Urban expressway parallel pattern recognition based on intelligent IOT data processing for smart city

Abstract With the sustained and rapid development of the social economy and the rapid growth of urban vehicles, urban expressway has developed rapidly. As the roadmap of urban traffic, the urban expressway has a relatively high and stable driving speed, and also bears a large amount of urban traffic. However, in recent years, with the expansion of the city scale, the congestion of urban expressways has become increasingly severe. In various areas of the merged area, due to various factors such as mismatched traffic capacity and reduced driving speed, it is easy to cause deterioration of road conditions in a short period of time and cause more secondary accidents. In order to reduce the incidence of expressway traffic accidents and to avoid as much as possible the casualties and property losses caused by accidents, Intelligent Transportation Systems (ITS) supported by information technology, data communication transmission technology, control technology and traffic engineering were introduced. With the rise of artificial intelligence and the continuous development of ITS, the video acquisition methods of real-time traffic flow data and the image recognition ability of video sequences continue to improve, providing theoretical basis and technical support for the research of urban expressway parallel pattern recognition. Aiming at the shortcomings of traditional pattern recognition methods, such as weak anti-interference to complex traffic environment and low correct recognition rate, this paper studies the pattern recognition method based on image processing, and selects the fuzzy C-means clustering in the currently used clustering methods (FCM) algorithm Because the FCM algorithm cannot obtain the global optimal solution and need to determine the number of cluster categories in advance, this paper uses ReliefF algorithm and Particle Swarm Optimization (PSO) to compare the feature weight and number of clusters of traditional FCM algorithm. Make improvements. Through the experimental analysis of the acquired video images, the results show that the improved FCM algorithm based on the proposed method has better real-time and accuracy in the application of urban expressway parallel pattern recognition.