Urban traffic abnormality identification method based on complex network theory

Abstract

The invention provides an urban traffic abnormality identification method based on a complex network theory. The method comprises the following steps: step 1, constructing an urban traffic network based on traffic data; step 2, carrying out feature extraction and screening based on a complex network theory; step 3, carrying out the abnormality recognition and prediction of a traffic system; and step 4, evaluating and verifying a model. According to the invention, the scientific and reliable technical support and theoretical support can be provided for the recognition and prediction of the urban traffic congestion abnormality based on the complex network theory and a machine learning method. Therefore, the congestion abnormality of the urban traffic system can be efficiently and accuratelyidentified and predicted, and the method has important significance in ensuring the healthy and stable operation of the urban traffic system and improving the reliability of the urban traffic system;and the method is scientific and good in manufacturability and has the great application and popularization value.

Description

technical field [0001] The present invention proposes a method for identifying urban traffic anomalies based on complex network theory, constructs an urban traffic network based on empirical urban traffic data and road network structure information, uses complex network theory for feature extraction and screening, and combines machine learning technology to identify traffic anomalies And prediction, which belongs to the interdisciplinary field of machine learning and network science. Background technique [0002] In recent years, with the rapid development of urbanization, transportation has become one of the main infrastructures for the development of modern society, and it also plays a pivotal role in the current development model of "Internet + transportation". However, when urban traffic develops into a huge and complex traffic network system with the growth of the city, the problem of traffic congestion becomes more and more prominent. In fact, traffic congestion has c...

AI Technical Summary

Problems solved by technology

In addition, traditional traffic anomaly identification methods are mostly based on changes in traffic flow, and different algorithms are designed to identify abnormalities in traffic flow parameters without considering the structural characteristics of urban traffic networks, resulting in low traffic anomaly identification and prediction efficiency

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