The simulation model developed in this project was

probabilistic (based on probabilities of simulated visitors selecting available travel itineraries) and was

terminating (an entire day was modeled).

Model Runs

The model was run 135 times to generate information on average PPV. Each run simulated carriage

road use from 9:00 a.m. to 5:00 p.m. (the hours of peak

use), but only recorded output from 10:00 a.m. to 5:00

p.m. Output from the first hour was considered unreliable because people who would have entered the

carriage roads before 9:00 a.m. were not included in

the simulation. The completed model was run for six

levels of total daily use of the carriage roads (multiples

of the current use level of 3,000 visitors): 375, 750,

1,500, 3,000, 6,000, and 12,000 visitors per day. The

model runs were repeated five times at each use level

to capture stochastic variation and generate statistical confidence intervals. PPV conditions were recorded

for four use zones: the entire carriage road system,

road sections designated as high-use zones, road sections designated as low-use zones, and the road section

between intersections 6 and 9 (a particularly heavily

traveled section).

The model was also run for validation purposes.

Data on how many simulated visitor parties exited at

each exit point each hour were gathered for 20 model

runs. Data on how many parties passed by the west

side of Eagle Lake (the site of a permanent infrared

trail counter) were gathered for 16 runs.

Model Outputs

Results are presented first on PPV conditions, and

then on the results of model verification and validation. Figure 25 summarizes results estimating PPV

conditions across all carriage road sections in the

system for different total use levels. The results of the

simulated days are expressed in the number of minutes out of an hour that a typical visitor will see

certain selected numbers of PPV. For example, in

figure 25, when 1,500 visitors use the roads in a day,

a typical visitor would see no one else (0 PPV) for 48

minutes out of an hour, one to five other visitors for

11 minutes out of an hour, and six or more other

visitors for 1 minute out of an hour. Each data point

shown represents the mean values, rounded to the

nearest minute, from five model runs. Standard deviations calculated for these mean values range from

1.10 minutes (total use 6,000, 0 PPV) to 0.03 minutes

(total use 12,000, 21 to 30 PPV).

Figure 26 provides a comparison of PPV results

among the different use zones when 3,000 visitors

use the system in a day (the approximate current use

level on the average day). The use zones are (1) lowuse road sections, (2) all of the road sections in the

system, (3) high-use road sections, and (4) the road

section between intersections 6 and 9, the most

heavily used road section in the system. Each data

point shown represents the mean values, rounded to

the nearest minute, from five model runs. Standard

deviations calculated for these mean values range

from 1.58 minutes (highest-use road section, 0 PPV)

to 0.10 minutes (all road sections, 11 to 15 PPV).

Figure 27 compares observed data with theoretical

distributions used in the model. Figure 27A shows the

empirical distribution of interarrival times (amount

of time between arrivals of visitor parties) gathered

with the laptop computer at entrance points alongside

a theoretical exponential (M = 103.94) distribution.

Figure 27B shows the distribution of average biker

travel speeds calculated from visitor surveys alongside a theoretical lognormal (M = 0.799 min/100 m, SD

= 0.431) distribution. Figure 27C shows the distribution of hiker travel speeds gathered with visitor surveys and calculated with a GIS program alongside a

theoretical lognormal (M = 1.615 sec/100 m, SD =

0.879) distribution. Visual comparison of these distributions suggests there is little reason to conclude that

the data are poorly fitted by the theoretical distributions used in the simulations.

Figure 28 summarizes output validation results.

Results are shown for three comparisons between

observed data and model outputs: (A) the distribution

of visitors across exit points, (B) the distribution of

visitor exits across time, and (C) the distribution of

visitors who passed by the Eagle Lake infrared trail

counter site through the hours of the day. Visual

comparison of the distributions suggests there is no

reason to conclude that the observed data are poorly

fitted by the model output.

Conclusions

This case study illustrates the utility of computer

simulation as a tool for estimating how hiking and

biking use are spatially and temporally distributed

and how these use patterns vary with a range of use

levels. PPV outputs for different total use level conditions and use zones provide a sophisticated view of

carriage road use that would be difficult to observe

directly, but that is strongly related to the quality of

the visitor experience. A second phase of this project

involved a survey of carriage road visitors to help

determine PPV-related standards of quality (Jacobi

and Manning 1999; Manning and others 1999, 2000).

The computer simulation was then used to estimate

the maximum number of visitors that can be accommodated on the carriage roads without violating PPVrelated standards (Manning and others 1998).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 53

Figure 25—Persons-per-viewscape (PPV)

distributions for six total use levels, for all

road sections in the system.

Figure 26—Comparison of persons-perviewscape (PPV) distributions among different use zones with 3,000 total system use.
54 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

References

Manning, R.; Valliere, W.; Minteer, B.; Wang, B.; Jacobi, C. 2000.

Crowding in parks and outdoor recreation: a theoretical, empirical, and managerial analysis. Journal of Park and Recreation

Administration. 18(4): 57–72.

Jacobi, C.; Manning, R. 1999. Crowding and conflict on the carriage

roads of Acadia National Park: an application of the visitor

experience and resource protection framework. Park Science.

19(2): 22–26.

Manning, R.; Valliere, W.; Wang, B.; Jacobi, C. 1999. Crowding

norms: alternative measurement approaches. Leisure Sciences.

21(2): 97–115.

Wang, B.; Manning, R. 1999. Computer simulation modeling for

recreation management: a study on carriage road use in Acadia

National Park, Maine, USA. Environmental Management. 23(2):

193–203.

Manning, R.; Jacobi, C.; Valliere, W.; Wang, B. 1998. Standards of

quality in parks and recreation. Parks and Recreation. 33(7): 88–94.

Figure 28—Frequency comparisons for validation of

model outputs.

Figure 27—Frequency comparisons for the

verification of input distributions.

(A) Inter-arrival Times

0

5

10

15

20

25

154575

105135165195225255285315345

Average Time Between Arrivals of Visitor Parties (sec)

Percentage Frequency

Observed Data

Exponential Distribution

(B) Biking Travel Speeds

0

5

10

15

20

25

30

35

0.1250.6251.1251.6252.1252.6253.1253.6254.1254.625

Average Time Taken to Travel 100 meters (min)

Percentage Frequency

Observed Data

Lognormal Distribution

(C) Hiking Travel Spe eds

0

5

10

15

20

25

30

35

0.251.252.253.254.255.256.25

Average Time Taken to Travel 100 meters (min)

Percentage Freqency 

Observed Data

Lognormal Distribution

(A) Exit Points 

0

5

10

15

20

25

30

35

40

Eagle

Lake

Visitor

Center

Parkman

Mt.

Place of Exit

Percentage of Total Exits

Census Data

Model Output

(B) Exit Times

0

5

10

15

20

10:00-

11:00

11:00-

12:00

12:00-

1:00

1:00-

2:00

2:00-

3:00

3:00-

4:00

4:00-

5:00

5:00-

6:00

Time of Day

Percentage of Total Exits

Census Data

Model Output

(C) Eagle Lake Counter

0

5

10

15

20

10:00-

11:00

11:00-

12:00

12:00-

1:00

1:00-

2:00

2:00-

3:00

3:00-

4:00

4:00-

5:00

5:00-

6:00

Time of Day

Percentage Frequency

Observed Counter Data

Model Output
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 55

Acadia National Park Scenic Roads:

Estimating the Relationship

Between Increasing Use and

Potential Standards of Quality _____

Jeffrey C. Hallo

Robert E. Manning

William A. Valliere

Purpose

Automobile use is the primary means of transportation to and through most National Parks, and for

many visitors it is also the predominant means by

which the park is experienced. Some National Park

units, such as the Blue Ridge National Parkway, are

designed primarily for automobile use. Others, such

as Acadia National Park on the Maine coast, rely on

scenic roadways interspersed with pull-offs or side

roads to vistas to provide “the national park experience” to a majority of users. Despite the importance of

automobile use in the National Parks, little research

has been conducted to examine crowding, automobile

congestion, and social carrying capacities on park

roads. This case study illustrates how a computer

simulation model of automobile traffic in Acadia National Park can be used to help estimate the maximum

number of vehicles that can be accommodated on the

park road system.

