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

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

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.

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

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)

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)

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

RBSim Object Model

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

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

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

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

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

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.

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.

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

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

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

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

Crowding, lack of parking, long queues

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

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

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

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

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 - 2001 Current Facility - 2001 Actual - Easter 2001 Actual - Easter 2001 (Average) RBSim accurately modelled the increase in visitor duration for the new masterplan.

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

Visitor Satisfaction – crowding Opening overflow parking causes crowding at boardwalks

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

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

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.

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.

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.

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.

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