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a: Shortest path length distribution; solid lines shows a fit to the function. b: Eccentricity distribution.
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Citations
... Smart city is highly reliant on the performance of a public transportation system [1]. Its impact to improve one's quality of life is as immense as the challenges it faces in different components [2] given a complex and intermodal public transport route network [3] [4]. In an assessment study on Philippines' transport sector [6], there is small improvement in the quality of transport services that can be attributed to inefficient transport planning and traffic management. ...
... Complex network analysis has been widely applied to the analysis of topology and traffic dynamic of transport systems like subways [4], railways [5], public transportation [6], or air transportation networks. ...
Applied complex network theory has become an interesting research field in the last years. Many papers have appeared on this subject dealing with the topological description of real transport systems, from small networks like the Italian airport network to the worldwide air transportation network. A comprehensive topological description of those critical structures is relevant in order to understand their dynamics, capacities, and vulnerabilities. In this work, for the first time, we describe the Spanish airport network (SAN) as a complex network. Nodes are airports, and links are flight connections weighed by traffic flow. We study its topological features and traffic dynamics. Our analysis shows that SAN has complex dynamics similar to small-size air transportation networks of other developed economies. It shares properties of small-world and scale-free networks, and it is highly connected and efficient and has a disassortative pattern for high-degree nodes.
... The study of public transportation systems from a network theory perspective started rather recently [1,2]. Most works on this topic are aimed at analyzing the topological structure of public transportation networks (or PTNs) of different cities (e.g. in Poland [2], Hungary [3], China [4]) with regard to various modes of transportation like bus [2] or subway [5]. Usually in these cases the underlying network is defined with bus stops or subway stations as nodes and some rule to assign links between these stops and stations. ...
... These models have been used in virtually all the papers dealing with PTNs and were applied to analyze various cities in Poland [2], Hungary [3], 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 Springer Nature 2021 L A T E X template China [4], among others. Such analysis is especially easy to conduct since the data needed to build a basic PTN is nowadays available publicly for most big cities around the world (see Figure 2). ...
In this paper, we propose a framework for solving the novel problem of role discovery in a public transportation network (PTN). We model a PTN as a weighted node-attributed network whose nodes are public transport stations (stops) grouped with respect to their geospatial position, node attributes store information about social infrastructure around the stations (stops), and weighted links integrate information about the travelling distance and the number of hops in the transportation routs between the stations (stops). Our framework discovers meaningful node roles in terms of both topological and infrastructural features of a PTN and is capable of extracting useful insights about the overall PTN’s efficiency. We apply the framework to the newly collected open data of St Petersburg, Russia, and point out some transportation and infrastructural weaknesses that should be taken into consideration by the city administration to improve the PTN in the future.
... Therefore, the characteristics of the networks can be established. Besides, the deduction of properties and meanings of the networks can also be enabled by identifying the measured outcome in quantified figures [29,30]. ...
People have their favorite type of sport, but such preferences tend to be shared for nearly a lifetime. How this preference persists remains inconclusive; hence, this study attempts to determine why people have different viewpoints on sports. It is reasonable to infer that these differences arise from differences in culture, occupation, and race. Therefore, we collected the following data and conducted research in Korea, the United States, and Japan, countries with various differences. The types of sports that people play were collected through surveys and comparisons among sports networks. Namely, “Sport Classification,” “The K-12 Physical Education System (textbooks),” “Survey (actual physical activity),” “Simple Notification Service (SNS) Activity” have been examined to deduce the reason why any particular sport is played. Firstly, Korea, the United States, and Japan conduct different physical education courses. Hence, the results affect people’s preferences. Secondly, what people post on SNS and their actual physical activities are different. Thirdly, the degree of connection between sports-type varied as well. Lastly, sports that serve the purpose of being regarded as hubs among sports-type were common in Korea, the United States, and Japan.
... (Birr et al., 2014) extends the topic to predictive models of vehicle arrival time at a stop. Accessibility in the sense of proximity is primarily addressed by GIS-oriented methodologies using network analyses in (Háznagy et al., 2015) and (Luo et al., 2019). (Mavoa et al., 2012) uses both proximity and frequency aspects to assess accessibility. ...
The paper aims to introduce a new way of comparing the efficiency of public transport operations based on publicly available data. It draws on four main sources, the Hungarian Central Statistical Office, public transport provider data, GTFS and OSM map layers. Methodologically, it combines the use of the GTFS format and corresponding static timetable component files, Thiessen polygons and empirical selection of relative indicators. As places of research, three comparable Hungarian cities have been selected by their population size; Pécs, Szeged and Miskolc. The comparison consists of 8 quantitative indicators that cover six major geographical aspects of transport operation (accessibility in terms of proximity, capacity, connectivity, density, frequency and velocity of vehicles). The analysis does not consider the mode of public transport, thus opening up the possibility of an independent comparison of efficiency regardless of various infrastructure characteristics. The results show that Miskolc and Pécs achieved the best values in four indicators. On the contrary, the city of Szeged, despite its diverse structure of transport modes, does not have an advantage in any aspect. The relatively loosely anchored methodology leaves room for an extension to include economic, environmental, and other specific factors.
