Theme Park Mobility in Disaster Scenariosgsolmaz/publications/GC13-ConferencePresentatio… · I...

Theme Park Mobility in Disaster Scenarios

Gurkan Solmaz and Damla Turgut

Department of Electrical Engineering and Computer ScienceUniversity of Central Florida - Orlando, FL

December 11, 2013

Solmaz, Turgut (UCF) GLOBECOM 2013 December 11, 2013 1 / 20

1 Motivation

2 Modeling the theme park

3 Mobility of the visitors

4 Simulation study

5 Conclusion


Motivation

ProblemI Need for scenario-specific modeling of human mobilityI Focusing on natural and man-made disasters of theme parksI Performance evaluation of the wireless ad hoc networksI Various crowd management and evacuation strategies for theme parks

can be tested

ObjectiveI Realistic modeling and simulation of human mobility in disaster

scenarios


Characteristics of theme parks

Theme parks consist of attractions which are entertainment placesI Rides, restaurants, and places for other activities.

Attractions are connected to each other and to exit points (gates) byroad

Roads are usually used by pedestrians and they have capacities (i.e.width)

Capacities of roads have effects on pedestrian flows


Modeling the theme park

We model theme parks as the combination of roads, obstacles, lands,and disaster events

Each road contains a set of waypoints (the movement points for thevisitors)

The gates are considered as the target locations

Roads show the possible ways to reach the target locations

Physical obstacles for free movement of the visitorsI Man-made obstacles: Buildings, fences, walls ...I Natural obstacles: Lakes, trees, forest, river ...

Disaster areas are classified as the circular red-zones

The red-zones prevent the use of some roads in their active times


Theme park maps

The model of the theme park can be created synthetically or usingreal maps

Using OpenStreetMap (OSM)† to extract the real theme park maps

Parsing the OSM data to generate the roads, the obstacles, the lands,and the gates.

The waypoints are collected using the OSM data

Connecting the consecutive waypoints to create the roads

Assigning width to the roads according to OSM types (footway, path,and pedestrian way)

†M. Haklay and P. Weber, “OpenStreetMap: User-generated street maps.” Pervasive Computing, vol. 7, no. 4, pp. 12?18,

Dec. 2008.


Theme park mapsr

up

S A704-L A704-L

L-4

07A

Main Street

Carolwood Park

Frontierland

Magic Kingdom

The H

u

b

te

ert

S ni

aM

evir

D dlr

oW

yaW seitilicaF

Floridian Way

Flor

idia

n W

ay

Walt Disney World Railroad

Magic Kingdom Drive

evir

D dlr

oW

World D

rive

Caribbean W

ayyaW liaronoM

etuoR edaraP

dnalrietnorF

Tom

orro

wla

nd Indy Speedw

ayMagic Kingdom

TheNew

FantasylandNow

Open! DumboThe

FlyingElephant

First Aid

LibertyBell

Harper'sMill

StoryTimeSpot

JungleCruise

Cinderella'sCastleMoat

Mickey'sPhilharmagic

Monsters,Inc.

LaughFloor

Stitch'sGreat

Escape

It'sa SmallWorld

TheHauntedMansion

Tom Sawyer Island

MadTeaParty

SpaceMountain

Tom Sawyer Island

SplashMountain

Piratesofthe

Caribbean

The map of the Magic Kingdom extracted from (OSM) and the processedmap with 1300 waypoints (dots)


Macro-mobility

Each visitor selects an exit gate as a target point

The visitor tries to reach the target point by moving among thewaypoints

The next destination point is selected among all the visible waypoints

The visited waypoints or the waypoints in the red-zone are excluded

The movement along the way is constrained byI Knowledge about the world, obstacles and possible active red-zones

Random exploration if no available waypoint


Mobility of the visitors

Epcot theme park model including 20 visitors (triangles), 2300 waypoints,and 5 red-zones


Mobility of the visitors

Each visitor has a maximum speed

Maximum speed depends on physical attributes such as age, gender,and weight

Maximum speed: The visitor is completely free to walk w/odisturbance or the obstacles

The actual speed of a visitor is mostly less than the maximum speed

Due to the effects of the social interactions between visitors


Micro-mobility

Micro-mobility: the mobility of a visitor between two consecutivewaypoints

Social force model (SFM)† is used for speed and the directions of themovements

According to the social force concept, behavioral changes in thehuman are caused by the combination of the social interactions

We apply this model for the micro-mobility of the visitors

†D. Helbing and A. Johansson, “Pedestrian, crowd and evacuation dynamics,” Encyclopedia of Complexity and Systems Science,vol. 16, no. 4, pp. 6476?6495, 2010


Micro-mobility

Using SFM, we model the social forces on the visitors according totheir social interactions with the environment

Sum of the social forces by the people effects the velocity of a visitor

The usage of the same roads by the visitors causes an increase in thesocial interactions

Increase in the interactions slows down the flow of the visitors alongthe roads

Social force model is the best-fit model:I Theme parks are crowded areas with roads only used by pedestriansI Representing the crowd dynamics and the micro-mobility behavior


Simulation setup

TP-D, SLAW, RWP, and GPS traces are compared

Disaster mobility in Magic Kingdom park is simulated

simulation time 1000s

sampling time 0.5s

number of visitors 1000

min speed 0.5m/s

max speed 2.5m/s

number of red-zones

20

red-zone active time 500s

red-zone radius 50m

visibility 50m

random move dis-tance

10m

SFM - interactionstrength (A)

0.11 ±0.06

SFM - interactionrange (B)

0.84 ±0.63

SFM - relaxationtime (τ)

0.5s

SFM - λ 0.1


Simulation setup

The simulation of 2000 visitors and the impact of red zones in MagicKingdom


Simulation results

0 10 20 30 40 50 60 70 800

20

40

60

80

100

120

Flight lenghts (m)

Num

ber

of fl

ight

s

Simulation run 1Simulation run 2Simulation run 3Simulation run 4Simulation run 5

Consistency of flight length distributions of the TP-D model

Peak points: visibility and random-move parameters


Simulation results

50 100 150 200 250 300 350 400 450 5000

20

40

60

80

100

120

Flight lenghts (m)

Num

ber

of fl

ight

s

TP−DTPSLAWRWPGPS

Flight length distributions for TP-D, TP, SLAW, RWP, and the GPStracesDue to local knowledge, long flights w/o pausing is not possible


Simulation results

TP−D TP SLAW RWP GPS0

100

200

300

400

500

600

Ave

rage

flig

ht le

ngth

s (m

)

Shorter flights due to constraints such as visibility, red-zones, and thecrowd itself


Simulation results

20 30 35 40 50 600

100

200

300

400

500

600

700

800

900

1000

Visibility (m)

Ave

rage

eva

cuat

ion

time

(s)

Increase in the knowledge causes decrease in evacuation times


Simulation results

0 50 100 150 200280

300

320

340

360

380

400

420

Number of red zones

Ave

rage

eva

cuat

ion

time

(s)

The red-zones prevent the regular flow of the visitorsTunneling visitors to other ways increases the average evacuationtimes


Conclusion

Proposed a mobility model (TP-D) of the theme park visitors indisaster scenarios

Used real theme park maps to model the environment

Visitor movement is modeled using the theme park models and theSFM

Future work:

Evaluating the performance of WSNs with mobile sinks

Simulation of evacuation strategies


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