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A city is a complex system that never sleeps; it constantly changes, and its internal mobility (people, vehicles, goods, information, etc.) continues to accelerate and intensify. These changes and mobility vary in terms of the attributes of the city, such as space, time and cultural affiliation, which characterise to some extent how the city functions. Traditional urban studies have successfully modelled the 'low-frequency city' and have provided solutions such as urban planning and highway design for long-term urban development. Nevertheless, the existing urban studies and theories are insufficient to model the dynamics of a city's intense mobility and rapid changes, so they cannot tackle short-term urban problems such as traffic congestion, real-time transport scheduling and resource management. The advent of information and communication technology and big data presents opportunities to model cities with unprecedented resolution. Since 2018, a paradigm shift from modelling the 'low-frequency city' to the so-called 'high-frequency city' has been introduced, but hardly any research investigated methods to estimate a city's frequency. This work aims to propose a framework for the identification and analysis of indicators to model and better understand the concept of a high-frequency city in a systematic manner. The methodology for this work was based on a content analysis-based review, taking into account specific criteria to ensure the selection of indicator sets that are consistent with the concept of the frequency of cities. Twenty-two indicators in five groups were selected as indicators for a high-frequency city, and a framework was proposed to assess frequency at both the intra-city and inter-city levels. This work would serve as a pilot study to further illuminate the ways that urban policy and operations can be adjusted to improve the quality of city life in the context of a smart city. [ABSTRACT FROM AUTHOR]