Intelligent transportation systems (ITS) have made considerable progress along with the rapid development of technologies in recent decades. Nonetheless, a comprehensive understanding of the key technologies in the ITS domain remains elusive, and current studies have yet to systematically examine correlation patterns. To address the gap, an integrated methodology combining topic modeling and co-occurrence analysis is employed. By calculating the key index integrating with the frequency, betweenness centrality, and co-occurrence intensity, the key technologies and their basic correlations are found and analyzed. Moreover, a correlation pattern identification approach is proposed, leading to the recognition of three technology cluster patterns, namely chain clusters, cycle clusters, and star clusters. According to the service packages in the Architecture Reference for Cooperative and Intelligent Transportation, the 85 ITS-related key technologies and their cluster patterns are obtained from patent data. As a result, several primary technology clusters are uncovered, such as the chain cluster for electronic toll collection, the cycle cluster containing the person, vehicle, and road elements, and the star cluster used in the traffic state measurement and warning. The proposed methodology facilitates the recognition of ITS technologies’ relevance and structure pattern and holds potential for future applications in other technological domains.
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