Search

Most existing studies on public transit network (PTN) rely on either small-scale passenger flow data or small PTN, and only traditional network parameters are used to calculate the correlation coefficient. In this work, the real smart card data (SCD) (when passenger tap in and tap out a station) of over eight million users is used as a proxy of passenger flow to dynamically explore and evaluate the structure of large-scale PTNs with tens of thousands of stations in Beijing, China. Three types of large-scale PTNs are generated, and the overall network structure of PTNs are examined and found to follow heavy-tailed distributions (mostly power law). Further, three traditional centrality measures (i.e. degree, betweenness and closeness) are adopted and modified to dynamically explore the relationship between PTNs and passenger flow. Our findings show that, the modified centrality measures outperform the traditional centrality measures in estimating passenger flow. [ABSTRACT FROM AUTHOR]

Developing data‐driven approaches to understanding urban structures is important for urban planning. However, it is still challenging to combine different transport datasets into a unified framework and reveal the dynamics of urban structures with the emergence of shared mobility. In this study, we propose two empirical multilayer networks to infer and profile urban structures. First, a temporal network is constructed using traditional taxi data over years to reveal the urban structures. Second, a multimodal network is constructed using shared mobility and traditional taxi data over a year to reveal the urban structures. The proposed networks are tested in New York City using a large volume of shared bike, shared vehicle, and traditional taxi data. The multilayer network centralities and community detection enable us to profile the characteristics of the urban flows and urban structure. The analytical results allow us to acquire a better understanding of urban structures from a multilayer perspective, and also provide a geocomputation framework that is useful for urban and geographic researchers. [ABSTRACT FROM AUTHOR]

Urban agglomeration is an important strategy used to promote economic development and urbanization in China. Understanding the structure of urban agglomeration is therefore essential for policy‐makers and planners. In this study, the Beijing–Tianjin–Hebei urban agglomeration (BTHUG) is explored through a proposed spatial network analytical framework and a large mobile phone data set (over 20 million users). We first construct a weight‐directed spatial interaction network based on an origin–destination matrix derived from the data set. Several network metrics (i.e., degree, strength, the rich‐club coefficient, and the assortativity coefficient) and three selected community detection algorithms (i.e., Infomap, Louvain, and Regionalization) are applied and compared to reveal the structure of the BTHUG. A four‐level hierarchical structure is defined and observed: one global center, two local centers, major cities that have low mobility flow but strong linkages with the three centers, and peripheral cities that have low mobility flow and weak linkages with the three centers. In particular, the results imply that the spatial structure of the BTHUG is over‐dependent on the global center (i.e., Beijing and northern Langfang). Further, ignoring spatial interaction patterns in top‐down administrative planning for urban agglomeration may lead to ineffective integrated development. The implications for BTHUG planning are discussed. [ABSTRACT FROM AUTHOR]

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]