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Verein Konstantin 313945016 Melnik Svetlana 321372153
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Author: Dr. Jérôme Härri Karlsruhe Institute of Technology, Karlsruhe, Germany 2
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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
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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
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5 Mobility Mobility at that time was marginal considering the impressive size of the computer. Mobility had the same objectives as the traffic theory.
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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
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Benefits of vehicular communication Exchange messages between cars to alter traffic for safety purposes or traffic efficiency in order to avoid traffic jams. 7
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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
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Multilayer description of vehicular mobility patterns. Bi-directional interaction between traffic and network simulators 9 Aspects of vehicular mobility models
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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
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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
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Flow model: Defined at more detailed level by modeling interactions between vehicles. 12 Multilayer description of vehicular mobility patterns
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Isolated Embedded Federated 13 Bi-directional interaction between traffic and network simulators
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Isolated No specific interaction is defined or possible between the network and a traffic simulators 14 Network and traffic simulator may be:
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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:
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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:
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17 Scenario and performance
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18 Example
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Flow models Traffic models Behavioral models Trace-based models Random models 19 Categories of vehicular motion modeling:
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Mobility models based on traffic flow theory which has considered the road topology, the vehicular speed rules and the routing selection. 20 Flow models
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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
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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
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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
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Vehicular mobility is considered random and the mobility parameters that are sampled from random processes. Parameters such as: Speed Heading Destination 24 Random models
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Random Waypoint Model (RWM) Random Walk Model (RWalk) Reference Point Group Mobility Model (RPGM) 25 Random models
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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)
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Randomly generates a moving azimuth θ and the journey time t. 27 Random Walk Model (RWalk)
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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)
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Defines movements according to the following rules: 29 Reference Point Group Mobility Model (RPGM)
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30 A simplified architecture
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One picture is worth a thousand words 31
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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
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Freeway model restricts the movement on several bi-directional multi-lane freeways 33 Freeway model
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Manhattan model restricts vehicular movements to urban grids. 34 Manhattan model
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35 Notation description
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In both models, the movement of an individual vehicle is modeled according to the following set of rules: 36 Freeway and Manhattan model
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37 Manhattan model Vehicles use a stochastic turn function that randomly chooses next movement at each intersection Model example on video
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38 Model generation example
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39 Generating realistic map
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Vehicular motion models Example: Random Model Our opinion - 40 Summary
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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
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Thank you for attention 42
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