Description of Study Area

Established as the first eastern National Park, Acadia

currently receives more than 3 million visits annually.

The Schoodic Peninsula is one of three geographically

separate areas of Acadia, located approximately an

hour’s drive from the main Mount Desert Island portion of the park. Frazer and Schoodic Points are the

most frequently visited areas of this 900 ha portion of

the park.

A scenic road is the sole travel route through Schoodic

Peninsula. The road enters the park near Frazer Point

and follows the shoreline of the peninsula, reaching

Schoodic Point prior to leaving the park. The road

permits only one-way vehicle travel, except on access

roads to both scenic points. A segment of road between

Frazer and Schoodic Points was modeled in this study.

This segment was selected because it represented a

generalized section of the park scenic road.

Data Collection Procedures

Data collection consisted of two phases. During the

first phase, visitors were asked questions regarding

the acceptability of different levels of road traffic.

Using computer-edited photographs, respondents were

asked about the level of use they (1) prefer, (2) consider

acceptable, (3) consider so unacceptable they would no

longer visit, and (4) think the National Park Service

should allow. Mean responses for each of these four

evaluative domains (denoted as preference, acceptability, tolerance, and management action, respectively) provide a range of potential standards from the

perspective of current visitors.

Phase 2 of the study involved the collection of travel

route information using commercially available, handheld Global Positioning System (GPS) units. Route

data were collected during 9 days in July, 2003. Occupants of the first 10 cars to enter Schoodic Peninsula

at the start of the daily data collection period (9 a.m.)

were asked to participate in the study by carrying a

GPS unit in their car. Participants were instructed not

to leave the GPS units on their automobile dashboards

since preliminary trials had determined that units

were susceptible to overheating and malfunction in

this circumstance. The GPS units were collected from

participants at the Schoodic Peninsula exit, and the

route data were downloaded to a computer. The GPS

units were then returned to the park entrance for

collection of additional route data throughout the

sampling day.

The GPS units (GARMIN eTrex Legend) were set to

automatically record route points. Numerous points

were collected for every route, with each point containing

a latitude, longitude, and time. Specifications for the

GPS unit state that location information is accurate to

within 3 m. An Arc Macro Language (AML) script was

then used to extract data points of interest from the many

recorded. The script did this by first setting circular

zones around each intersection or point of interest. The

earliest and latest recorded data falling within these

zones were then extracted from the data set.

The final aspect of data collection, during phase two,

involved counting the number of cars arriving during

half-hour intervals between 9 a.m. and 7 p.m. These

headway counts were used to determine mean

interarrival times for each half-hour interval.

Model Characteristics

A simulation model of the Schoodic Peninsula was

built using Extend, a commercial, generalized simulation software package produced by Imagine That, Inc.

Car travel through the Schoodic Peninsula was set up

as a terminating, discrete event simulation model. In

other words, the simulation model was designed to

release individual cars over a 10-day peak period into

the system, each with its own unique set of information (in other words, attributes). The model, built from

Extend’s libraries of standard blocks, was organized

into hierarchical blocks and overlaid on a map of the
56 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

Schoodic Peninsula to provide a more accurate visual

representation.

Cars are generated, released into the model, and

assigned the necessary attributes in the entrance

block. The entry of cars follows a decaying exponential

distribution, based on mean interarrival times for the

half-hour intervals corresponding to the time of day in

the simulation. Once in the system, one of 193 unique

routes collected using GPS units are randomly selected and that route’s attributes are assigned to a

simulated vehicle. The route attributes specify whether

a simulated vehicle visits or bypasses Frazer and

Schoodic Points, the amount of time spent at each of

the two points, and the time required to travel on the

park road between Frazer and Schoodic Points. The

selection of a travel path based on its frequency of

occurrence in the data set of empirically derived routes

is characteristic of a probabilistic model. However, the

model was also set up with an option to route cars to

the next location specified in the route if the physical

capacity of either of the points’ parking lots was

exceeded. In this way the model represents a hybrid of

both probabilistic and rule-based models.

Model Outputs

The simulation model was run five times (50 days) at

the automobile density-related standards of quality

for preference, acceptability, management action, and

displacement (table 13). None of the standards for the

road segment were violated at the sampled use level

(M = 415 cars/day). At increased multiples of the

sampled use level, standards of quality were progressively violated, as measured by the percentage of time

out of standard (table 14).

These outputs can be used in many different ways.

For example, additional runs of the simulation between two and three times the sampled use level

showed that at approximately 2.1 times the sampled

Table 13—Standards of quality for the scenic park road collected during Phase 1.

Standards of quality

Type of standard (No. cars/quarter mile)

Preference 2.5

Acceptability 7.5

Management Action 8.5

Displacement 12.7

Table 14—Percentage of time each standard of quality was violated on the park road.

Multiple of Management

current use Preference Acceptability action Displacement

1 0.0 0.0 0.0 0.0

2 7.6 0.0 0.0 0.0

3 45.1 0.0 0.0 0.0

4 69.4 0.0 0.0 0.0

5 80.1 0.1 0.0 0.0

6 86.3 3.4 0.3 0.0

7 91.3 19.0 5.4 0.0

8 93.4 40.4 20.8 0.0

9 95.2 52.6 39.9 0.1

10 97.3 60.3 50.7 1.5

use level, the preference standard is violated 10 percent of the time. When multiplied by the sampled use

level this indicates that 871 cars per day could be

allowed into the Schoodic Peninsula without violating

visitors’ preference for crowding on the park road more

than 10 percent of the time. Similar outputs were not

generated for acceptability, management action, and

tolerance standards because the multiples of current

use at which the 10 percent threshold would be crossed

were beyond that deemed likely to occur, at least in the

near future.

The effect of the Frazer Point parking lot size on

crowding along the park roadway was also an output

of the simulation model. By tripping (virtual) digital

switches located on the bottom right of the model, a

physical capacity could be imposed at both Frazer and

Schoodic Point parking lots. When the parking lots

reached their designated capacity, cars were prohibited from entering and were forced to continue on the

park road to their next destination. However, because

the Schoodic Point parking lot is located beyond the

segment of park road modeled for this study, it does

not affect the crowding results. Restricting the number of cars at Frazer Point to 24 (the current number

of spaces in the parking lot) had no statistically significant effect on roadway crowding at any multiple of the

sampled use level (p > 0.05).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 57

Conclusion

This case study illustrates how computer simulation

models can estimate baseline use levels on park roads

and estimate how much use can be accommodated

before potential standards are violated. Results suggest that crowding on the road is not a problem at

existing use levels, even when cars are forced to bypass

their visit to Frazer Point. However, if use doubled,

managers may need to limit use if managing for a

preference-based standard of quality.

Port Campbell National Park,

Australia: Predicting the Effects of

Changes in Park Infrastructure and

Increasing Use__________________

Robert M. Itami

Purpose

The purpose of this case study is to demonstrate the

use of agent-based modeling and simulation to predict

the outcomes of increasing use and different management scenarios at a popular day-use site at Port Campbell

National Park in Victoria, Australia. The case study

demonstrates the modeling and simulation of multiple

travel modes, including arrivals by bus and private

automobile, and onsite pedestrian behavior. It illustrates how to construct agent-based visitor models

using RBSim for existing conditions and for a new

master plan with new parking facilities, pedestrian

walkways, visitor center, and toilet facilities. Finally,

simulation modeling is used to examine the performance of a new master plan over a 10-year time period

and compare the results with a “do nothing” alternative

which maintains the existing site management regime.

In this case study, the “do nothing” alternative is

referred to as “Scenario 1”and the new master plan is

referred to as “Scenario 2.” This case study is described

in detail as an example of rule-based simulations.

Description of Study Area

Port Campbell National Park and the associated

Bay of Islands Coastal Park are located on the Great

Ocean Road, approximately 250 km west of Melbourne.

Comprising 65 km of rugged and spectacular coastal

scenery, the two parks are protected in a strip ranging

in width from a few meters in the Bay of Islands Park

to 2 km within the Port Campbell National Park. The

parks have World Conservation Union (IUCN) ratings of Category II (National Parks) and Category III

(National Monuments) and are designated for ecosystem conservation and appropriate recreation and

protection of outstanding natural features, education, research, and recreation respectively. The Port

Campbell National Park attracts large and steadily

increasing numbers of visitors. The park’s popularity

is enhanced by its proximity to Melbourne and the

large number of tour buses that visit the site daily.

