An intelligent urban traffic data fusion analysis method based on improved artificial neural network

Abstract

With the advent of the era of deep Internet, the huge scientific and economic value of big data is gradually highlighted. However, there are high technical barriers in its data analysis methods. In order to explore the value space of large data, we need to abandon the traditional scheme and adopt new analysis methods. Strategies such as strengthening the overall framework of intelligent traffic standard system, the development and use of key technologies, and breaking down information barriers are of reference value for other cities to build intelligent traffic. The deep neural network algorithm integrates huge heterogeneous data with bionic learning algorithm, supports multi-source information screening, and achieves dynamic capture of time series, thus building a bridge between large data and value information. The article analyzes the current situation of traffic in multiple cities, and starts with the traffic under big data and cloud computing, and studies the application of big data and cloud computing in intelligent transportation system, in order to provide some reference for the development of smart Wuhan. With reference. The analysis results show that the infinite neural network has more powerful computational efficiency and performance advantages in data processing.