Big Data Analysis Technology for Electric Vehicle Networks in Smart Cities
Big Data Analysis Technology for Electric Vehicle Networks in Smart Cities
复制标题
智慧城市电动汽车网络大数据分析技术
DOI：
10.1109/tits.2020.3008884
复制
发表时间：
2021-03-01
影响因子：
8.5
通讯作者：
Wang, Qingjun
中科院分区：
文献类型：
--
作者：
Lv, Zhihan;Qiao, Liang;Wang, Qingjun
为了通过大数据分析技术探索智慧城市中的电动汽车网络，本研究利用大数据分析技术中的K-means和模糊理论，构建了基于目标函数的模糊均值聚类算法理论（FCM）。然后对FCM算法进行了改进，并对电动汽车网络进行了仿真。结果表明，在网络数据传输性能分析中，当传播成功概率为100%，且$\lambda $值在0.01-0.05之间时，最接近实际结果，数据延迟最小。在路径引导效果分析中，当面对拥堵路段时，本研究的路径引导策略可以有效地抑制拥堵的蔓延，实现交通拥堵的及时疏散。在进一步分析不同因素对交通状况的影响时，在路线引导下，随着设备市场渗透率（MPR）、车辆跟随率（FR）、拥堵程度（CL）的提高，诱导策略的改进更加清晰，经济效益也更大。本研究发现，利用大数据分析技术对电动汽车交通网络进行改进，可以显著降低网络数据传输性能延迟，改变路径，有效抑制拥堵蔓延，为电动汽车交通网络的发展提供了实验参考。
To explore the electric vehicle networks in smart cities through big data analysis technology, this study utilizes K-means and fuzzy theory in big data analysis technology to construct an objective function-based fuzzy mean clustering algorithm theory (FCM). Then, the FCM algorithm is improved, and the electric vehicle network is simulated. The results show that in the analysis of network data transmission performance, when the probability of successful propagation is 100% and the $\lambda $ value is between 0.01-0.05, it is closest to the actual result, and the data delay is the smallest. In the analysis of the route guidance effects, when facing congested road sections, the route guidance strategy of this study can restrain the spread of congestion effectively and achieve timely evacuation of traffic congestion. In the further analysis of the impact of different factors on traffic conditions, under route guidance, with the increase in market penetration rate (MPR) of devices, following rate (FR) of vehicles, and congestion level (CL), the improvement of the induction strategy becomes clearer, and greater economic benefits are achieved. This study has found that utilizing big data analysis technology to improve the electric vehicle transportation networks can reduce the network data transmission performance delay significantly and change the path to suppress the spread of congestion effectively, which has provided experimental references for the development of electric vehicle transportation networks.