Modeling Two Scenarios at Twelve

Apostles

RBSim was used to examine the impact of changes

in park infrastructure and increasing visitor rates

over a 10-year period on the Twelve Apostles site.

Some 701,000 people visited the site in 2001/2002, and

by 2006/2007 the number is expected to be 864,000.

Figure 29 shows the layout plans for the two scenarios.

Scenario 1 shows the conditions before development of

facilities in the new master plan. Car and bus traffic

entered the site from the Great Ocean Road and

traveled southwest onto a loop road, which was configured with car and bus parking around the perimeter of

the road. Pedestrians then entered the viewing platforms from the loop road. They proceeded along wooden

walkways and viewing platforms located at the top of

the cliffs overlooking the view of the Twelve Apostles.

On busy days the loop parking filled quickly, causing

drivers to park along the entry driveway and along

Great Ocean Road with resultant safety hazards.

Scenario 2 shows the layout anticipated in the new

master plan. In this scenario the viewing platforms

and walkways in the viewing areas remain the same,

but vehicular parking is located to the north of the

Great Ocean Road. A pedestrian underpass allows

pedestrians to pass safely under the Great Ocean Road

to the viewing platforms. Major facilities provided in

the new master plan include a new parking area for

buses, cars and trailers, a new visitor center with

public toilets, and a new walkway extending from the

visitor center to the viewing platforms.

Model Inputs

Data inputs for RBSim models fall into two categories: site characteristics and visitor characteristics.

Site characteristics include a GIS map of the road and

trail network, a Digital Elevation Model (DEM), an

inventory of visitor facilities (including location, capacity, and typical visit durations), and the rate of

arrival of visitors in cars, buses, or other travel modes.

Visitor characteristics include arrival curves, rules of

behavior, and typical trips.

Site Characteristics

Road and trail network—The existing road and

trail network for Scenario 1 was imported from Park

Victoria’s corporate GIS database. RBSim has utilities for importing GIS data from either MapInfo tab
58 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

Figure 29—Network layout and facilities for Scenario 1 and Scenario 2.

files or ESRI Shape files. The new road and trail

network for Scenario 2 was provided in the form of an

AutoCAD drawing file. The network for car and bus

parking, pedestrian walkways, and visitor facilities

was simplified and generalized (while maintaining

positional accuracy) and hand digitized with RBSim’s

digitizing functions. RBSim uses network algorithms

for calculating travel time, travel distances, and

shortest path from one destination to the next. This

requires that the travel network is structured in such

a way that the endpoints of all links connect to nodes,

and that there is a node at the intersection of three or

more links. The RBSim network import utility attempts to construct this topology by breaking links at

intersections and inserting nodes, joining the endpoints of links according to user-specified tolerances,

and building a tabular database that describes the

topology of the network in the form of a forward star

database. RBSim provides network editing functions

for correcting network topology and adding or deleting

links and nodes while maintaining network topology.

Once the network topology is corrected, the network

links are attributed for the maximum travel speed. In

the case of the main highway (Great Ocean Road) the

maximum speed is 90 km/hr. For entry roads the

maximum speed is 15 km/hr, 10 km/hr for parking

areas, and 4 km/hr for trails.

Digital elevation model (DEM)—The DEM represents the elevation of the land surface and is stored as

a grid matrix of elevations. The digital elevation

model for Port Campbell National Park was generated in ESRI ArcInfo from contour elevations and

spot heights derived from 1:25,000 topographic maps

with 10-m contour intervals. Elevations were interpolated on a 20-m floating point grid and imported

into RBSim in ESRI grid export format. The DEM is

used by RBSim to calculate uphill and downhill

slopes to determine walking speeds for pedestrians

and to calculate line-of-site across terrain for

intervisibility calculations. If a DEM is not specified,
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 59

Table 15—Facilities at the Twelve Apostles locale before (scenario 1) and after (scenario 2) the implementation of

the new master plan.

Facility Scenario 1 Scenario 2

Viewing platform 345 people 345 people

Informal lookout 5 people 30 people

Bus park 6 buses 12 buses

Car park 28 cars 165 cars

Visitor centre None 100 people

Toilet None 29 people

Trailer park None 12 cars

RBSim assumes a flat surface with an elevation of

zero.

Facilities inventory—Results of an onsite facilities

inventory to determine the type and capacity of visitor

facilities were entered into the Scenario 1 network as

node attributes. For Scenario 2, parking spaces for

cars, buses and trailers were counted from the drawings provided by Parks Victoria. The number of toilets

and the capacity of the visitor center were determined

from design documentation for the 2001 master plan.

The facilities for both scenarios are enumerated in

table 15.

Locales—A locale is a collection of one or more nodes

with associated facilities that have a shared identity

and can be grouped based on proximity or common

access. The Twelve Apostles visitor site, including

viewing platforms, boardwalks, parking lots, and trails

is an example of a locale. Locales are defined in the

travel network by selecting all nodes to be included in

the locale using the RBSim network editing functions,

and then assigning the “Locale” attribute to the nodes.

This function assigns all the selected nodes the same

locale name. The locale is important so that sitespecific rules can be created for agents. Also, agent

rules are only active when an agent enters a locale,

otherwise the agent follows its global trip plan. Each

locale must also have at least one entry node. In the

case of the Twelve Apostles locale, the entry node is

assigned to the intersection of the Great Ocean Road

and the entry road for both Scenarios 1 and 2.

Visitor Characteristics

Agents—The visitor simulation for Twelve Apostles

utilizes RBSim’s capability of using autonomous intelligent agents to simulate onsite visitor behavior.

For both Scenario 1 and Scenario 2, two agents were

defined to represent visitors arriving by bus and

those arriving in private automobiles. This reflects

the source of visitor data from road traffic counts,

which differentiates between buses and cars. In this

simulation, because of the simplicity of the site, the

main constraint on site behavior is the duration of

stay. Agents are assigned two different travel modes.

The “default” travel mode is walking by foot. Agents

are assigned a second “Arrival” travel mode as part

of the typical trip. In this case the arrival travel mode

for automobiles is car, and the bus arrival travel

mode is bus. Agents are also assigned a maximum

travel speed for their default travel mode. In both

cases the maximum travel speed is 4 km/hour.

Arrival curves—Parks Victoria has a policy of managing visitor use for the 95th percentile busiest day.

This means facilities should be planned for the 19th

busiest day of the year. From several detailed traffic

analyses conducted over a period of 5 years, it was

determined that on the 95th percentile day (Easter

Saturday), 44 buses visited the Twelve Apostles. In

2006, at 7 percent growth per year, this is estimated to

rise to 62 buses (40.3 percent growth from 2001). In

2011, visitation is estimated at 87 buses (96.7 percent

growth from 2001).

Car projections are factored for the average growth

of Twelve Apostles visitation (3.5 percent). On the

95th percentile day (Easter Saturday), 1,589 cars

visited the Twelve Apostles. In 2006, at 3.5 percent

growth per year, this number is estimated to rise to

1,887 cars (18.8 percent growth from 2001). In 2011,

visitation is estimated at 2241 cars (41.1 percent

growth from 2001). Figure 30 shows detailed arrival

curves for cars; similar data were developed for buses.

Agent rules—Agent rules are a set of user-defined

behaviors that are defined using a stimulus/response

or event/action framework. RBSim exposes runtime

properties of the network, agent, and global events.

Each of these properties will have a state or value that

can be defined as a stimulus or event. Boolean logic can

be used to combine two or more stimuli to create

complex conditions for behavior. Behavior is defined

as a directive to search for a facility. An example of a

complex rule is:

If (TravelMode = ‘Car’ AND Locale = ‘12 Apostles’

AND LocaleEntry = True) THEN Find Carpark

Agent rules are assigned to agents in the management scenario builder. The user can specify the order

in which the agent considers rules for execution. For

instance, an agent should always park a car before

going to a visitor center. Agent rules are a set of userdefined agent actions that are triggered by changes in

the agent’s location, travel mode, or other characteristics of the network or agent. An interface for designing

rules exposes the set of simulation properties that can

act as triggers. Agent rules are active within the

context of a locale.

