Vehicular Mobility Modeling for VANET

Härri, Jérôme

doi:10.1002/9780470740637.ch5

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…The authors of [19,20] refined the classification criteria, covering the various levels of motion (e.g., trip, path, and flow). In general, the macro-and microscopic division methods in [12] are in use.…”

Section: Existing Surveysmentioning

confidence: 99%

“…Härri et al [20] analyzed the characteristics of the models from the perspective of network and application. And the interaction between the mobility model and the network simulator was discussed in [19]. Härri et al [19,20] provided a framework for generating realistic vehicular mobility patterns, which is of significance to guide the study of the relationship between application requirements and motion module selection.…”

Section: Existing Surveysmentioning

confidence: 99%

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Vehicular mobility patterns and their applications to Internet-of-Vehicles: a comprehensive survey

Cui

et al. 2022

Sci. China Inf. Sci.

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With the growing popularity of the Internet-of-Vehicles (IoV), it is of pressing necessity to understand transportation traffic patterns and their impact on wireless network designs and operations. Vehicular mobility patterns and traffic models are the keys to assisting a wide range of analyses and simulations in these applications. This study surveys the status quo of vehicular mobility models, with a focus on recent advances in the last decade. To provide a comprehensive and systematic review, the study first puts forth a requirement-model-application framework in the IoV or general communication and transportation networks. Existing vehicular mobility models are categorized into vehicular distribution, vehicular traffic, and driving behavior models. Such categorization has a particular emphasis on the random patterns of vehicles in space, traffic flow models aligned to road maps, and individuals’ driving behaviors (e.g., lane-changing and car-following). The different categories of the models are applied to various application scenarios, including underlying network connectivity analysis, off-line network optimization, online network functionality, and real-time autonomous driving. Finally, several important research opportunities arise and deserve continuing research efforts, such as holistic designs of deep learning platforms which take the model parameters of vehicular mobility as input features, qualification of vehicular mobility models in terms of representativeness and completeness, and new hybrid models incorporating different categories of vehicular mobility models to improve the representativeness and completeness.

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Section: Existing Surveysmentioning

confidence: 99%

Section: Existing Surveysmentioning

confidence: 99%

Vehicular mobility patterns and their applications to Internet-of-Vehicles: a comprehensive survey

Cui

et al. 2022

Sci. China Inf. Sci.

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“…The vehicular networking literature offers different types of vehicular mobility models like flow, traffic, behavioral, and trace-based for analyzing the vehicular traffic patterns [27]. Among these types of models only trace-based vehicular mobility models which are built from either real vehicular traces or combining some real trace information with synthetic mechanisms, offer some parking pattern through the Origin -Destination (O/D) matrices used in the traces.…”

Section: Parking Patternsmentioning

confidence: 99%

Smart cities recharged: Improving electrical vehicles recharging by routine-aware scheduling

Kuran

Viana

Iannone

et al. 2014

2014 IEEE 10th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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International audienceIn this paper, we propose a two-layered parking lot management system for charging scheduling of electric vehicles (EVs) considering a realistic vehicular mobility pattern. EVs are categorized in two groups based on their mobility patterns: Regular EVs and Irregular EVs. We use the data from an vehicular mobility trace collected from the Canton of Zurich for the regular EVs and a probabilistic pattern built on top of this Zurich trace aiming at modeling the behavior of irregular EVs. To the extend of our knowledge, this is the first EV charging scheduling study in the literature that utilizes a big-scale realistic vehicular mobility trace. The performance of the proposed system is compared with well-known routine unaware scheduling mechanisms (e.g., First Come First Serve) with regard to maximizing the parking lot revenue. Our results show that, our proposed system outperforms well-known routine unaware scheduling mechanisms and it is evident that real environments would benefit from using such parking lot management systems in Smart Cities

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