1. Monitoring and Management of Visitor Flows in
Recreational and Protected Areas
January 30 – February 2, 2002
Vienna, Austria
RBSim 2
Simulating the Complex Interactions Between Human Movement and the Outdoor Recreation environment

2. Authors Robert Itami – GeoDimensions Pty Ltd Rob Raulings – eFirst
Glen MacLaren – GeoDimensions Pty Ltd
Kathleen Hirst – GIS Applications Pty Ltd
Randy Gimblett – University of Arizona
Dino Zanon – Parks Victoria
Peter Chladek – Parks Victoria

3. RBSim 2 Recreation Behaviour Simulation
Simulates human behaviour on linear recreation networks
Allows recreation managers to test alternative management scenarios
Simulates the interactions between:
Management actions
Environmental conditions
Human behaviour
Generates statistical outputs to measure performance of a scenario against management objectives.

4. RBSim technology framework
RBSim integrates two technologies:
Geographic Information Systems (GIS) to capture environmental conditions and recreation facilities
Intelligent agents – to simulate human behaviour

5. RBSim imports environmental data from GIS
Road and Trail networks
Facility locations (Parking lots, Visitor Centres, camp grounds)
Facility attributes (visitor capacity, typical visit duration, site qualities)
Elevation data (used to calculate slope, and intervisibility)

6. Humans are modeled as Intelligent Autonomous Agents
An Autonomous agent is a system situated within and part of an environment that senses that environment and acts on it, over time, in pursuit of its own agenda and so as to affect what it senses (and acts on) in the future.
Franklin and Graessner(1996)

7. RBSim Simulation Architecture
Object oriented
Component based
Modeled on “reality”

8. RBSim Object Model

9. Road and Trail Network
Links – road speed, slope, width, surface, travel restrictions
Nodes – Facilities, Capacities, Site Qualities

10. Global Events Event Name Event Start Time Event End Time
Event: Rain storm
Start time: 4:15pm
End Time: 4:30pm

11. Arrival Curves
Arrival Curves plot arrival rates for each entrance for each travel mode over a 24 hour day.

12. Agents Agent Travel Modes
Cars
Buses
Helicopters
Pedestrian
Agent Personality Preferences for site attractions
Agent Rules
Trip Planning Logic

13. Agent Rules
Rules are comprised of triggers or events generated by changes in the internal state of the agent, changes in the network or changes in global events.
The behavior generated by a rule causes the agent to find a new path to the facility.
IF Travelmode = ‘car’
AND Locale = ‘12 Apostles’
AND LocaleEntry = True
THEN Find Carpark

14. Way Finding Logic of Agents
Alternative paths are determined by:
Preferences for site attractions
Travel Time to alternative destinations
Time remaining in Agent’s trip
The number of facilities along a trail that satisfies the current motivation list
The available capacity of facilities.
Agents use a combination of fuzzy logic, gravity models, network algorithms and rules to maxmise satisfaction and minimise travel time.

15. Way finding Example Loch Ard Gorge
Because agents have different personalities, level of fitness, and trip durations, the trip planning logic results in different choice behavior between agents.

16. Typical Trips Entry and Exit nodes Destinations Arrival Curves
Agent Type
Mode of Travel

17. Management Scenarios
Scenarios allow managers to combine different network configurations, facilities, arrival rates and events to create a rich set of options.

18. Simulation Engine Generates agents
Executes event schedules such as opening and closing of gates, sunrise and sunset, and weather events.
Schedules statistical outputs

19. Example: 12 Apostles Master plan
Before and after simulation
Visitor growth projected to 10 years
Impact on facilities
Impact on visitor satisfaction
Impact on visitor movement patterns

20. Crowding, lack of parking, long queues

21. Scenarios Growth in visitor numbers Increase car and bus parking
Relocate parking
New Visitor Centre
New toilet block
Vehicular/Pedestrian separation

22. Scenarios Facility S1 S2 Viewing Platform 345 People Informal Lookout
Bus Park
6 Buses
12 Buses
Car Park
30 Cars
245 Cars
Visitor Centre
None
100 People
Toilet
29 People
Trailer Park
12 Cars

23. Simulation Runs
Scenario 1
Pre-master plan
Scenario 2
2001
Master plan

24. Results Visitor duration Visitor Satisfaction
Impact of visitor numbers on facility capacity

25. Visitor duration
Previous Facility v Current Facility
Length of Stay (Successful Trips)
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
20.0
22.0
24.0
26.0
28.0
30.0
32.0
34.0
36.0
38.0
40.0
42.0
44.0 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Hour of the Day
Length of Stay (minutes)
Previous Facility
Current Facility
Actual - Easter 2001
Actual - Easter 2001
(Average)
RBSim accurately modelled the increase in visitor duration for the new masterplan.

26. Visitor satisfaction – Visual Encounters
Crowding at peak times increases dramatically in 2011

27. Visitor Satisfaction – crowding
Opening overflow parking causes crowding at boardwalks

28. Visitor Satisfaction – Average queuing times for parking
Average queuing times at car parks increase to almost 2 minutes in 2011

29. Facility Management – available car parking
The car park is full from 1:00 PM TO 5:00 by 2006.

30. Management recommendations
Bus parking will need to be managed between 3:00 pm to 5:00 pm within 5 years (eg. use informal spaces near the visitor centre).
Limit car arrivals after 1:00 pm in 10 years or build an extension to the car park.
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.

31. Conclusions
RBSim is an effective framework for examining the impacts of recreation infrastructure on visitor movement.
Simulation provides a comprehensive tool for managing high use recreation settings.
Simulation can assist managers in refining facility management plans and the impact on visitor flows and satisfaction.

32. Future research - behaviour
More behavioural research is required to validate choice behaviour for a wide range of recreator types and environmental settings.
We hope to develop a library of agents that represent typical profiles and behaviour.
Study the effectiveness of alternative management controls on behavioural outcomes.
Study the management decision making process to determine most effective means of integrating simulation technology into the decision making process.

33. Future R & D - RBSim Add probabilistic rules
Expose a wider range of simulation states for agent rules.
Develop standard/automated statistical methods for summarizing simulation outputs.
Link behavioural simulations to other environmental impact models.
Develop new classes of agents.

34. The beginning …