Table 16 shows the agent rules used in the scenarios.

Scenario 1 used rules 1, 2, and 5; and Scenario 2 used

all five rules. These rules direct the behavior of the

agents once they enter the Twelve Apostles locale.
60 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

Figure 30—Car arrival curves used in the simulations for Twelve Apostles, for 2001

Easter Saturday traffic count data as well as projections for 2006 and 2011.

Table 16—Rules used by agents during the simulation runs.

Rule

ID Rule

1 If Arriving at a locale in a car then find a car park.

2 If arriving at locale entry in a bus then find bus parking.

3 When at Twelve Apostles find visitor centre.

4 At Twelve Apostles find toilet.

5 At any locale find viewing platform (repeatedly).

Table 17—Typical trips, with durations generated randomly by the simulator between the

minimum and maximum duration (in minutes) for cars and buses.

Description Min duration Max duration Travel mode

Buses Easter Saturday 2001 60 80 Bus

Cars Easter Saturday 2001 30 40 Car

Note that special rules (3 and 4) are needed in Scenario

2 for the new visitor center and toilet facilities.

Typical trips—The typical trip represents a pattern

of visitation within the park. The typical trip is defined

by an entry and exit node to the travel network, one or

more destinations, a travel mode (car, bus, and so on),

a set of rules, trip duration and an arrival curve. Two

typical trips were assigned to each scenario—one for

buses, the second for cars. The trips to Twelve Apostles

are all round trips, so the entry and exit nodes are the

same (just offsite). The destination node is the entrance to the site on Great Ocean Road. Buses follow

rules 2, 3, 4, and 5. Cars follow rules 1, 3, 4, and 5. Table

17 shows the duration for each type of typical trip. The

duration for the trip varies randomly within the range

specified. The durations are estimated from analysis

of traffic count data.

Model characteristics—All agents follow a global

trip plan. These plans however provide only a general

trip itinerary. Once the agent begins its trip, changing

conditions of the network (facilities becoming full),

global events (rain storms), and agent states (agent

fitness, running short on time), can all act together to

change the behavior of the agent according to rules
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 61

and the internal way-finding logic of the agent. The

way finding reasoning of an agent is influenced by the

following factors: (1) available time (defined by time

elapsed subtracted from total trip duration), (2) travel

mode as it affects travel time, (3) agent preferences, (4)

list of rules and their order, (5) currently executing

rules, (6) internal state of the agent, (7) current location of the agent, (8) condition of the network including

availability of facilities, access restrictions, and travel

time to destinations on the network, and (9) previously

visited destinations.

When an agent arrives at a locale, it checks to see if

there is a duration set for this locale in the global trip

itinerary. If the duration is greater than 0 then the

agent checks to see if there is enough time left in its

total trip duration by subtracting the time elapsed

since the beginning of the trip and the time to travel to

the exit node. If the remainder is positive and greater

or equal to the duration set for this locale, the agent

enters the locale and performs an initialization procedure. Once the locale network has been initialized, the

agent then evaluates all possible combinations of

destinations from its current node location. The agent

then evaluates each path and rejects any path that

exceeds the available locale visit duration. The remaining paths are then ranked to maximize the site

preferences and contain facilities that are on the

agent’s current rule list. A gravity model is used to

weight the paths so paths with high-priority facilities

are ranked higher for facilities close to the agent’s

current location.

Once the preferred path is selected, the agent loads

it as its current trip itinerary. The agent then traverses

this itinerary as far as it can in the current time step.

If the agent encounters a node that contains facilities

that are on its current rule list, the agent changes its

internal state to “visiting facility” and generates a

visit duration for that facility. If the facility at the node

has no available capacity (for example, the parking lot

is full), the agent “looks ahead” on its itinerary to see

if a facility of the same class is available. If there is, the

agent then continues its trip toward that node. If there

is no other facility of the same class, the agent will then

change its state to “queuing” and waits until the

facility becomes available.

During each iteration of the simulation, the agent

must check its available trip time, current travel

mode, current rule list, and current state. Any of these

can trigger a change in behavior. The agent may

abandon its current trip and calculate a path back to

its car, or to the exit. If the conditions have not

changed, then the agent continues to execute its current behavior.

Although there are many more details to this behavior, the above reflects the overall logic behind the

agent’s way-finding logic. When implemented, the

logic produces behavior that appears “smart” in that

the agent generates logical paths and exhibits behavior that is humanlike.

Simulation Outputs and Management

Recommendations

Several model outputs were produced to both predict the future effect of increasing use on current

facilities and to compare Scenarios 1 and 2. The

RBSim simulation runs for 2001, 2006, and 2011

produce visitor loads and capacities for each facility

in the simulation including car and bus parking,

visitor center, restrooms, and viewing platforms. The

status of each facility is written to a database at each

time step of the simulation. These outputs are then

summarized for each hour of the day.

Trip Completion Rates—Trip completion rates

measure the failure rate of visits to the Twelve Apostles

site. If all parking is full, agents cannot enter the site,

and the trip is counted as failed.

Trip completion rates in 2001 are around 100 percent, primarily because the available capacity (average or minimum) never reaches zero (fig. 31). In 2006,

with average car park capacity projected to fall to 5

percent with current facilities and minimum capacity

projected to reach zero for at least three hours of the

day, trip completions fall to the 91 to 95 percent range.

As visitors arrive at the facility and find no parking

spaces, they are forced to leave the locale. By 2011,

with average car park capacity around 3 percent for 3

hours of the day, and minimum capacity at zero for

approximately 5 hours, trip completions fall to 80

percent during peak loading.

Visitor Encounters—Visual encounters are generated using line-of-sight calculations between each

agent and all other agents within a 200-m radius.

Screening effects of terrain are also incorporated into

the calculation. The summaries can be generated for

any link, node, or facility, or summaries can be made

for each individual agent. Figure 32 shows the average

visual encounters per visitor at lookouts. The number

of encounters peaks around 3:00 in the afternoon with

around 60 people visible at one time.

Queuing Times for Parking—RBSim simulates

queuing behavior for parking with current facilities.

The simulator records for each agent the total time in

minutes that each party waits for parking. Projections

in figure 33 show that average queuing, by 2011, will

be double that of 2001. Average peak queuing time is

expected to rise to approximately 1.25 minutes by

2006 and to almost 2 minutes by 2011.
62 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

Figure 31—Trip completion rates in 2001 and projected rates for 2006 and 2011.

Figure 32—Average visitor encounters at lookouts in 2001, 2006, and 2011.

Current Facility Projections - 2001 v 2006 v 2011

Trip Completion Rate

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour of the Day

Trip Completion Rate (%)

Current Facility - 2001

Current Facility - 2006

Current Facility - 2011

Current Facility Projections - 2001 v 2006 v 2011

Average Visitor Encounters at Lookouts

0

5

10

15

20

25

30

35

40

45

50

55

60

65

7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour of the Day 

Average Visitor Encounters

Current Facility - 2001

Current Facility - 2006

Current Facility - 2011
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 63

Figure 33—Average queuing times at car parks in 2001, 2006, and 2011.

Current Facility Projections - 2001 v 2006 v 2011

Average Queuing Time at Car Parks

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

1.40

1.50

1.60

1.70

1.80

1.90

2.00

7 8 9 10 11 12 14 15 16 17 18 19 20

Hour of the Day

Average Queuing Time (minutes)

Current Facility - 2001

Current Facility - 2006

Current Facility - 2011

13

Scenario 1 vs Scenario 2

Average Available Car Park Capacity

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Hour of the Day

Scenario 1 -2001

Scenario 2 -2001

Scenario 1 Parking: 28 spaces Scenario 2 Parking: 165 spaces

Average Capacity Available (%)

Figure 34—Average available parking capacity for Scenario 1 and Scenario 2 for 2001 traffic data.
64 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

Car Park Capacity—Figure 34 shows the average

available capacity for the car park over the day. At 100

percent, the car park is empty; at 0 percent, all spaces

are taken. The figure shows that for Scenario 1 the

parking lot is full from 11:00 a.m. until 5:00 p.m. In

Scenario 2 there currently is excess capacity throughout the day.

