UDel Mobility Model

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UDel Mobility Model Simulator

• Jonghyun Kim
• Advisor Dr. Bohacek
• Email kim_at_eecis.udel.edu

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Contents

• Objectives
• Simulator Design Modeling Node Mobility
• Demo Simulation
• Simple Simulation Set
• Simulation Results
• Summary
• Future work

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Objectives
1. Implement realistic mobility simulator for
pedestrian and car by applying UDel mobility
model 2. Generate realistic mobility data from
our simulator 3. Compare our simulated mobility
data to actual measured data from pedestrian
literature 4. Based on our mobility data, we will
investigate impact on performance of MANET
routing protocols (future work)
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Simulator Design Modeling Node Mobility
1. Project overview
Map builder
1. Generate map data
UDel Mobility Simulator
Raytracing
2. Generate realistic mobility data
3. Generate Pathloss data
QualNet
4. Generate any statistical data ex ) data
about routing protocols
5. Analyze the data and get the result
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Simulator Design Modeling Node Mobility
Map Builder
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Simulator Design Modeling Node Mobility
UDel Mobility Simulator
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Simulator Design Modeling Node Mobility
2. Implementation method of UDel Mobility
Simulator
Discrete event method - Some different
events are specified - Whenever an event
occurs, the function for the event is
executed Events - REACH_END_OF_SEGMENT -
CATCH_UP - EXIT_FIFO - START_UP -
MEET_IN_OPPOSITION - SEND_NEXT_CAR
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Simulator Design Modeling Node Mobility
Events - REACH_END_OF_SEGMENT
Mobile node
Lane
Segment
Segment can be one of sidewalk, roadway, hallway,
or walkway Mobile node can be one of pedestrian
or car
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Simulator Design Modeling Node Mobility
Events - REACH_END_OF_SEGMENT
Mobile node
Lane
Segment
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Simulator Design Modeling Node Mobility
Events - CATCH_UP
Lane
Node A
Node B
Lets assume that node A speed is faster than
Node Bs speed
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Simulator Design Modeling Node Mobility
Events - CATCH_UP
Lane
A
B
As current lane
As changing lane
Node A should decide whether or not it changes
current lane to overtake front node B If there
is enough space in the changing lane, node A has
chance to change lane Probability of changing
lane -V1 the average speed of all
nodes on current lane -V2 min (the
average speed of all nodes on changing lane, As
desired speed) -For pedestrian, A
-0.225 B 1.7 -For car, A -0.225 B
0.1
Reference K. I. Ahmed, Modeling drivers
acceleration and lane changing behavior, Ph.D.
dissertation, MIT, 1999
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Simulator Design Modeling Node Mobility
Events - EXIT_FIFO
Lets assume that all nodes speed is the same
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Simulator Design Modeling Node Mobility
Events - EXIT_FIFO
FIFO

Since there is no enough distance to enter next
segment, green node goes into FIFO How much
distance is needed for green node to enter the
next segment ?
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Simulator Design Modeling Node Mobility
Distance-Speed Relationship for Pedestrians
Reference S. J. Older, Movement of pedestrian
on footways in shopping street, traffic
engineering and control, pp. 160163, 1968.
F. P. D. Navin and R. J. Wheeler,
Pedestrian flow characteristics, traffic
engineering, pp. 3036, 1969.
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Simulator Design Modeling Node Mobility
We derived the equation below based on
distance-speed relationship For pedestrian,
Distance (S, S) S
Desired speed, S Current speed,
Dmin minimum distance between people (at least
0.35m) For car, Distance (S) A
B S In dry conditions, (A,
B) (1.78, 10) and (1.45, 7.8) In wet
conditions, (A, B) (0.415, 8.3) and (0.230, 6.0)
S. Shekleton, A GPS study of car following
theory, in Conference of Australian Institutes
of Transport Research (CAITR), 2002. T. Dijker,
P. H. L. Bovy, and R. G. M. M. Vermijs, Car
following behavior in different flow regimes, in
Motorway Traffic Flow Analysis pp. 4970. J. Piao
and M. McDonald Analysis of stop and go driving
behavior through a floating vehicle approach, in
Proc. Of the IEEE Intelligent Vehicles Symposium,
2003
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Simulator Design Modeling Node Mobility
Events - EXIT_FIFO
FIFO

