Verein Konstantin Melnik Svetlana

Author: Dr. Jérôme Härri  Karlsruhe Institute of Technology, Karlsruhe, Germany 2

 Main objective was to understand the link between the traffic speed, flow and density for an efficient dimensioning of the transport infrastructures and to help resolve traffic problems. 3 Traffic theory

 At the same time appeared computer, and a new research domain called computer networking that later gave birth to Internet.  Connecting computers with each other in communication network brought to revolution in information management 4 Computer networking

5 Mobility  Mobility at that time was marginal considering the impressive size of the computer.  Mobility had the same objectives as the traffic theory.

Fusion of two studies  Vehicles became part of communication network.  Appearance of VANET (Communications: V2V, V2I).  Two research domains regrouped and motivated the study of vehicular mobility for networking research. 6

Benefits of vehicular communication  Exchange messages between cars to alter traffic for safety purposes or traffic efficiency in order to avoid traffic jams. 7

Vehicular mobility models  Definition: Simulation of real behavior of vehicular traffic to produce realistic mobility patterns.  Reason: Best choice for validation of networking protocols for vehicular applications.  Meaning: Vehicular mobility significantly impacts the networking shape of VANET. 8

 Multilayer description of vehicular mobility patterns.  Bi-directional interaction between traffic and network simulators 9 Aspects of vehicular mobility models

 Trip modeling: Mobility is defined by macroscopic motions between Points-of-Interests (PoI) according to an origin-destination (OD) matrix. 10 Multilayer description of vehicular mobility patterns

 Path modeling: Mobility is modeled by defining end-to-end paths. Path may be optimized based on driver’s preferences. Origin and destination points may be random or based on Trip model. 11 Multilayer description of vehicular mobility patterns

 Flow model: Defined at more detailed level by modeling interactions between vehicles. 12 Multilayer description of vehicular mobility patterns

 Isolated  Embedded  Federated 13 Bi-directional interaction between traffic and network simulators

 Isolated  No specific interaction is defined or possible between the network and a traffic simulators 14 Network and traffic simulator may be:

 Embedded  A vehicular traffic simulator is embedded into a network simulator and vice versa, allowing a bidirectional interaction between both simulators. 15 Network and traffic simulator may be:

 Federated  Vehicular traffic simulator is federated with a network through a communicating interface.  Other simulators such as VANET application simulator may also be added. 16 Network and traffic simulator may be:

17 Scenario and performance

18 Example

 Flow models  Traffic models  Behavioral models  Trace-based models  Random models 19 Categories of vehicular motion modeling:

 Mobility models based on traffic flow theory which has considered the road topology, the vehicular speed rules and the routing selection. 20 Flow models

 Trip and path models, where either each car has an individual trip or a path, or a flow of cars is assigned to trips or paths. 21 Traffic models

Behavioral models  Not based on predefined rules.  Dynamically adapt to a particular situation by mimicking human behaviors, such as: social aspects, dynamic learning  AI (Artificial intelligence) concepts. 22

 Mobility traces may also be used in order to extract motion patterns and either create or calibrate mobility models.  Another source of mobility information also comes from surveys of human behaviors. 23 Trace-based models

 Vehicular mobility is considered random and the mobility parameters that are sampled from random processes.  Parameters such as: Speed Heading Destination 24 Random models

 Random Waypoint Model (RWM)  Random Walk Model (RWalk)  Reference Point Group Mobility Model (RPGM) 25 Random models

 The most popular random model.  Each vehicle randomly samples a destination d and a speed v that will be chosen to move toward d.  Vehicles maintain a fixed velocity between waypoints. 26 Random Waypoint Model (RWM)

 Randomly generates a moving azimuth θ and the journey time t. 27 Random Walk Model (RWalk)

 Nodes are separated into groups, where a group leader determines the group’s general motion pattern. V leader (t) θ leader (t) 28 Reference Point Group Mobility Model (RPGM)

 Defines movements according to the following rules: 29 Reference Point Group Mobility Model (RPGM)

30 A simplified architecture

One picture is worth a thousand words 31

 Vehicles randomly sample destinations on graph vertices and are restricted to moving at a specific velocity on the graph edges.  Freeway model  Manhattan model 32 Improving the realism of models

 Freeway model restricts the movement on several bi-directional multi-lane freeways 33 Freeway model

 Manhattan model restricts vehicular movements to urban grids. 34 Manhattan model

35 Notation description

 In both models, the movement of an individual vehicle is modeled according to the following set of rules: 36 Freeway and Manhattan model

37 Manhattan model  Vehicles use a stochastic turn function that randomly chooses next movement at each intersection  Model example on video

38 Model generation example

39 Generating realistic map

 Vehicular motion models  Example: Random Model  Our opinion - 40 Summary

 Jérôme Härri, “Vehicular Mobility Modeling for VANET”  Vaishali D. Khairnar, Dr. S.N.Pradhan, “Mobility Models for Vehicular Ad-hoc Network Simulation”  Valentin Cristea, Victor Gradinescu, Cristian Gorgorin, Raluca Diaconescu, Liviu Iftode, "Simulation of VANET Applications“  Jérôme Härri, Fethi Filali and Christian Bonnet, “A Framework for Mobility Models Generation and its application to Inter-Vehicular Networks” 41 References

 Thank  you  for  attention 42