Management Implications

Simulation results predict that bus parking will be

inadequate during the busiest time of the day between

2:00 and 4:00 p.m. by the year 2006. This shortage is

exacerbated by the year 2011, as bus parking is inadequate for the whole period from 3:00 to 5:00 p.m. By

2006 the car park is full from 1:00 to 4:00 p.m. By 2011

the car park is full from 12:00 to 5:00 p.m.

The longer walk from the new parking facilities to

the viewing platforms extends the average length of

stay an average of 6 to 7 minutes. As the number of

visitors increase, there is increasing pressure on viewing platforms and lookouts. Crowding increases because of the increased duration of stay and the increased capacity of car parks. It is expected that visitor

satisfaction will decrease with an increase in visitors.

This is caused by increased queuing times at parking

lots, an increase in the length of stay, the number of

visual encounters, especially at viewing platforms,

and the number of visits that fail because of lack of

parking at peak periods. This can partially be resolved

by increasing the capacity of viewing platforms, but

the long-term solution will require redistributing the

visitors to other sites, especially at peak periods. Bus

parking will need to be managed between 3:00 to 5:00

p.m. within 5 years (for example, use informal spaces

near the visitor center). Car arrivals will have to be

limited after 1:00 p.m. in 10 years or an extension to

the car park will have to be built. Viewing platforms

will have to be increased in capacity in the 5- to 10-year

time horizon if the overflow car park is used or if the

car park is extended further.

Mount Rainier National Park:

Collecting Data to Model Visitor

Use on a Complex Frontcountry

Trail System ____________________

Mark E. Vande Kamp

Purpose

The purpose of this case study is to illustrate novel

data collection procedures that were developed to

model a complex frontcountry trail system in Mount

Rainier National Park.

On clear summer days when the avalanche lilies are

in bloom and blue ice is visible in the glacial crevasses

high above, it is easy to understand why Paradise

Meadow is the most heavily used area in Mount

Rainier National Park. In 2003 an estimated 370,000

people drove, rode, or hiked to the lodge, visitor center,

and climbing facilities at 5,400 feet on the south flank

of the mountain (NPS 2004). Of those, about 70 percent took walks or hikes on the system of paved and

gravel trails (fig. 35) in the subalpine meadow north of

the visitor center and lodge (Vande Kamp 2001). Those

walks and hikes are an important aspect of many

visitors’ experiences of Mount Rainier (Johnson and

others 1991). At the same time, the level of visitation

in the meadow has created negative impacts on the

physical resources and the quality of visitor experiences found there. Offtrail hiking has damaged vegetation in many areas of the meadow (Rochefort and

Swinney 2000), and at peak times, visitor movement

on popular trails is impeded by high visitor density

(Vande Kamp and Zweibel 2004).

To set policies that protect the physical resources

and the quality of visitor experiences, managers of

Mount Rainier seek to understand the relationships

between manageable aspects of visitation (where visitors go, what visitors do, how many visitors are present)

and important resources or experiences directly threatened by visitation. Toward this end, Mount Rainier is

developing computer simulation models of visitation

patterns in Paradise Meadow that can be used:

• To estimate potentially informative measures

of visitation that are difficult to observe directly (such as the square cm of trail per hiker)

and to relate those estimates to more easily

measured and managed measures of visitation (such as the number of vehicles in the

parking lot).

• To identify “bottlenecks” in the trail system

where hiker density is highest and changes in

trails or hike routing information might be

most effective.

• To explore the impacts of hypothetical changes

in management policy, trail construction, or

visitation yet to occur in the real world (while

acknowledging the assumptions necessary for

such prediction).

The Challenge of Modeling a Complex

Trail System

Simulation models have generally been applied to

either complex trail systems that are remote and

extensive, such as wilderness areas, or simple trail

systems that are readily accessible, such as overlooks

and visitor facilities (refer to other case studies in

this report). The itinerary information necessary to
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 65

develop simulation models of those environments

has most often been collected by contacting visitors

after their hikes and recording the route and time

information that they recall. This method can yield

accurate itinerary data for both types of situations

because hikers in remote and complex trail systems

usually pay close attention to navigation, and hikers

in simple systems don’t have complex itineraries to

recall. An alternate method of collecting itineraries

in which visitors to remote and extensive systems are

given map-diaries before their hikes and asked to

Figure 35—Trail system and waypoint sign locations at Paradise Meadow.

record their movement has also been used to obtain

accurate itinerary data.

It is more difficult to collect accurate itinerary

data in Paradise Meadow because the trail system is

both complex and readily accessible. Many visitors

do not have a clear destination when entering the

meadow, and they meander through the trail system without maintaining their geographic orientation. Visitors to specific locations such as Alta Vista

or Myrtle Falls often depend on directional signs for

guidance and do not know which of several possible
66 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

routes they followed. Recall of hiking itineraries in

subsequent interviews is often vague and inaccurate. Providing a map-diary and asking visitors to

keep a detailed record of their movement is also

problematic. Hikers who keep such itinerary records

are required to pay considerable attention to both

the map and the environment. Such attention is

likely to alter their hiking itineraries and thus lead

to simulation models that accurately estimate the

visitation patterns of hikers who attend to maps but

not the visitation patterns of typical visitors.

A method of collecting itinerary information is needed

that (1) can provide detailed route and time information and (2) poses a small burden on hikers that is

unlikely to alter hiking behavior. The rest of this case

study describes a method of collecting itinerary information that is particularly well suited for use in

complex and readily accessible trail systems like Paradise Meadow. The waypoint card method combines

waypoint signs with recording of the time when visitors pass strategic locations to create a method of

collecting itinerary data that is simple for visitors to

complete but does not require large numbers of survey

workers.

Methods

The goal of sampling was to produce a random

sample of visitor parties entering Paradise Meadow

via four access points leading from the parking lots or

visitor center. Figure 35 shows the trail system with

the four entry points labeled A, B, C, and D. A survey

worker was stationed at each entry point and instructed to approach every third party who passed. If

the number of survey workers is smaller than the

number of entry points into the trail system, a more

complex sampling schedule in which workers rotate to

the different entry points can be used to produce a

representative sample of hiker itineraries. The sample

was collected on August 23 and 24, 2003, between 9:00

a.m. and 6:00 p.m.

When approaching a hiking party, survey workers

introduced themselves, stated that they were conducting a study of visitor movement for Mount Rainier, and

asked one member of the party to participate in the

study. A laminated copy of the Office of Management

and Budget disclosure statement was handed to those

visitors who agreed to participate. Then the survey

worker asked for the visitor’s home zip code, the size of

his or her party, the number of party members who

were less than 18 years old, and the party’s hiking

destination. The survey worker then instructed the

respondent how to complete the remainder of the

survey by explaining:

There are waypoint signs like this one (pointing to the

entry waypoint) placed along trails throughout the

meadow. Every time you pass one of these signs, please

write the letter of the sign and the time on your card.

I’ll fill out this first one for you (writing down the time

and entry waypoint letter on the card). If you pass the

same point more than once, please write down the time

each time you pass. There are pencils and clocks on

each sign. When you are done hiking, please leave your

card with the survey worker where you leave the

meadow, or place the card in the survey box like this

one (point to exit sign and box) if no one is there.

Most respondents returned their cards directly to a

survey worker. When they did, the worker asked them

if there were any places that they stopped for more

than 5 minutes during their hike. If so, the worker

noted the location and duration of the stop on the

waypoint card.

Figure 36 shows the front and back of the waypoint

cards on which data were written. The cards were

printed on waterproof card stock and included a twisttie that could be used to attach them to a belt loop or

backpack strap so that they would be handy for respondents to write on as they hiked. Figure 37 shows

the front of a waypoint sign. Each sign was printed on

8.5- by 11-inch card stock, attached to a metal stake,

and covered with a clear plastic bag to protect it from

rain or fog. A small LCD clock was attached to the

center of the sign. The clocks on all signs were synchronized. The back of each sign was identical to the front,

with the exception that the clock area included text

explaining that a clock could be found on the other

side.