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Simulator Design Modeling Node Mobility
Events - EXIT_FIFO
FIFO

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Simulator Design Modeling Node Mobility
Events - EXIT_FIFO
Enough distance is now available
FIFO

FIFO

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Simulator Design Modeling Node Mobility
Events - EXIT_FIFO
FIFO

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Simulator Design Modeling Node Mobility
Events - EXIT_FIFO
FIFO

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Simulator Design Modeling Node Mobility
Events - EXIT_FIFO
FIFO

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Simulator Design Modeling Node Mobility
Events - START_UP
Enough distance is now available
FIFO
FIFO


Yellow node starts up
When red node exits FIFO, it checks to see if
the following node stopped If the following
node stopped, red node makes it start to move
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Simulator Design Modeling Node Mobility
Events - START_UP
White node starts up
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Simulator Design Modeling Node Mobility
Events - START_UP
Show mobility simulator version 1.0
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Simulator Design Modeling Node Mobility
Events - MEET_IN_OPPOSITION
Right-hand side
Left-hand side
Right-hand side
Left-hand side
Each lane is bi-directional
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Simulator Design Modeling Node Mobility
Events - MEET_IN_OPPOSITION
Injected
Right-hand side
Left-hand side
Right-hand side
Left-hand side
When two nodes meet in opposition, left-hand
side node gives a way to right-hand side node
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Simulator Design Modeling Node Mobility
Events - MEET_IN_OPPOSITION
Right-hand side
Left-hand side
Right-hand side
Left-hand side
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Simulator Design Modeling Node Mobility
Events - MEET_IN_OPPOSITION
Right-hand side
Left-hand side
Injected
Right-hand side
Left-hand side
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Simulator Design Modeling Node Mobility
Events - MEET_IN_OPPOSITION
Right-hand side
Left-hand side
Right-hand side
Left-hand side
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Simulator Design Modeling Node Mobility
Events - MEET_IN_OPPOSITION
Right-hand side
Left-hand side
Right-hand side
Left-hand side
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Simulator Design Modeling Node Mobility
Events - SEND_NEXT_CAR
Urban street
During simulation, cars will exit or enter the
city When its time for some cars to enter the
city, this event occurs The number of vehicles
that enter the city per traffic signal period is
Poisson distributed mean
Reference A. Kamarajugadda and B. Park,
Stochastic traffic signal timing optimization,
Center for transportation studies at the
university of Virginia, Tech. Rep.
UVACTS-15-0-44, 2003.
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Simulator Design Modeling Node Mobility
Events - SEND_NEXT_CAR
Urban street
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Simulator Design Modeling Node Mobility
Events - SEND_NEXT_CAR
Urban street
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Simulator Design Modeling Node Mobility
Events - SEND_NEXT_CAR
Urban street
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Simulator Design Modeling Node Mobility
3. Trip generation
1) Pedestrian case - Pedestrian has a home
office - Pedestrian initiates trips from its
office at random times - Pedestrian chooses a
destination - Destination can be an office,
group meeting location or class room -
Pedestrian goes to the destination with desired
speed through shortest path Desired speed
of pedestrian Pedestrian desired speeds
are approximately Gaussian distributed
Mean speed 1.34 m/s Standard deviation
0.26 Minimum speed 0.7 m/s
Maximum speed 1.86 m/s
Reference D. Helbing, Sexual differences in
human crowd motion, Nature, vol. 240, p. 252,
1972 The statistics of crowd
fluids, Nature, vol. 229, p. 381, 1971
G. K. Still, Crowd dynamics, Ph.D.
dissertation, university of warwick, 2000.
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Simulator Design Modeling Node Mobility
First step to choose a destination Fraction
of trips that leave a building U/M M
mean time between trips that leave a building
(M depends on the characteristic of
the building and time of the day) U
mean time between trips (i.e pause time) as
exponentially distributed Second step to
choose a destination Probability of selecting a
range of distance to travel
D2
D3
D1
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Simulator Design Modeling Node Mobility
CCDF of Distance Traveled During Outdoor
Walking Trips
Reference B. Pushkarev and J. M. Zupan, Urban
Space for Pedestrians. MIT press, 1975
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Simulator Design Modeling Node Mobility
Simple group mobility - Some nodes will join
group - All nodes in group move together -
Speed of all nodes is the average over desired
speed - All nodes in group occupy the whole
lanes - A node behind group just follow group
even if the node is faster than group - Group
just follow a node ahead even if group speed is
faster than node - Groups of pedestrians play
an important role in platooning Group trip -
There exist lots of trip cases ex ) case1
office ? class ? a walkway ? disperse
case2 office ? class ? hallway ? disperse -
What is the probability for each case ?
Traffic light - If there is traffic light on
segment and red light is on, pedestrian stops
until green light is on
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Simulator Design Modeling Node Mobility
2) Car case - Car initiates trips from a
certain location - At each intersection, cars
turn or go straight according to the turning
probabilities Turning probability 0.2
Desired speed of car Cars speed/ speed limit
is approximately Gaussian distributed Mean
0.78 m/s Standard deviation 0.26
Speed limit 13.4 m/s Minimum speed
13.4?0.5 m/s Maximum speed 13.4?1.4 m/s
Desired speed Speed limit ? Random number
J. E. Hummer, Unconventional left-turn
alternatives for urban and suburban arterials,
ITE Journal, vol. 68, 1998 M. J. Bayarri, J. O.
Berger, G. Molina, N. M. Rouphail, and J. Sacks,
Assessing uncertainties in traffic simulation A
key component in model calibration and


