A Framework to Analyze Function Domains of Autonomous Transportation Systems Based on Text Analysis

Abstract

With the development of information and communication technologies, the current intelligent transportation systems (ITSs) will gradually become automated and connected, and can be treated as autonomous transportation systems (ATSs). Function, which unites cutting-edge technology with ATS services as a fundamental component of ATS operation, should be categorized into function domains to more clearly show how ATS operates. Existing ITS function domains are classified mostly based on the experience of experts or the needs of practitioners, using vague classification criteria. To ensure tractability, we aim to categorize ATS functions into function domains based on text analysis, minimizing the reliance on subjective experience. First, we introduce the Latent Dirichlet Allocation (LDA) topic model to extract text features of functions into distribution weights, reflecting the semantics of the text data. Second, based on the LDA model, we categorize ATS functions into twelve function domains by the k-means method. The comparison between the proposed function domains and the existing counterparts of other ITS framework demonstrates the effectiveness of the LDA-based classification method. This study provides a reference for text processing and function classification of ATS architecture. The proposed functions and function domains reveal the objectives in future transportation systems, which could guide urban planners or engineers to better design control strategies when facing new technologies.