Waypoint signs were placed at locations in the

meadow selected to define most common hiking itineraries. For example, common sequences of waypoints

passed included C, I, I, D for hikes to Myrtle Falls, or

C, G, K, P, R, M, I, C for the loop hike to Panorama

Point. The sign locations were selected to define general boundaries for the lower, middle, and upper

meadow. The number of signs necessary to provide

precise information about hiking routes in the lower

meadow (locations below the F, G, H, and I signs) was

likely to impose an unreasonable burden on hikers

because the signs would have been only yards apart.

Because the trail system becomes simpler as one

moves away from the parking areas, longer hikes into

the upper meadow were more precisely defined by the

waypoint card data than were shorter hikes in which

visitors did not leave the lower meadow.

Exit signs (fig. 38) also measuring 8.5 x 11 inches

and attached to metal stakes were also placed at the

entry/exit points. Plastic boxes were attached to the

base of each staked exit sign where respondents could

deposit cards if the survey worker was not present

when they finished hiking.

Results

Of the 351 parties asked to participate in the study,

17 refused at the initial contact and 265 returned
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 67

Figure 36—Front and back of the waypoint card respondents used to record the waypoints they

passed while hiking.

Figure 37—Typical waypoint sign placed alongside

the trail.

Date: _____ Card # _____ 

Int.: _____ Zip ________ 

Party Size: ____ No.<18yo: ___ 

Dest.: ____________________ 

Stop: _____________________ 

INSTRUCTIONS: For each waypoint sign you pass, please write

the letter of the way-point and the

time you passed it on the other

side of this card.

If you pass the same way-point

more than once, WRITE THE

LETTER AND TIME EVERY

TIME YOU PASS.

After your hike, please give this

card to the worker at the trailhead

or drop it in the “Study Card” box

at the trailhead. 

 Way Point Time

OMB # Expiration 

WAY POINT

F

The Current Time Is:

Please Do Not Move or Otherwise Alter

This Temporary Sign 

Mt. Rainier National Park is conducting a study of the routes

people hike on these trails. A random sample of hikers are

recording the times that they pass these way-points today.

Please Enter the Way Point and Time on Your Card

Way Point Time

A 11:38 Example

Example

CLOCK 
68 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

Figure 38—Exit sign placed at entry/exit points

instructing respondents how to return waypoint

cards when the survey worker was not present.

Table 18—Duration of stops at three locations in Paradise

Meadow.

Duration of stops

Mean Standard

Locations minutes deviation N

Myrtle Falls 10.6 3.7 20

Alta Vista 11.7 4.1 7

Panorama Point 17.2 13.7 16

completed cards, yielding a total response rate of 76

percent. Anecdotal reports from survey workers indicated that respondents found it easy to follow the

survey instructions. An initial check of the 265 completed itineraries found that 48 (18 percent) included

sequences of waypoint signs that were impossible to

encounter. However, closer examination of those impossible routes suggested that 27 occurred because

respondents overlooked a single waypoint sign. If the

overlooked signs are replaced in those itineraries, the

incidence of impossible routes falls to 8 percent. In

total, 70 percent of all parties approached for the study

returned waypoint cards that traced possible hiking

routes.

Many visitors found it difficult to recall the locations

and durations of the times they stopped hiking for

more than 5 minutes. In addition, survey workers

often found it difficult to talk with returning parties

while maintaining the sampling interval for entering

parties (particularly at the busiest entry point,

waypoint D). Thus, a relatively small number of respondents described the places they stopped and the

duration of their stops. Table 18 summarizes the

WAY POINT STUDY

IF YOU HAVE A WAY POINT CARD

AND YOU ARE FINISHED WITH

YOUR WALK OR HIKE, PLEASE:

1. Write this last way point and the current

time on your card.

2. Recall the locations where you stopped

hiking for more than five minutes. Write

each location and the amount of time

you spent there on your card.

For example: “Alta Vista — 18 min.”

3. Put your card in the box below. 

THANK YOU

duration data for three locations at which hikers

commonly stop.

Discussion

The high response rate, low incidence of impossible

routes, and anecdotal reports all suggest that respondents found it easy to accurately record and report their

hiking routes by filling out the waypoint cards. Thus,

waypoint cards offer a clear advantage over retrospective interviews when applied in Paradise Meadow.

The relative usefulness of the waypoint card method

in comparison to map-diaries is currently more difficult to determine because this initial study was not

designed to compare and contrast those data collection

methods. For example, a comparison of the degree to

which participating in the waypoint card study or a

map-diary study would alter hiking routes is not

possible. (The question of whether the waypoint card

method altered hiking itineraries at all will be tested

by future work in which simulation models based on

the waypoint card itineraries are constructed and

their validity is tested.) Other comparisons of the

waypoint card and map-diary methods of collecting

itinerary show a mixture of advantages and disadvantages. The data provided by the waypoint cards are

ideally formatted for the building of computer simulation models, and are much easier to code and enter into

a computerized database than the data from mapdiaries. However, the waypoint data do not provide the

same level of detail as a complete map-diary. In perhaps the most extreme case from the current study,

figure 35 shows at least seven possible hiking routes

through the lower meadow for a hiker whose waypoint

card began with points B, G.

When the results of the waypoint study were used to

construct a simulation model of use for the observed

weekend, three shortcomings of the data were identified: (1) a lack of detail describing hikes in the lower

meadow (discussed above), (2) poor information about

the durations and locations of hiking breaks, and (3)

lack of information about hiking patterns on the trail

loops at Alta Vista, Glacier Vista, and Panorama

Point. For the construction of the preliminary model,

these shortcomings could be addressed by making
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 69

assumptions about hiker behavior. However, future

applications of the waypoint card method could be

improved by addressing each shortcoming.

The data describing hiking breaks were limited, but

were generally consistent with observations that most

hiking breaks occurred at a few locations in Paradise

Meadow (Myrtle Falls, Alta Vista, Panorama Point).

The validity of the computer simulation would be

improved if the distribution of stop durations at those

locations could be more accurately described. In future

studies, visitors at those sites will be systematically

observed to collect such descriptive data.

More detailed description of hikes in the lower

meadow can most easily be obtained by adding

waypoints in that area. The initial success of the

method and verbal reports from respondents indicating that participation was not a concern suggest that

such additions will not pose a significant burden.

Thus, waypoint signs will be added at several more

junctions in the lower meadow.

The specific changes necessary to obtain more detailed description of hiking patterns on trail loops has

not been determined. Waypoint signs could be added,

systematic observations could be made, or some combination of those two techniques might be adopted.

Inspection of each trail loop prior to the next waypoint

card study will determine the alterations or additions

to the study design.

Currently available GPS receivers offer a means of

collecting hiker itinerary data that is superior to the

waypoint card method or any other paper-and-pencil

instrument. Unfortunately, the receivers are not yet

cheap enough that they can be distributed to a large

number of hikers without unacceptable costs due to

breakage, loss, and pilfering. Nonetheless, small numbers of GPS receivers might be used to collect information that could be used to validate the waypoint card

information (for example, by asking some visitors to

both fill out a waypoint card and carry a GPS receiver).

Such validation studies could develop procedures useful in future large-scale studies of hiker itineraries

using GPS receivers.

Conclusion

Computer simulation modeling holds great promise

as a management tool. As models are developed for a

variety of recreational settings, novel problems will

inevitably arise. This case study discusses how a new

data collection procedure was used to address the

problem of collecting information describing use of a

complex trail system at Paradise Meadow in Mount

Rainier National Park.

References

Johnson, D. R., Foster, K. P.; Kerr, K. L. 1991. Mount Rainier

National Park 1990 general visitor survey. Technical Report.

Seattle, WA: University of Washington, College of Forest Resources, Pacific Northwest Cooperative Ecosystem Studies Unit.

137 p.

National Park Service. 2004. National Park Service public use

statistics office Web site. ttp://www2.nature.nps.gov/stats/

Rochefort, R. M.; Swinney, D. D. 2000. Human impact surveys in

Mount Rainier National Park: past, present, and future. In: Cole,

D. N.; McCool, S. F.; Borrie, W. T.; O’Loughlin, J., comps. Wilderness science in a time of change conference—Volume 5: wilderness ecosystems, threats, and management. Gen. Tech. Rep.