validation, National Institute of
Statistical Sciences, Tech. Rep. 137, 2003.
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Simulator Design Modeling Node Mobility
CDF of the ratio of observed speeds to speed
limit and CDF of a fitted Gaussian distribution
Reference Jianhe Du and Lisa Aultman-Hall, An
Investigation of the Distribution of Driving
Speeds Using in-Vehicle GPS Data, Vermont
Institute of Transportation Engineers Annual
Meeting, 2004, available at http//www.neite.org/
vt/dist1_2004/
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Demo Simulation
Parameters - Number of pedestrian nodes
3,000 - Number of car nodes 150 -
Simulation time 3,000 seconds - Map UD map
- Number of lanes on walkway 4 - Traffic
light period 90s - U 300 seconds
Show mobility simulator version 1.05 with UDEL map
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Simple Simulation Set
Map1 with building on measuring walkway
measuring point
150 meter
Map2 without building on measuring walkway
measuring point
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Simple Simulation Set
Parameters - Number of nodes 10,000 -
Simulation time 1,800 seconds - Map Map1,
Map2 - Number of lanes on walkway 4, 8, 16 -
Traffic light period 90s, 120s, 150s, 180s,
210s - Passing rule easy to pass, hard to
pass, our mobility passing rule - U 18000,
12000, 6000, 4800, 3000, 1200, 600, 300 seconds
- Mobility model constrained random way point,
our mobility model Measurement data - Passing
time, current speed, desired speed, node ID,
direction
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Simple Simulation Set
Random way point model
Initial point based on random seed
Step 1
Choose next destination randomly
Step 2
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Simple Simulation Set
Step 3
Pause for some random time
Choose next destination randomly
Step 4
Step 3 ? Step 4 ? repeat
Show random way point model movie
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Simulation Results
Reference B. Pushkarev and J. M. Zupan, Urban
Space for Pedestrians. MIT press, 1975 PP. 94
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Simulation Results
UDel mobility model conforms to the actual
measurement
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Simulation Results
Since there is no interaction among pedestrians,
flow rate is so high So, constrained random way
point model is not realistic
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Summary
1. MANET protocol performance evaluation varies
by mobile node mobility 2. Researchers may
use random way point mobility model as mobile
node mobility 3. As we saw, constrained random
way point is not realistic 4. UDel mobility model
approaches realistic model 5. MANET protocol
performance needs to be re- evaluated based
on UDel mobility model
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Future work

1. Investigate impact of UDel mobility model on
   performance of MANET routing protocols