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Big Data Aided Vehicular Network Feature Analysis and Mobility Models Design

机译:大数据辅助车载网络特征分析与移动模型设计

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Vehicular networks play a pivotal role in intelligent transportation system (ITS) and smart city (SC) construction, especially with the coming of 5G. Mobility models are crucial parts of vehicular network, especially for routing policy evaluation as well as traffic flow management. The big data aided vehicle mobility analysis and design attract researchers a lot with the acceleration of big data technology. Besides, complex network theory reveals the intrinsic temporal and spatial characteristics, considering the dynamic feature of vehicular network. In the following content, a big GPS dataset in Beijing, and its complex features verification are introduced. Some novel vehicle and location collaborative mobility schemes are proposed relying on the GPS dataset. We evaluate their performance in terms of complex features, such as duration distribution, interval time distribution and temporal and spatial characteristics. This paper elaborates upon mobility design and graph analysis of vehicular networks.
机译:车载网络在智能交通系统(ITS)和智慧城市(SC)建设中起着举足轻重的作用,尤其是随着5G的到来。移动性模型是车辆网络的关键部分,尤其是对于路由策略评估以及交通流管理而言。大数据辅助的车辆移动性分析和设计随着大数据技术的加速吸引了很多研究人员。此外,考虑到车辆网络的动态特性,复杂网络理论揭示了其固有的时空特征。在下面的内容中,介绍了北京的一个大型GPS数据集及其复杂的特征验证。依靠GPS数据集提出了一些新颖的车辆和位置协同移动方案。我们根据复杂特征(例如持续时间分布,间隔时间分布以及时间和空间特征)评估其性能。本文阐述了车辆网络的移动性设计和图形分析。

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