RMRS-P-15-VOL-5. Ogden, UT: U.S Department of Agriculture,

Forest Service, Rocky Mountain Research Station: 165–171.

Vande Kamp, M. E. 2001. Paradise parking lots: hiker vs non-hiker

use. Technical Report. Seattle, WA: University of Washington,

College of Forest Resources, Pacific Northwest Cooperative Ecosystem Studies Unit. 3 p.

Vande Kamp M. E.; Zweibel, B. R. 2004. A regression-based model

of visitor use in the paradise developed area of Mount Rainier

National Park. Technical Report. Seattle, WA: University of

Washington, College of Forest Resources, Pacific Northwest

Cooperative Ecosystem Studies Unit. 38 p.
70 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 71

Introduction ____________________

As the case studies in Chapter 4 illustrate, simulation modeling can be a valuable tool for recreation

planning and management. Although simulation modeling is already well developed for business applications, its adaptation to recreation management is less

developed. Relatively few resources have been devoted

to realizing its potential. Further progress is needed in

refining the models, and their inputs and outputs.

Equally important is work to assess the validity of

models as well as to understand the precision of model

estimates and the appropriateness of using model

outputs for various purposes. Finally, there is substantial potential to link models of recreation behavior

to transportation modeling and to models of biophysical impacts to provide more holistic perspectives.

Software Development ___________

One of the keys to making simulation modeling of

recreation a more easily used tool is to automate as

many of the steps in data entry, model development,

and model output as possible. That has been a primary

focus of work on RBSim, given its development as an

application specific to recreation. For example, users

can navigate through the program using drop-down

menus. More generic software packages, such as Extend, could also be more automated and focused on

specific recreation applications. While this would require substantial work, it is a means of taking advantage of the considerable developmental effort that has

already gone into the generic software package and

would greatly improve its usability.

The ability to use simulation modeling could also be

improved by specifying the most useful generic data

inputs and outputs, along with efficient means of

collecting data and displaying model outputs. For

Chapter 5:

Future Directions for

Simulation of Recreation

Use

David N. Cole

example, table 19 lists most of the common types of

data needed to develop a computer simulation model.

The case studies illustrate some of the ways such data

can be collected. More comprehensive and detailed

descriptions of data collection methods might be useful. Table 20 lists some of the more important outputs

that can be displayed. Software developed in such a

way that common types of data are easily input and

outputs are readily produced would be a major step

forward.

Other needed improvements include linkage to Geographic Information Systems (GIS) and the ability to

produce visualizations. The ability to import data

directly from GIS files would greatly decrease the time

required to get models up and running, while the

ability to automatically display outputs on maps developed using GIS would increase the interpretability

of model results. Visualization capabilities also increase the utility of the modeling as a communication

device. Linkage to GIS and visualization capabilities

have been fundamental aspects of component architecture in the development of RBSim. Linkages to GIS

and visualization in the models based on Extend are

also being developed.

Current simulation modeling requires contracting

with academic or consulting institutions. Much can be

learned from working with academics and consultants, but much of the value of simulation modeling lies

in the ability of managers and planners to use it

routinely. It must become a tool that managers themselves can develop and use. Although we are currently

far from that capability, if there is sufficient interest in

simulation modeling, procedures and software could

be developed to make it a reality. Recent experience

with Geographic Information Systems (although

clearly more broadly useful) provides an example of

the possibilities for modeling in the future.
72 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

Table 19—Common data inputs for simulation models of recreation.

1. Network Information

a. headways (trailhead,...)

b. links (trail segment, road segment, river segment,...)

c. nodes (campsite, picnic site, attraction site, trail junction,...)

d. travel routes

2. Environmental Data (desirable but not always necessary)

a. digital elevation model

3. User Information

a. proportion of groups with differing modes of travel

b. proportion of groups with differing group sizes

c. other means of differentiating between groups

4. Travel Route Information

a. when groups arrive at headways by user type (mode of travel, group size...) may vary by season

within year, week within season, day within week and hour within day

b. travel routes (links and nodes visited) that groups take by user type (mode of travel, group size...)—

may vary by season, week, day of the week and hour of the day

c. travel speeds on links

d. delay times at nodes

5. Data for Verification Purposes

a. encounters per unit of time per link and/or node (encounters need to be carefully specified—they

can be defined in various ways)

b. amount of use per unit of time per link and/or node

Table 20—Common outputs from simulation models that are useful in recreation management.

1. Amount of use per unit of time for each link and/or node, as well as aggregations of links and nodes

a. common time units are per season/year, per day/night and at-one-time (PAOT, PPV)

b. metrics can include both measures of central tendency (mean, median, mode) and distribution

(maximum, minimum, proportion of days/nights/instances with varied encounter levels or above or

below some standard)

c. differences by group type (mode of travel, group size...) might also be desirable

2. Encounters per group per unit of time for each link and/or node, as well as aggregations of links and

nodes

a. common time units are per trip and per day/night

b. metrics can include both measures of central tendency (mean, median, mode) and distribution

(maximum, minimum, proportion of days/nights/instances with varied encounter levels or above or

below some standard)

c. encounters need to be carefully specified—they can be defined in various ways (for example, visual

on link, overtaking on link, meeting on link, node)

d. differences by group type (mode of travel, group size...) might also be desirable

3. Identifying maximum use levels that can enter without violating some standard

4. Queuing times

Improved Data Collection _________

Data input can be improved by making the collection

of visitor data more routine and by taking advantage

of improvements in technology. In many parks and

protected areas, data on natural resources are collected routinely, while the collection of data on people

is ignored. Such data are important, not only because

recreationists can significantly degrade the integrity

of natural resources but also because management is

often charged with providing opportunities for quality recreation experiences. Several techniques for

monitoring visitors are well established (Watson and

others 2000); they simply need to be applied more

frequently and more consistently.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 73

New technologies are also emerging to simplify the

process of collecting visitor data (Cessford and Muhar

2004). In particular, there have been recent improvements in the utility of video, time recording of use

distribution data, and the ability to download data

remotely, reducing the frequency of onsite equipment maintenance. Chip technology and tracking

devices have advanced to the point where they can

sometimes be used to obtain data on the routes that

visitors take. Further development of these technologies will increase the cost effectiveness of model

development.

Improved Model Outputs _________

The case studies in the preceding chapter illustrate

the wide range of recreation situations that can be

modeled, as well as some of the important applications

of model output. However, the appropriateness of different types of simulation models and model outputs as

means of addressing the issues facing recreation managers needs attention. The current state of computer

simulation of recreation is better able to address some

of these situations and applications than others.

Currently, procedures for conducting terminating

simulations are better developed than those for conducting steady-state simulations. Terminating simulations are clearly well suited to modeling day use;

however, it is unclear whether or not steady-state

simulations would be a better way to model what is

happening during periods of peak use by visitors on

multiday visits. If we should deem steady-state simulation to be appropriate, there are some further

challenges that must be addressed. One of the most

difficult challenges of steady-state simulation is determining the appropriate runtime length needed to

generate statistically valid model outputs (that is,

outputs that are not biased by start-up effects or

autocorrelation). Centeno and Reyes (1998) have

developed techniques to establish steady-state

runtime lengths needed to achieve specified levels of

accuracy for model outputs, but they were designed

primarily for manufacturing applications and may be

of limited utility in recreation applications. For example, the 2,000-day runtime length for the steadystate simulations used in the John Muir case study

had to be selected arbitrarily. While intuitively one

would expect this runtime length to be adequate, the

methods developed in the simulation literature could

not be used to test our intuition. Before the full

potential of computer simulation can be realized for

outdoor recreation management, more research is

needed to develop and standardize methods to establish steady-state simulation runtime lengths.

The precision of model estimates has not been

adequately estimated in recent applications. For

example, we hope to use computer simulation as a

means of estimating encounter rates in the interior of

large backcountry areas, without needing to directly

monitor encounters within the area. This should be

possible if model estimates are precise enough. Current models can produce estimates of encounters (as

illustrated in the case studies), but we do not know

whether the estimates are precise enough to be useful

for this purpose.

