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UDel Mobility Model & Simulator

This project aims to implement a realistic mobility simulator for pedestrians and cars using the UDel mobility model. It will generate mobility data and compare it to actual measured data to analyze the impact on MANET routing protocols.

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UDel Mobility Model & Simulator

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  1. UDel Mobility Model &Simulator Jonghyun Kim Advisor : Dr. Bohacek Email : kim@eecis.udel.edu

  2. Contents • Objectives • Simulator Design & Modeling Node Mobility • Demo Simulation • Simple Simulation Set • Simulation Results • Summary • Future work

  3. 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)

  4. 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

  5. Simulator Design & Modeling Node Mobility Map Builder

  6. Simulator Design & Modeling Node Mobility UDel Mobility Simulator

  7. 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

  8. Simulator Design & Modeling Node Mobility * Events - REACH_END_OF_SEGMENT Lane Mobile node Segment Segment can be one of sidewalk, roadway, hallway, or walkway Mobile node can be one of pedestrian or car

  9. Simulator Design & Modeling Node Mobility * Events - REACH_END_OF_SEGMENT Lane Mobile node Segment

  10. Simulator Design & Modeling Node Mobility * Events - CATCH_UP Lane Node A Node B Let’s assume that node A’ speed is faster than Node B’s speed

  11. Simulator Design & Modeling Node Mobility * Events - CATCH_UP Lane A B A’s current lane A’s 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, A’s 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

  12. Simulator Design & Modeling Node Mobility * Events - EXIT_FIFO • Let’s assume that all nodes’ speed is the same

  13. 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 ?

  14. 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. 160–163, 1968. F. P. D. Navin and R. J. Wheeler, “Pedestrian flow characteristics,” traffic engineering, pp. 30–36, 1969.

  15. 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. 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

  16. Simulator Design & Modeling Node Mobility * Events - EXIT_FIFO FIFO …

  17. Simulator Design & Modeling Node Mobility * Events - EXIT_FIFO FIFO …

  18. Simulator Design & Modeling Node Mobility * Events - EXIT_FIFO Enough distance is now available FIFO … FIFO …

  19. Simulator Design & Modeling Node Mobility * Events - EXIT_FIFO FIFO …

  20. Simulator Design & Modeling Node Mobility * Events - EXIT_FIFO FIFO …

  21. Simulator Design & Modeling Node Mobility * Events - EXIT_FIFO FIFO …

  22. 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

  23. Simulator Design & Modeling Node Mobility * Events - START_UP White node starts up

  24. Simulator Design & Modeling Node Mobility * Events - START_UP Show mobility simulator version 1.0

  25. Simulator Design & Modeling Node Mobility * Events - MEET_IN_OPPOSITION Left-hand side Right-hand side Right-hand side Left-hand side • Each lane is bi-directional

  26. Simulator Design & Modeling Node Mobility * Events - MEET_IN_OPPOSITION Injected Left-hand side Right-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

  27. Simulator Design & Modeling Node Mobility * Events - MEET_IN_OPPOSITION Left-hand side Right-hand side Right-hand side Left-hand side

  28. Simulator Design & Modeling Node Mobility * Events - MEET_IN_OPPOSITION Left-hand side Right-hand side Injected Right-hand side Left-hand side

  29. Simulator Design & Modeling Node Mobility * Events - MEET_IN_OPPOSITION Left-hand side Right-hand side Right-hand side Left-hand side

  30. Simulator Design & Modeling Node Mobility * Events - MEET_IN_OPPOSITION Left-hand side Right-hand side Right-hand side Left-hand side

  31. Simulator Design & Modeling Node Mobility * 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 : 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.

  32. Simulator Design & Modeling Node Mobility * Events - SEND_NEXT_CAR Urban street

  33. Simulator Design & Modeling Node Mobility * Events - SEND_NEXT_CAR Urban street

  34. Simulator Design & Modeling Node Mobility * Events - SEND_NEXT_CAR Urban street

  35. 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.

  36. 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 : D[2] D[3] D[1]

  37. 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

  38. 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

  39. 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 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 = 13.4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.

  40. 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/

  41. 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

  42. Simple Simulation Set • Map1 with building on measuring walkway measuring point 150 meter • Map2 without building on measuring walkway measuring point

  43. 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

  44. Simple Simulation Set Random way point model Initial point based on random seed Step 1 Choose next destination randomly Step 2

  45. 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

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

  47. Simulation Results UDel mobility model conforms to the actual measurement

  48. Simulation Results Since there is no interaction among pedestrians, flow rate is so high So, constrained random way point model is not realistic

  49. 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

  50. Future work • Investigate impact of UDel mobility model on performance of MANET routing protocols

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