implementation and evaluation of mobility models with opnet04… · implementation and evaluation...

Implementation and Evaluation of Mobility Models with OPNET 1

Lehrstuhl Netzarchitekturen und NetzdiensteInstitut für InformatikTechnische Universität München

Implementation and Evaluation ofMobility Models with OPNET

Abschlussvortrag zur Masterarbeit

von

Thomas Oberwallner

09.04.2013

Betreuer: Alexander von Bodisco


Outline

I. Motivation

II. Modules Import of OpenStreetMap (OSM) and routing Mobility model Statistics

III. Evaluation Introduction of scenarios Simulation results


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


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

Modules: Import of OSM and Routing



Routing with Dijkstra algorithm

Weight of edges Distance for shortest path Time for quickest path

Pending nodes stored in a Min-Heap



OSM file consists of: Nodes: Points in map with a defined position Ways: Contain references to points, form roads


Routing with A* algorithm Weight of edges

• Distance for shortest path• Time for fastest path

Pending nodes stored in a Min-Heap



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


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


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


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


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 betweenneighbours

Network metrics Number of neighbours

Neighbour distance

Number of network partitions


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


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

Adaptive routes produce the fastest routes followed by Lévy-Flight and Geo-Routed-Waypoint routes

Number of cars

Lower Manhattan

Soest Regensburg

500 / 441 / 807 60.50 km/h 48.87 km/h

48.87 km/h

1000 / 883 / 1614

56.68 km/h 23.86 km/h

31.02 km/h

1500 / 1324 / 2421

52.13 km/h 15.88 km/h

21.17 km/h


Evaluation – Speed on sections in Soest


Video

Click to edit Master text styles Second level

• Third level– Fourth level

» Fifth level


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 driver-models

Car following, overtaking or traffic lights

Car following, multilane roads, traffic lights

Trip generation Random, adaptive,distance-based

Random, activity-based, 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 informationNot aggregated

Import/Export Traces Yes/YesGPX-Format

No/Yes (NS2, GloMoSim, QualNet, NET)

No/Yes(unknown format)

Network Simulation Yes (no hybrid simulation)

No No


Questions

Thank you for your time and attention.

Questions?


Classification of synthetic mobility models

Click to edit Master text styles Second level

• Third level– Fourth level

» Fifth level


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


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


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


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


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

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