Intergeneration Division Based on Key Component Analysis in an Autonomous Transportation System Using the Natural Language Processing Method

Abstract

Advancement of emerging technologies and increasing of transport demands accelerate the evolution of the autonomous transportation system (ATS). Framework and architecture of ATS are becoming a research hotspot; however, by far, few studies on transportation intergeneration division are not basically involved. Previous works indicate that key components are critical representation in the distinguishing of long-term era. Besides, massive text material accumulates as the research work goes on, and natural language processing technique keeps developing, which makes quantitative research on key components in intergeneration division become possible. In this work, a method based on the massive text analysis is proposed. First, the LDA2vec is used to get the relationship between components and other elements. Then, a word set is from the component word set extraction module based on component items. Finally, the component word set is clustered to get ATS generation and to generate key components. Based on an analysis of large-scale important traffic texts, our method divides the traffic system into three generations for Chinese traffic from 2010 to 2022. The key components of our method given are consistent with human cognition of ATS. Successful application indicates that this work can be extended to other intergeneration division fields.