Keumsook Lee

Statistical Analysis of the Metropolitan Seoul Subway System: Network Structure and Passenger Flows

The Metropolitan Seoul Subway system, consisting of 380 stations, provides the major transportation mode in the metropolitan Seoul area. Focusing on the network structure, we analyze statistical properties and topological consequences of the subway system. We further study the passenger flows on the system, and find that the flow weight distribution exhibits a power-law behavior. In addition, the degree distribution of the spanning tree of the flows also follows a power law.

Industrial agglomeration and transport accessibility in metropolitan Seoul

This study aims to reveal the relationship between industrial agglomeration and transport accessibility in the Seoul metropolitan area. Our study suggests that in spite of the rapid expansion of the Seoul metropolitan area, central business districts still function as centers of the industry and transportation system; the agglomeration of most industrial subsectors are occurring in central areas and only primary and manufacturing sectors’ clusters are located out of these areas; both of subway and road networks show higher level of accessibility in central Seoul and big cities. This implies a strong relationship between the industrial agglomeration and the transport accessibility, and such hypothetical relationship is tested for every industrial subsector using logit analysis. Our findings indicate that although there are industrial variations in the magnitude of impacts and the significance level, transport networks are, in general, positively associated with industrial agglomeration and this is especially true for service sectors.

Emergence of criticality in the transportation passenger flow: scaling and renormalization in the Seoul bus system.

Social systems have recently attracted much attention, with attempts to understand social behavior with the aid of statistical mechanics applied to complex systems. Collective properties of such systems emerge from couplings between components, for example, individual persons, transportation nodes such as airports or subway stations, and administrative districts. Among various collective properties, criticality is known as a characteristic property of a complex system, which helps the systems to respond flexibly to external perturbations. This work considers the criticality of the urban transportation system entailed in the massive smart card data on the Seoul transportation network. Analyzing the passenger flow on the Seoul bus system during one week, we find explicit power-law correlations in the system, that is, power-law behavior of the strength correlation function of bus stops and verify scale invariance of the strength fluctuations. Such criticality is probed by means of the scaling and renormalization analysis of the modified gravity model applied to the system. Here a group of nearby (bare) bus stops are transformed into a (renormalized) “block stop” and the scaling relations of the network density turn out to be closely related to the fractal dimensions of the system, revealing the underlying structure. Specifically, the resulting renormalized values of the gravity exponent and of the Hill coefficient give a good description of the Seoul bus system: The former measures the characteristic dimensionality of the network whereas the latter reflects the coupling between distinct transportation modes. It is thus demonstrated that such ideas of physics as scaling and renormalization can be applied successfully to social phenomena exemplified by the passenger flow.

Master equation approach to the intra-urban passenger flow and application to the Metropolitan Seoul Subway system

The master equation approach is proposed to describe the evolution of passengers in a subway system. With the transition rate constructed from simple geographical consideration, the evolution equation for the distribution of subway passengers is found to bear skew distributions including log-normal, Weibull, and power-law distributions. This approach is then applied to the Metropolitan Seoul Subway system: analysis of the trip data of all passengers in a day reveals that the data in most cases fit well to the log-normal distributions. Implications of the results are also discussed.

How complexity emerges in urban systems: Theory of urban morphology.

Human beings develop the land and transform land use patterns, constructing artificial structures. Among them, the city is a representative system and its morphology has attracted much attention. While most existing studies have been devoted to individual dynamics and focused on the proximity of specific areas of a city, we here pay attention to the city as a complex system, where interactions between individuals give rise to emergent properties. Specifically, analyzing the big data on every building in Seoul City, we specify the relevant interactions among constituents and probe the emergence of complex land use patterns. In particular, based on the empirical observations, we illustrate that interactions between land uses are frustrated, which serves as a basic postulate of the theory of urban morphology. We examine this conjecture with the help of a layered Ising-type model and disclose that the actual land use pattern emerges at the criticality of the system in the presence of heterogeneously distributed fields. It is also remarked that our model, allowing quantitative predictions, can easily be applied to other cities around the world.

Information-sharing tendency on Twitter and time evolution of tweeting

While topics on Twitter may be categorized according to their predictability and sustainability, some topics have characteristics depending on the time scale. Here we propose a good measure for the transition of sustainability, which we call the information-sharing tendency, and find that the unpredictability on Twitter is provoked by the exposure of Twitter users to external environments, e.g., mass media and other social network services. In addition, it is demonstrated that the numbers of articles and comments on on-line newspapers serve as plausible measures of exposure. From such measures of exposure, the time evolution of tweeting can be described, when the information-sharing tendency is known.

Visualization of Passenger Flows of the Metropolitan Seoul Subway System

This study proposes visualization methods of the diurnal passenger flows on the Metropolitan Seoul Subway system (MSSs) and examines the passenger trip behaviors of major central business districts (CBDs). We mine the MSS passenger flow information from a single day T-card passenger trip transaction database. It is practically intractable to analyze such flows, involving huge, complex space-time data, by means of general statistical analysis. On the other hand, dynamic visualizations of the passenger flows make it possible to analyze intuitively and to grasp effectively characteristics of the passenger flows. We thus propose several methods to visualize the passenger flow information. In particular, we visualize dynamic passenger flows of each link on the subway network and analyze the time-space characteristics of passenger ridership for the three major CBDs. As the result, we can ascertain the strong association between CBD and subway line and clarify the distinction among three major CBDs in the diurnal patterns of subway passenger flow.

Classification of Subway Trip Patterns from Smart Card Transaction Databases

To understand the trip patterns of subway passengers is very important to making plans for an efficient subway system. Accordingly, there have been studies on mining and classifying useful patterns from large smart card transaction databases of the Metropolitan Seoul subway system. In this paper, we define a new classification of subway trip patterns and devise a classification algorithm for eleven trip patterns of the subway users from smart card transaction databases which have been produced about ten million transactions daily. We have implemented the algorithm and then applied it to one-day transaction database to classify the trip patterns of subway passengers. We have focused on the analysis of significant patterns such as round-trip patterns, commuter patterns, and unexpected interesting patterns. The distribution of the number of passengers in each trip pattern is plotted by the get-on time and get-off time of subway transactions, which illustrates the characteristics of the significant patterns.