Road traffic is an important component of the national economy and social life. Promoting intelligent and Informa ionization construction in the field of road traffic is conducive to the construction of smart cities and the formulation of macro strategies and construction plans for urban traffic development. Aiming at the shortcomings of the current road traffic system, this article, on the basis of combining convolution neural network, situational awareness technology, database and other technologies, takes the road traffic situational awareness system as the research object, and analyzes the information collection, processing, and analysis process of road traffic situational awareness system. Convolutional neural networks (CNN), region-CNN (R-CNN), fast R-CNN, and faster R-CNN are used for vehicle class classification and location identification in road image big data. The deep convolutional neural network model based on road traffic image big data was further established, and the system requirements analysis and system framework design and implementation were carried out. Through the analysis and trial of actual cases, the results show the application effect of the realized road traffic situational awareness system, which provides a scientific reference and basis for the establishment of modern intelligent transportation system.

中文翻译：

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基于图像大数据的道路交通态势感知系统研究

道路交通是国民经济和社会生活的重要组成部分。推进道路交通领域智能化、信息化建设，有利于智慧城市建设，有利于制定城市交通发展宏观战略和建设规划。本文针对当前道路交通系统存在的不足，在结合卷积神经网络、态势感知技术、数据库等技术的基础上，以道路交通态势感知系统为研究对象，对信息采集进行分析，道路交通态势感知系统的处理与分析过程。卷积神经网络（CNN）、区域-CNN（R-CNN）、快速R-CNN、并且faster R-CNN用于道路图像大数据中的车辆类别分类和位置识别。进一步建立了基于道路交通图像大数据的深度卷积神经网络模型，进行了系统需求分析和系统框架设计与实现。通过对实际案例的分析和试验，结果展示了实现的道路交通态势感知系统的应用效果，为建立现代智能交通系统提供了科学参考和依据。