Implementation and Evaluation of Mobility Models with OPNET

Transcription

1 Lehrstuhl Netzarchitekturen und Netzdienste Institut für Informatik Technische Universität München Implementation and Evaluation of Mobility Models with OPNET Abschlussvortrag zur Masterarbeit von Thomas Oberwallner Betreuer: Alexander von Bodisco 1

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3 Outline I. Motivation II. Modules Import of OpenStreetMap (OSM) and routing Mobility model Statistics III. Evaluation Introduction of scenarios Simulation results 3

4 Motivation Existing mobility framework in OPNET Mobility models: Random Waypoint/Direction/Walk, Group Mobility Statistics: Spatial node distribution, node speed distribution, link duration, transient phase Existing VANET simulators Complex movement models (SUMO/VanetMobiSim) Complex configuration (SUMO) Import of OSM files incomplete (VanetMobiSim) Few statistics about movement (SUMO/VanetMobiSim) Goal Simulation of traffic on real maps Fast movement model of VANETs with focus on the main aspects of vehicular movement Simulate accurately enough to evaluate routing protocols Import/Export Traces 4

5 Modules: Import of OSM and Routing OSM file consists of: Nodes: Points in map with a defined position Ways: Contain references to points, form roads Creation of routing graph by minimizing road graph Vertex in routing graph: intersection Edge: Connects two intersections 5

6 Modules: Import of OSM and Routing Creation of routing graph by minimizing road graph Vertex in routing graph: intersection Edge: Connects two intersections Routing with Dijkstra algorithm Weight of edges Distance for shortest path Time for quickest path Pending nodes stored in a Min-Heap 6

7 Modules: Import of OSM and Routing OSM file consists of: Nodes: Points in map with a defined position Ways: Contain references to points, form roads Creation of routing graph by minimizing road graph Vertex in routing graph: intersection Edge: Connects two intersections Routing with A* algorithm Weight of edges Distance for shortest path Time for fastest path Pending nodes stored in a Min-Heap 7

8 Modules: Mobility model Trip generation Routed-Geo-Waypoint Similar to Random Waypoint Random destination Fastest route to destination based on speed limits Adaptive Destination like in Routed-Geo-Waypoint Fastest route to destination according to average speed of all cars which drove on sections Lévy-Flight Distance to destination exponentially distributed Drive shortest path to destination 8

9 Modules: Mobility model Speed Every car has its desired speed Speed limits: Drivers obey speed limits depending on desired speed Behaviour: If no car in front: speed = (desired speed / 100) * speed limit If slower car in front: If distance > safety-distance: speed = (desired speed / 100) * speed limit If distance = safety-distance: speed = speed of car in front If distance < safety-distance: speed = 0.95 * speed of car in front 9

10 Modules: Mobility model Traffic signals Position of traffic signal (TS) extracted from map Timing: Every TS has a uniform distributed offset [0; 20]s Every TS has a uniform distributed duration [5; 15]s No yellow-phase Two roads with an angle closest to 180 have a green signal at the same time 10

11 Modules: Mobility model Intersections First in, first out principle Departure and destination of all cars which crossed the intersection in the previous 2 seconds is stored Arriving car comes from direction A and drives in direction B Waiting decision depends on the direction of previous cars: If no car crossed the intersection: ok If cars came from A: ok If cars went from B to A: ok Else: Wait 2 seconds 11

12 Modules: Statistics Map metrics Size of map Type of roads Number of nodes/intersections Distance between nodes/intersections Speed limits Number of traffic signals Mobility metrics Car speed Distance of routes Travel time of routes Number of cars per section Relative speed of neighbours Speed ratio between neighbours Spatial dependence between neighbours Network metrics Number of neighbours Neighbour distance Number of network partitions 12

13 Evaluation: Introduction of scenarios Characteristic scenarios Lower Manhattan: Parallel roads from north to south and east to west, traffic signals on most intersections Soest: Radial, concentric ring roads Regensburg: Normal city 13

14 Evaluation: Simulation results basic features Number of cars set according to length of road network The higher the number of cars the lower the average speed the higher the time until the destination is reached the lower the driven distance the lower the number of crossed intersections the lower the relative speed the higher the number of neighbors the lower the number of network partitions Number of Lower cars Manhattan 500 / 441 / km/h Regensbu rg km/h km/h Adaptive routes thekm/h fastest routes23.86 followed by Lévy-Flight and 1000 / 883 / produce Geo-Routed-Waypoint routes 1614 km/h km/h Soest 14

15 Evaluation Speed on sections in Soest 15

16 Video Click to edit Master text styles Second level Third level Fourth level» Fifth level 16

17 Comparison of VANET simulators Master-Thesis VanetMobiSim SUMO Import OSM-Files Yes Yes (since current version), but incomplete Yes (NETCONVERT) Mobility model Car following, traffic lights, overtaking, individual drivermodels Car following, overtaking or traffic lights Car following, multilane roads, traffic lights Trip generation Random, adaptive, distance-based Random, activitybased, sightseeing Flow definitions, OD matrices, random, population statistics Routing A*, fast Dijkstra, slow A*, fast Statistics Map metrics, mobility metrics, network metrics, simulation performance metrics Node density Position dump, edgelane traffic, trip/route information Not aggregated Import/Export Traces Yes/Yes GPX-Format No/Yes (NS2, GloMoSim, QualNet, NET) No/Yes (unknown format) Network Simulation Yes (no hybrid simulation) No No 17

18 Questions Thank you for your time and attention. Questions? 18

19 Classification of synthetic mobility models Click to edit Master text styles Second level Third level Fourth level» Fifth level 19

20 Modules: Import of OpenStreetMap road maps File-Format: OSM XML-File contains 3 element types Node Point in map Contains latitude, longitude, id, (version, timestamp, userid, changeset) Way Contains references to nodes + additional data like highway type, speed limit, name, surface, one way Relation Forms restrictions or areas Contains references to nodes and ways Important: Guessing of unknown data 20

21 Modules: Import/Export of GPS-Traces File Format: GPX Wpt: Waypoint Attributes: Latitude, longitude Elements (optional): Elevation, timestamp Rte: Route Elements: Name, description, list of route points (wpt) Trk: Track Elements: Name, description, list of track segments Trkseg: Track segment Elements: List of waypoints 21

22 Modules: Mobility Model Overtaking Overtaking decision depends on: Overtaking enabled by configuration? Driving out of town? Car in front driving its desired speed? Desired speed >> Desired speed of car in front? Car in front not overtaking at the moment? No opposing traffic? Overtake will be completed before arriving at the next intersection? Following car overtakes by keeping to drive its desired speed When position of overtaking car has reached its predecessor: Positions in lists are switched 22

23 Evaluation: Simulation results advanced features Pause: Average speed of currently moving cars increases Time from start to destination decreases Driver changes: Variance of driven distance and number of crossed intersections decreases Individual driver models (Sunday driver vs. Speeder): Low amount of cars: Speeder faster than Sunday drivers High amount of cars: Almost no difference Overtaking: No differences, because urban scenario has been simulated Low amount of overtaking maneuvers 23

24 Evaluation: Performance of simulation Time of import of map is <10 seconds in evaluated maps Simulation duration depends quadratically on number of cars Disabling periodical statistics speeds up simulation by a factory of 3.5 to 16 Simulations (without statistics) on average 38.5% faster than VanetMobiSim Memory usage depends quadratically on number of cars Enabling statistics consumes about 12 MB memory 24