Role and challenge of technology toward a smart sustainable city: Topic modeling, classification, and time series analysis using information and communication technology patent data

Abstract

This research defines and classifies essential technology groups that comprise a smart sustainable city (SSC) and explores patterns of technological growth by analyzing patents. First, topic modeling and network analysis are conducted using patent text data and international patent classification code, respectively. Patents are classified and defined based on the technology theme and the type of technology provider. Second, hardware-oriented and service application-oriented technologies are classified and predicted using machine learning. Third, the similarities in growth patterns of different technology groups are explored using dynamic time warping (DTW). Moreover, lagging or preceding relationships are examined using multivariate time series analysis. Network infrastructure and Internet-of-things (IoT) application technologies have short-term lagging and preceding relationships along with long-term equilibrium relationships. In terms of player groups, long-term relationships between device vendors, chipset makers, network operator and IoT service provider, and network equipment vendors are confirmed. Technologies for realizing a sustainable city must be prioritized, and the strategic direction of technology must be established to realize truly smart sustainable cities. The supplier of technologies along with possible collaboration opportunities must also be selected. This study proposes the role and direction of technology for SSC and presents a comprehensive methodology for analyzing technology using patents.