To explore precision further, we employed a procedure proposed by Freese (1960), based on a chi-square

test, for computing the potential error associated with

model estimates in relation to observations of the real

world. We used the validation data collected at Arches

National Park presented in Chapter 4. The number of

cars at parking lots was observed for 4 days and

compared with model estimates of cars in parking lots.

At the Delicate Arch parking lot, the mean number of

cars observed was 29 when the model predicted 25.

Freese’s procedure indicates that, at a 95 percent

confidence level, the error associated with model estimates is 24 percent or about 7.7 cars. This error

estimate can be used to interpret the finding in the

Arches study that the maximum number of hikers who

can visit Delicate Arch in a day, without violating

standards, is 315. Given a 24 percent error, the maximum that can be allowed is somewhere between 240

and 390 hikers per day—not necessarily 315. Even

this estimate of error should be used with caution,

however, because 4 days of observation at parking lots

may not be sufficient to provide an accurate estimate

of real-world conditions. With a larger sample, we

would have a more accurate estimate to compare with

the model.

Validation ______________________

A related but different issue is validation, how well

the model mimics the real world. The difference between problems with precision and the need for model

validation can be clarified with an example. We may

use a simulation model to estimate the maximum

persons-at-one-time (PAOT) at a recreation site for

which we have a standard of no more than 100 PAOT.

Our model may estimate, based on the mean of numerous simulation runs, that currently the PAOT does not

exceed 90. Either low precision or an invalid model can

reduce the utility of this estimate. For example, perhaps this estimate has an error of about 50 percent (it

is not very precise)—the real estimate could be anywhere between 45 and 135. If this were the case, it

would be impossible to conclude from a mean model

estimate of 90 whether the standard has been exceeded or not. The mean model estimate would have to

be greater than 150 before we could confidently conclude that the standard was exceeded. An invalid
74 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005

model is similarly problematic. If the model estimates

a mean PAOT of 90 when the estimate should be 110,

results are not very useful. Validation is a means of

assessing the likely accuracy of model outputs.

In the case studies presented in Chapter 4, model

validation is generally limited to techniques that rely

on intuitive judgments. For example, in the John Muir

case study, sensitivity analyses conducted with the

simulation model suggest that when total simulated

use is increased, estimates of hiking and camping use

and encounters increased. This analysis supports the

internal and face validity of the simulation model. The

model works the way that experts believe it should and

outputs seem reasonable. However, such analysis is

less useful for making conclusions concerning how

well the simulation model outputs correspond with

data for the real world—the actual trail and campsite

system. Due to lack of sufficient data concerning

actual hiking and camping use and encounters, quantitative validation techniques were not possible in the

John Muir case study.

The above example underscores the need to collect

data that can be used not only as inputs to a computer

simulation model, but as the basis for quantitative

validation of the simulation model. Furthermore, while

there is a relatively extensive body of literature describing validation techniques for simulation models

of manufacturing systems, there is a lack of recent

research concerning the appropriateness of alternative statistical techniques for validation of computer

simulation models of parks, wilderness, and related

outdoor recreation systems. More research is warranted to develop standardized, quantitative methods

to assess the validity of computer simulation models

designed for outdoor recreation management.

Linkage to Transportation

Models ________________________

Park planning could be greatly improved by linking

recreation models to transportation models. Historically, the term “modeling” has been used in the transportation planning and engineering field to refer to

travel forecasting models. Applications of transportation forecasting models have focused primarily on

urban environments with complex transportation systems; however, forecasting models have been developed to forecast vehicle traffic volumes for Yosemite

National Park. In general, transportation networks

within National Parks and related areas are relatively

simple in comparison to typical applications of forecasting models. Data used to develop forecasting models for urban areas include trip diaries describing

travel patterns within the transportation network,

data on the characteristics of the transportation system, land use and/or activity data, and census data.

More recently, transportation modeling applications

have broadened to include simulation modeling. In

many ways, transportation simulation modeling resembles the recreation simulation modeling techniques

described in this report. In particular, both are probabilistic. In other words, simulation results will vary

across simulation runs or replications in a manner

that represents the stochasticity or uncertainty and

time-varying nature of the system being modeled.

While transportation and recreation simulation modeling approaches have comparable capabilities, transportation modeling software may not be as well suited

to park management applications because it has been

purpose-built to represent urban transportation system elements such as streets, highways, and light-rail

transit lines. In contrast, simple spreadsheet modeling is usually deterministic, meaning that for a given

parameter value (such as total daily use level) the

model output will be the same for each replication. In

this way, spreadsheet modeling is similar to regression modeling and other statistical or mathematical

modeling approaches.

The output from transportation models should correspond closely with the input to the recreation simulation models described in this report. Hence, development of linkages between the two sets of models is

equivalent to developing a more comprehensive model.

Linkage to Biophysical

Impacts________________________

Another possibility, fraught with both promise and

pitfalls, is linking models of visitor distribution with

models of biophysical impact. If successful, such a

linkage would make it possible to estimate and/or

predict impacts across both space and time from easily

collected data on visitor inputs to the system. Alternative management scenarios and futures could be evaluated on the basis of detailed information about where

and when different types of impact might occur. This

capability could improve the ability to assess environmental impacts, within planning contexts, by an order

of magnitude.

The most obvious barrier to achieving this potential

is inadequate knowledge about the relationship between visitation and resultant biophysical impacts.

Lack of investment in research on recreation impacts

is a fundamental contributor to this problem, as is the

complexity of the variables that interact to explain

variation in impact levels. The relationship between

the amount of use and the amount of impact is nonlinear (Cole 2004). Moreover, variables such as visitor

behavior and environmental characteristics commonly

explain at least as much of the variation in impact as

amount of use. Some of the impacts of most interest

and concern, such as long-term effects on the viability
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005 75

of animal populations, are difficult to study. Despite

these challenges, the potential insight that could be

gained by linking visitation and biophysical impact

models suggests that it is worth attempting.

Conclusions____________________

As this report illustrates, computer simulation modeling of recreation can contribute significantly to

recreation planning and management. Valid models

of visitor distribution and flow can be developed and

linked with transportation models and possibly with

models of biophysical impact. The result would be a

greatly improved ability to assess the operation of

entire systems (as opposed to single elements in

isolation), with continuous information across space

(as opposed to discrete locations) and across time (as

opposed to discrete times) for measures that may be

difficult to assess directly. Such a comprehensive

perspective would vastly improve the ability to plan

and manage wisely.

Progress to date has been exciting. But it has also

been limited, opportunistic, and not well coordinated.

Funding of the collaborative work that resulted in

this report provided the opportunity to improve coordination and mutual learning, to assess the current

state-of-the-art, and to make progress on a few issues. As is often the case, this effort identified many

issues that need additional attention. Software needs

to be further developed and made more user-friendly.

Data need to be more routinely and carefully collected. More modeling projects need to be undertaken, with funding sufficient to better assess the

precision of estimates and the validity of models.

Finally, the kinds of information that models can

provide need to be more commonly incorporated in

decisionmaking processes.

References _____________________

Centeno, M.; Reyes, M. 1998. So you have your model: what to do

next? A tutorial on simulation output analysis. Proceedings of the

1998 Winter Simulation Conference: 23–29.

Cessford, G.; Muhar, A. 2004. Monitoring options for visitor numbers in National Parks and natural areas. Journal for Nature

Conservation. 11: 240–250.

Cole, D. N. 2004. Environmental impacts of outdoor recreation in

wildlands. In: Manfredo, M.; Vaske, J.; Field, D.; Brown, P.;

Bruyere, B., eds. Society and resource management: a summary

of knowledge. Jefferson City, MO: Modern Litho: 107–116.

Freese, F. 1960. Testing accuracy. Forest Science. 6: 139–145.

Watson, A. E.; Cole, D. N.; Turner, D. L.; Reynolds, P. S. 2000.

Wilderness recreation use estimation: a handbook of methods

and systems. Gen. Tech. Rep. RMRS-GTR-56. Ogden, UT: U.S.

Department of Agriculture, Forest Service, Rocky Mountain

Research Station. 198 p.
76 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-143. 2005
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