This article proposes a new approach to achieve fast and reliable transfer of data and uses machine learning techniques for data processing to improve the performance and data transmission security of the vehicular network. The proposed approach is the combination of 5G cellular network and alternative data transmission channels. The data collection experiment took place within different areas of the city of Berlin over a 3-month time period and involved the use of 5G technologies. The study carried out the analysis and classification of big data with the help of position-based routing protocols and the Support Vector Machine algorithms. The said techniques were employed to detect non-line-of-sight (NLoS) conditions in real time, which ensure the secure transmission of data without the loss or degradation of network performance. The novelty of the work is that it tackles various traffic scenarios (the extent of road congestion can affect the quality of big data transmission) and offers a way to improve big data transfer using the Support Vector Machine technology. The study results show that the proposed approach is effective enough with big data and can be employed to improve the performance of urban VANET networks and data transmission security. The study results can be useful in developing high-performance 5G-VANET applications to improve traffic safety in urban vehicular environments.

本文提出了一种实现数据快速可靠传输的新方法，并使用机器学习技术进行数据处理，以提高车载网络的性能和数据传输安全性。建议的方法是结合 5G 蜂窝网络和替代数据传输通道。数据收集实验在柏林市的不同地区进行了 3 个月的时间，涉及 5G 技术的使用。该研究借助基于位置的路由协议和支持向量机算法对大数据进行分析和分类。上述技术用于实时检测非视距（NLoS）条件，确保数据的安全传输，而不会丢失或降低网络性能。这项工作的新颖之处在于它解决了各种交通场景（道路拥堵的程度会影响大数据传输的质量），并提供了一种使用支持​​向量机技术改进大数据传输的方法。研究结果表明，所提出的方法对于大数据足够有效，可用于提高城市VANET网络的性能和数据传输的安全性。研究结果可用于开发高性能 5G-VANET 应用程序，以提高城市车辆环境中的交通安全。研究结果表明，所提出的方法对于大数据足够有效，可用于提高城市VANET网络的性能和数据传输的安全性。研究结果可用于开发高性能 5G-VANET 应用程序，以提高城市车辆环境中的交通安全。研究结果表明，所提出的方法对于大数据足够有效，可用于提高城市VANET网络的性能和数据传输的安全性。研究结果可用于开发高性能 5G-VANET 应用程序，以提高城市车辆环境中的交通安全。