Big Data Aided Vehicular Network Feature Analysis and Mobility Models Design

被引:5
|
作者
Sun, Ruoxi [1 ]
Ye, Jing [2 ]
Tang, Ke [3 ]
Zhang, Kai [4 ]
Zhang, Xin [4 ]
Ren, Yong [4 ]
机构
[1] Tsinghua Univ, Dept Elect Engn, China Transport Telecommun & Informat Ctr, Beijing 100084, Peoples R China
[2] Natl Univ Singapore, Fac Engn, Singapore 119077, Singapore
[3] Beijing Jiaotong Univ, Elect & Informat Engn, Beijing 100084, Peoples R China
[4] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
来源
MOBILE NETWORKS & APPLICATIONS | 2018年 / 23卷 / 06期
关键词
Big data; Vehicular network; Complex network; Mobility models;
D O I
10.1007/s11036-017-0981-z
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:1487 / 1495
页数:9
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