Recreation Behavior Modeling and Simulation
Posted: March 17th, 2008 | 3 Comments »In the processing of building a coherent story around my research endeavors and considering potential outcome, I have returned to exploring how agent-based modeling techniques can help grapple with the validation and significance of user-genereated content in the realm of urban/mobility/tourist research. Current tourism simulation and modeling (see for instance TourSim) works mainly rely on specific surveys to build and evaluation the simulation. In addition, the data collected describe tourist behaviour such as spending habits, and psychological motivations for tourism. These sparse information make it hard to reflect the complexities of tourist behavior and build effective and efficient decision support tools to assess planning decisions. What is required for recreation planning, is verification of how tourists act spatially at recreation sites. However key variables such as the speed of tourist travel, wayfinding decisions, crowd avoidance, and other spatial behaviour, are not yet well understood to model the tourist visiting a city. One of my hypothesis is that digital footprints such as user-generated content can help develop agent-based models and simulations of tourist flows and movements (in that case through photography).
Similar to transport research, some tourist research collect quantitative data of tourist activity such walking and photography. In Building better agents: Geo-temporal tracking and analysis of tourist behavior the authors use quantitative data captured by sensors to build agent-based models of tourist behaviors. Their simulation provide one way for managers to accurately predict future impacts, and their spatial patterns of the develop of certain tourist areas. They analyze:
- detailed visitor counts
- average trip durations
- tourist behavior
- spatial patterns of movement
in order to reveal some group and individual behaviors:
Crowding: Determing through correlation whether people were spending less time, for example, on the viewing platform, during more crowded times of the day.
Graphing: Provide detailed information about times and sequences of travel for individuals and groups
Travel time: Time frequency distribution to be analyzed. Correlation between time spent in various area of the study site
Travel sequence: Tourist behavior can be devided into distinguishable groups based on movement sequences
However, the overall validity of the simulations remains uncertain without detailed calibration data. As described in Understanding of tourist dynamics from explicitly disclosed location information, the flickr dataset can provide more coarse grained quantitative observations of similar phenomenon. However, user-generated data can surpass the scalability and time constraints of surveys and sensor-based approaches. My current believe is that the availability of data over the world’s most photographed cities can allow me to validate tourist models build from user-generated content. Building such a model and validate it with simulation over several cities might be one nice outcome.
Next steps in that direction, Michael Batty wrote a book on Cities and Complexity: Understanding Cities with Cellular Automata, Agent-Based Models, and Fractals. Repast Simphony 1.0 has recently been released which includes a point and click interface for model development and full GIS support.
Fabien,
Thanks for linking to my work (TourSim). Just to clarify; the page you linked to are surveys for tourism planners to evaluate TourSim as a way to determine the usefulness of this approach in the context of their tourism planning needs. This data is not used to inform the agent behaviour within the model. Rather, this behaviour is drawn from a range of large surveys that gather specific destinations, accommodations, activities, and characteristics of each tourist trip. These surveys are products of Statistics Canada (Canadian Travel Survey and International Travel Survey). These behaviours are then validated compared to the 2004 Nova Scotia Tourist Exit Survey. If you take a look at the metadata behind these surveys, I think you would have a hard time calling the range of variables, or number of cases “sparse”!
I appreciate your perspective that flickr photos could be a useful source of data for determining places where tourists visit within a destination, but I’m not certain that this would encapsulate the true (very broad) range of tourist behaviours. I would think that there are numerous classes of tourist (and indeed people in society) who are not active on a site like flickr, or perhaps on the internet at all. If one were to parameterize agent behaviour based on this limited data source, I would hope that these behaviours were not ascribed to an entire body of tourists visiting a destination, but rather one tourist type (“the tech-savvy traveler” perhaps?).
Your mention of Gimblett’s work is a good one. He has come out with a recent edited book that details quite a bit about agent simulation for management within recreation areas; the caveat being that you can control the entry and exit of people within such a closed environment. This is one of the limitations of my work; in making a provincial-scale model, there is no way to truly control and track individuals, thus the reliance on survey data for agent parameterization. However, in discussion with tourism planners, the true utility of an ABM comes when looking at such large-scale dynamics, and how they play out over a competitive landscape. Most tourism planners I’ve spoken to seem content with their ability to manage and understand small-scale destinations using current methods.
Anyways, I’ll be interested to see how your work unfolds. I’d be interested to know what software platform you end up using to implement your model, and the type of feedback it receives. I’ve added a reference to some other articles you might find interesting.
All the best,
Peter A. Johnson,
McGill University,
Montreal, Canada
Deadman et al. The Role of Goal-Oriented Autonomous Agents in Modeling People-Environment Interactions in Forest Recreation. Mathematical and Computer Modelling (1994) vol. 20 (8) pp. 121-133
O’Connor et al. Geo-temporal tracking and analysis of tourist movement. Mathematics and Computers in Simulation (2005) vol. 69 pp. 135-150
Thanks a lot for your comments and references Peter. I did not mean to undermine your work that I find fascinating. I used the word “sparse” because your dataset covers a certain area and probably a certain type of tourism. My experience with the city of Florence is that surveys do not capture certain tourists. The type of visitor who are in the city for the day and not leave traces in visiting museums nor in hotels. Of course by no mean I pretend that the flickr dataset encapsulates the large range of tourists. However, these user-generated information have a richness not carried by traditional tourist surveys. They provide individual and group digital traces of important aspects of a city during a visit. One aspect of my research is to find out more about the profile of people describing, uploading and georeferencing photos. In a second step I will not to valid these data with other datasets (such as the surveys you mention).
Hy,
I see that you are interested in Recreation behavior about Modeling and simulation system, can I propose you one excellent book about Modeling and simulation?
I find one amazing free to download. Maybe I can help you with your research?!
This book collects original and innovative research studies concerning modeling and simulation of physical systems in a very wide range of applications, encompassing micro-electric-mechanical systems, measurement instrumentation, catalytic reactors, bio mechanical applications, biological and chemical sensors, magneto-sensitive materials, silicon photonic devices, electronic devices, optical fibers, electro-microfluidic systems, composite materials, fuel cells, indoor air-conditioning systems, active magnetic levitation systems and more.
You can find it here: http://sciyo.com/books/show/title/modelling_and_simulation