... On the other hand, there are several studies that talk about transport networks in general (Háznagy et al., 2015: Louf et al., 2014Shiau and Lee, 2017). There are also a lot of papers that only study metro (subway) systems as complex networks and their topology. ...
The COVID-19 (SARS-CoV-2) pandemic is changing the world, the way we socialize, as well as politics and public transport logistics and Mexico is not the exception. Authorities are designing new policies for public transport. The first restrictions include the close of different metro stations to ensure the social distance, avoid excess of passengers and reduce the demand, which also changes the metro system and the way it operates. In this paper a model is presented, based on the analysis and comparison of the Mexico’s City Metro System as a total network as well as the network with stations that are closed due to COVID-19. Using complex networks will give to the population information about the connectivity, efficiency and robustness of the system, in order to be able to make improvements, have adequate planning, set up different policies to improve and meet the needs of the system after COVID-19.
... The current article uses research on Internet robustness as a guiding use case for applying graph metrics to study network robustness. However, since the application of graph theory and network science [11] (Chapter 1) is growing in many other research fields such as natural sciences [135], social networks (including networks of human actors like friends, customers, influencers, but also criminals) [136], organizational networks (such as logistics [137,138], public transportation networks [139], information and process flows) and further information technology and service networks (including application-layer communication, cloud connectivity [8], peer-to-peer systems, and blockchain transactions [140]), the present survey could also be consulted by researchers in those and further domains, whenever the robustness of networks needs to be investigated. A more detailed transfer and interpretation of metrics for further networks is left for-potentially very interesting and fruitful-future research in those fields. ...
Research on the robustness of networks, and in particular the Internet, has gained critical importance in recent decades because more and more individuals, societies and firms rely on this global network infrastructure for communication, knowledge transfer, business processes and e-commerce. In particular, modeling the structure of the Internet has inspired several novel graph metrics for assessing important topological robustness features of large complex networks. This survey provides a comparative overview of these metrics, presents their strengths and limitations for analyzing the robustness of the Internet topology, and outlines a conceptual tool set in order to facilitate their future adoption by Internet research and practice but also other areas of network science.
... This method is widely used and considered one of the most effective yet simple methods to assess electrical network resiliency. This method initially started in social studies to represent the relationship between various entities or people and was later applied to critical infrastructure assessment [18][19][20][21][22]. It also forms the base that other methods like logical or optimization used to resemble the network [23]. ...
Looking at cities these days, we could observe the advanced level of developments and integration of technologies in all sectors and systems. However, at the same time, we are living in a changing world. In recent years, humans started to realize the cost of this development and the burden laid on the planet due to human activities. This increase in complexity, challenges, and uncertainties fueled the need to adopt a resiliency-based design approach. In this book chapter, we address the resiliency in energy systems definition, some assessment methods, and the integration of renewable energy sources on it. Also, we added two real case studies that further explain resiliency repercussions and enhancement practices. Nevertheless, considering the importance of mutual dependency and security of critical systems, we highlight these nexuses between energy and some of the critical systems within the scope of resiliency.
... Later, the same authors (Derrible and Kennedy 2010) employed network topological analysis as an initial step in assessing the resilience of a general network. Háznagy et al. (2015) identified similarities and differences within the urban public transportation systems of five Hungarian cities using CNT by comparing their network descriptors. Wang et al. (2015) quantified the robustness of metro networks subjected to random failures and targeted attacks. ...
... 2 networks, stations and stops are treated as nodes, whereas the edges link consecutive stations along specific routes [2]. ...
... The transportation system is an important entity that has substantial economic and social impact in a country. To explore and understand the underlying mechanisms of complex systems such as transportation systems, it is essential to use network analysis [1,2]. The area of network or graph analysis is gaining popularity in recent years due to its effectiveness in studying complex systems and provide opportunities for improvement and optimization. ...
... Without some pre-processing, this data cannot be used for network analysis. Haznagy et al. in 2015 classified bus stoppages as nodes and the trips between stoppages are represented as edges [2]. The researcher ran into some data pre-processing challenges. ...
Graph analysis approach was used to analyze a city bus network. The bus trip data were segregated into 4 smaller bus datasets (weekday-peak-hour, weekday-off-peak-hour, weekend-peak-hour, and weekend-off-peak-hour) with the main goal of studying and identifying the differences between peak hours and off-peak hours, as well as between weekdays and weekends. These differences are identified in terms of global characteristics and network evolution distinctions. Based on the analysis of the simplified network, we identified the relative shape of the networks and the centrality attributes. A comparative analysis is performed between the networks at different times to understand the impact of peak against off-peak and weekday against weekends. Significant differences are found in the analysis. In addition to the above, highly important routes and stops were also identified, where optimization initiative is planned for a more efficient bus transport network. The findings can serve as a starting point for future route optimization of the similar network.
