Optimization of Navigation Method for GPS Mass Transit Network Data Based on Support Vector Machine

Abstract

Mobile robot autonomous navigation is a hot topic in the field of robotics or unmanned vehicles. Support vector machine (SVM) is a new algorithm based on statistical learning theory, which has gradually become a new research hotspot in the field of data mining after artificial neural network. In this article, we focus on the SVM to optimize the data navigation method of global positioning system (GPS) mass transit network. First, the vehicle navigation system and road network data model are introduced theoretically. Then, the navigation scheme of GPS mass transit network data is designed and the navigation model is established. Finally, this article uses the knowledge of support vector machine to optimize the data navigation method of traffic network and analyze the data. The experimental results show that the proposed method is based on the SVM, which has a good experimental effect.