Mobility Model of Theme Park Visitors


Theme park mobility model (TP) is a scenario-specific human walk model. In our model, the non-determinism of movement decisions of theme park visitors is combined with deterministic behavior of attractions. The attractions are categorized as rides, restaurants, and live shows. The time spent at these attractions are computed using queueing-theoretic models.

The realism of the model is evaluated through extensive simulations and compared with existing mobility models and the GPS traces of theme park visitors. The results show that our proposed model provides a better match to the real-world data (from CRAWDAD archive) compared to current state-of-the-art movement models.


Recent advances in mobile devices enabled the increased popularity and usage of mobile applications. The realistic modeling of human movement has significant importance for the performance assessment of mobile wireless systems.


  • Mobility models drastically change performance results of networks
  • Early models based on random walks are very coarse approximations
  • Focusing on human walks due to limited vehicle use in theme parks

Outcomes of our model are useful for

  • Performance evaluation of mobile applications
  • Theme park administration


  • A novel scenario-specific mobility model
  • Representing the social behavior of gathering in attractions, spending time in queues, and the least-action principle
  • The outcomes are synthetically generated mobility traces that are useful for simulations
  • The best statistical match to the GPS traces amongst the tested synthetic models

Downloads & Media

  • Download IEEE Transactions on Mobile Computing journal article: Article
  • Download IEEE LCN'12 conference paper: Paper
  • Download IEEE LCN'12 conference presentation: Presentation
  • Download UCF Graduate Research Forum poster: Poster
  • Link to GitHub repository for the simulation: Source Code

Simulation Videos

The below two videos show the simulation of the mobility model. Left: 1 person. Right: 2000 people

Related Publications

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