udel mobility model & simulator jonghyun kim advisor : dr. bohacek email : kim@eecis.udel.edu

UDel Mobility Model &Simulator

Jonghyun Kim

Advisor : Dr. Bohacek

Email : kim@eecis.udel.edu

Contents

- Objectives

- Simulator Design & Modeling Node Mobility

- Demo Simulation

- Simple Simulation Set

- Simulation Results

- Summary

- Future work

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)

Simulator Design & Modeling Node Mobility

1. Project overview

Map builder

UDel Mobility Simulator

QualNet

1. Generate map data

2. Generate realistic mobility data

4. Generate any statistical data ex ) data about routing protocols

Raytracing

3. Generate Pathloss data

5. Analyze the data and get the result

Map Builder

UDel Mobility Simulator

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

* Events - REACH_END_OF_SEGMENT

Segment can be one of sidewalk, roadway, hallway, or walkwayMobile node can be one of pedestrian or car

Segment

Mobile nodeLane

* Events - REACH_END_OF_SEGMENT

Segment

Mobile nodeLane

* Events - CATCH_UP

Let’s assume that node A’ speed is faster than Node B’s speed

Node A Node B

* Events - CATCH_UP

• 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, A’s desired speed) -For pedestrian, A = −0.225 B = 1.7 -For car, A = −0.225 B = 0.1

)1/(1 ))21(( VVBAe

Reference : K. I. Ahmed, “Modeling drivers’ acceleration and lane changing behavior,” Ph.D. dissertation, MIT, 1999

A’s changing lane

A’s current lane

* Events - EXIT_FIFO

• Let’s assume that all nodes’ speed is the same

• 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 ?

Distance-Speed Relationship for Pedestrians

Reference : S. J. Older, “Movement of pedestrian on footways in shopping street,” traffic engineering and control, pp. 160–163, 1968. F. P. D. Navin and R. J. Wheeler, “Pedestrian flow characteristics,” traffic engineering, pp. 30–36, 1969.

• 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)

)08.1/( *min SSDS

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. 49–70.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

Enough distance is now available

* Events - START_UP

…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

Enough distance is now available

* Events - START_UP

White node starts up

* Events - START_UP

Show mobility simulator version 1.0

* Events - MEET_IN_OPPOSITION

Right-hand side

Right-hand sideLeft-hand side

Left-hand side

• Each lane is bi-directional

Right-hand side

Left-hand side

• When two nodes meet in opposition, left-hand side node gives a way to right-hand side node

Injected

Right-hand side

Left-hand side

Right-hand side

Left-hand side

Injected

Right-hand side

Left-hand side

Right-hand side

Left-hand side

* Events - SEND_NEXT_CAR

Urban street

• During simulation, cars will exit or enter the city• When it’s 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 : oadsEntering RNumber of

ing)-prob_turn(iod Signal Perλ rtRateVehicleSta 1

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.

Urban street

3. Trip generation1) 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.

* 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 :

])1[400/1(])[400/1( iDiD ee

• CCDF of Distance Traveled During Outdoor Walking Trips

Reference : B. Pushkarev and J. M. Zupan, Urban Space for Pedestrians. MIT press, 1975

* 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

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 Car’s speed/ speed limit is approximately Gaussian distributed Mean = 0.78 m/s Standard deviation = 0.26 Speed limit = 13.4 m/s Minimum speed = 13.40.5 m/s Maximum speed = 13.41.4 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.

CDF of the ratio of observed speeds to speed limit andCDF 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/

Demo Simulation

Show mobility simulator version 1.05 with UDEL map

*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

Simple Simulation Set

• Map1 with building on measuring walkway

• Map2 without building on measuring walkway

measuringpoint150 meter

measuringpoint

*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

Choose next destination randomly

Random way point model

Initial point based on random seed

Step 1

Step 2

Pause for some random time

Choose next destination randomly

Step 3

Step 4

Step 3 Step 4 repeat

Show random way point model movie

Simulation Results

Reference : B. Pushkarev and J. M. Zupan, Urban Space for Pedestrians. MIT press, 1975 PP. 94

Simulation Results

UDel mobility model conforms to the actual measurement

Simulation Results

Since there is no interaction among pedestrians, flow rate is so highSo, constrained random way point model is not realistic

Summary

1. MANET protocol performance evaluation varies by mobile node mobility 2. Researchers may use random way point mobility model as mobile node mobility3. As we saw, constrained random way point is not

realistic4. UDel mobility model approaches realistic model5. MANET protocol performance needs to be re- evaluated based on UDel mobility model

Future work

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

udel mobility model & simulator jonghyun kim advisor : dr. bohacek email : kim@eecis.udel.edu

realistic mobility simulator

realistic mobility data

green node

simulated mobility data

walkway mobile node

result slide

map data

nodes speed

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