A Mobility Model of Theme Park Visitors
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A Mobility Model of Theme Park Visitors Gürkan Solmaz and Damla Turgut Department of Electrical Engineering and Computer Science, University of Central Florida Abstract We present a novel human mobility model for theme parks. In our model, the non-determinism of move- ment decisions of visitors is combined with deter- ministic 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) com- pared to current state-of-the-art movement models. Introduction Recent advances in mobile devices enabled the in- creased popularity and usage of mobile applications. The realistic modeling of human movement has sig- nificant importance for the performance assessment of mobile wireless systems. Motivation • 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 Contributions • A novel scenario-specific mobility model • Representing the social behavior of gathering in attractions, spending time in queues, and the least-action principle • The best statistical match to the GPS traces amongst the tested synthetic models Modeling a Theme Park • Fractal points and clusters Figure 1: Clusters generated by DBScan • Self-similar fractal points and least action trip planning satisfy fundamental statistical features of human walks. • The areas with high densities represent the popular places. • Attractions Attractions are represented by queueing models. The types and the weights of the queues are based on the previous work on theme park design. • Landmark Figure 2: Left: a landmark model, right: trajectory of a visitor. A landmark is a place where there are multiple static queues, static noise points, and mobile nodes. Visitor Model removed initial moving inNoise Point inQueue t = 0 arrived at noise point arrived at attraction waiting time or hangout time passed serviced hangout time passed Figure 3: States of a visitor. • Visitors pre-plan their visit by deciding hangout times and the set of attractions to be visited. • The modified LATP algorithm decides the next destinations and minimizes the travel distances. Theme Park with Landmarks Figure 4: Application to a real-world scenario: Disney World. The mobility model can be easily applied to model a complete theme park scenario. By separating landmarks as vertices and adding weighted non- directional edges, we can generalize the mobility from a landmark to the whole theme park. Fractal points Clusters DBScan Classify clusters Attractions & noise points Add visitors Landmark Theme park Graph with landmarks & roads Figure 5: The phases of modeling a theme park. Simulation Results 200 400 600 800 1000 0 50 100 150 200 Flight lengths (m) Number of flights TP SLAW RWP GPS traces Figure 6: Flight length distributions. Set 1 Set 2 Set 3 Set 4 Set 5 0 5 10 15 20 Mobility traces Number of waiting points per hour TP SLAW RWP GPS Figure 7: Number of waiting points. 0 200 400 600 800 1000 0 100 200 300 400 500 600 700 Waiting times (sec) Number of waiting times TP SLAW GPS traces Figure 8: Waiting time distributions. References [1] G. Solmaz, M.I. Akbas, and D. Turgut. A mobility model of theme park visitors. IEEE Trans. on Mobile Computing, February 2015. [2] G. Solmaz and D. Turgut. Pedestrian mobility in theme park disasters. IEEE Communications Magazine, June 2015.
Transcript of A Mobility Model of Theme Park Visitors
A Mobility Model of Theme Park VisitorsGürkan Solmaz and Damla Turgut
Department of Electrical Engineering and Computer Science, University of Central Florida
Abstract
We present a novel human mobility model for themeparks. In our model, the non-determinism of move-ment decisions of visitors is combined with deter-ministic behavior of attractions. The attractionsare categorized as rides, restaurants, and live shows.The time spent at these attractions are computedusing queueing-theoretic models. The realism ofthe model is evaluated through extensive simulationsand compared with existing mobility models and theGPS traces of theme park visitors. The results showthat our proposed model provides a better match tothe real-world data (from CRAWDAD archive) com-pared to current state-of-the-art movement models.
Introduction
Recent advances in mobile devices enabled the in-creased popularity and usage of mobile applications.The realistic modeling of human movement has sig-nificant importance for the performance assessmentof mobile wireless systems.
Motivation•Mobility models drastically changeperformance results of networks
•Early models based on random walks are verycoarse approximations
•Focusing on human walks due to limitedvehicle use in theme parks
Outcomes of our model are useful for:• Performance evaluation of mobile applications• Theme park administration
Contributions•A novel scenario-specific mobility model•Representing the social behavior of gatheringin attractions, spending time in queues, andthe least-action principle
•The best statistical match to the GPS tracesamongst the tested synthetic models
Modeling a Theme Park
• Fractal points and clusters
Figure 1: Clusters generatedby DBScan
•Self-similar fractalpoints and leastaction trip planningsatisfy fundamentalstatistical features ofhuman walks.
•The areas with highdensities representthe popular places.
• Attractions
Attractions are represented by queueing models.The types and the weights of the queues are basedon the previous work on theme park design.
• Landmark
Figure 2: Left: a landmark model, right: trajectory of a visitor.
A landmark is a place where there are multiple staticqueues, static noise points, and mobile nodes.
Visitor Model
removedinitial moving
inNoise Point
inQueue
t = 0
arrived at
noise point
arrived at attraction
waiting tim
e or
hangout time passe
d
serviced
hangout time passed
Figure 3: States of a visitor.
•Visitors pre-plan their visit by deciding hangouttimes and the set of attractions to be visited.
•The modified LATP algorithm decides the nextdestinations and minimizes the travel distances.
Theme Park with Landmarks
Figure 4: Application to a real-world scenario: Disney World.
The mobility model can be easily applied to modela complete theme park scenario. By separatinglandmarks as vertices and adding weighted non-directional edges, we can generalize the mobilityfrom a landmark to the whole theme park.
Fractal points ClustersDBScan Classify clusters
Attractions & noise points Add visitors
Landmark Theme parkGraph with
landmarks & roads
Figure 5: The phases of modeling a theme park.
Simulation Results
200 400 600 800 10000
50
100
150
200
Flight lengths (m)
Num
ber
of fl
ight
s
TPSLAWRWPGPS traces
Figure 6: Flight length distributions.
Set 1 Set 2 Set 3 Set 4 Set 50
5
10
15
20
Mobility traces
Num
ber
of w
aitin
g po
ints
per
hou
r
TPSLAWRWPGPS
Figure 7: Number of waiting points.
0 200 400 600 800 10000
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200
300
400
500
600
700
Waiting times (sec)
Num
ber
of w
aitin
g tim
es
TPSLAWGPS traces
Figure 8: Waiting time distributions.
References
[1] G. Solmaz, M.I. Akbas, and D. Turgut.A mobility model of theme park visitors.IEEE Trans. on Mobile Computing, February 2015.
[2] G. Solmaz and D. Turgut.Pedestrian mobility in theme park disasters.IEEE Communications Magazine, June 2015.
Contact Information
•Web: http://www.cs.ucf.edu/ gsolmaz•Email: [email protected]•Phone: +1 (407) 399 6241
