Topologic characteristics and sustainable growth of worldwide urban rail networks

2021 ◽  
pp. 2150151
Author(s):  
Rui Ding ◽  
Ting Zhang ◽  
Tao Zhou ◽  
Yilin Zhang ◽  
Tongfei Li ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Dynamic Network ◽  
Network Size ◽  
Sustainable Growth ◽  
Network Efficiency ◽  
Network Growth ◽  
Network Expansion ◽  
Rail Network ◽  
Urban Rail ◽  
Network Properties

The urban rail network system plays a significant role in the urban transportation system and urban economic development. Further study of the urban rail network properties can provide additional guidance to related scholars and designers. This study explored urban rail network properties worldwide, including assessments of their static and dynamic network topologic characteristics. Statically, this study analyzed various related network topological indicators for all of these urban rail networks. We found that, with increasing network size, the average degree slightly increases while the complexity and connectivity decrease. Using the Kolmogorov–Smirnov goodness-of-fit, the scale of the degree interval of these cities is [3.5 12.2]. Approximately 90% of these cities have network efficiency values less than 0.12, and 78% of these cities have lower assortativity coefficients. Focusing on the sustainable growth of rail networks, this study tested some specific networks to further deliberate their network expansion ability, network growth, and network robustness properties. The network expansion capability of small networks is relatively poor, while that of large networks is relatively strong. A simulation of network growth suggests that the connection of nodes with the maximum path length will seriously affect the efficiency and characteristics of the network. The robustness of the network indicates that adopting the maximum nodal degree elimination strategy will affect the function of the network. The results provide essential reference information for the rational planning, structural optimization, safe operation and sustainable growth of rail transit networks.

Network properties as functional traits for fungi

2021 ◽  
Author(s):  
Carlos Aguilar-Trigueros ◽  
Mark Fricker ◽  
Matthias Rillig
Keyword(s):  
Functional Traits ◽  
Interspecific Variation ◽  
Functional Trait ◽  
Ecological Strategies ◽  
Body Type ◽  
Network Growth ◽  
Transport Efficiency ◽  
Network Parameters ◽  
Phylogenetic Affiliation ◽  
Network Properties

<p>Fungal mycelia consist of an interconnected network of filamentous hyphae and represent the dominant phase of the lifecycle in all major fungal phyla, from basal to more recent clades. Indeed, the ecological success of fungi on land is partly due to such filamentous network growth. Nevertheless, fungal ecologists rarely use network features as functional traits. Given the widespread occurrence of this body type, we hypothesized that interspecific variation in network features may reflect both phylogenetic affiliation and distinct ecological strategies among species. We show first that there is high interspecific variation in network parameters of fungi, which partly correlates with taxonomy; and second that network parameters, related to predicted-mycelial transport mechanisms during the exploration phase, reveal the trait space in mycelium architecture across species.  This space predicts a continuum of ecological strategies along two extremes: from highly connected mycelia with high resilience to damage but limited transport efficiency, to poorly connected mycelia with low resilience but high transport efficiency. We argue that mycelial networks are potentially a rich source of information to inform functional trait analysis in fungi, but we also note the challenges in establishing common principles and processing pipelines that are required to facilitate widespread use of network properties as functional traits in fungal ecology.</p>

Measuring Performance of Dynamic and Network Structures by SBM Model

2015 ◽  
pp. 527-558
Author(s):  
N. Aghayi ◽  
Z. Ghelej Beigi ◽  
K. Gholami ◽  
F. Hosseinzadeh Lotfi
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Dynamic Network ◽  
Black Box ◽  
Time Dependent ◽  
Data Envelopment ◽  
Network Efficiency ◽  
Sbm Model ◽  
Radial Model ◽  
Decision Making Units

The conventional Data Envelopment Analysis (DEA) model considers Decision Making Units (DMUs) as a black box, meaning that these models do not consider the connection and the inner structures of DMUs. Moreover, these models consider that the activities of DMUs in each time are independent of other times, but in the real world, the inner structures of DMUs are complicated, and the activities of DMUs are dependent on other times. Therefore, in this chapter, the authors consider DMUs with network structure and the activity of each DMU in each time dependent to activity of other times, so they call this structure a dynamic network. To this end, in this chapter, models are suggested to evaluate the dynamic network efficiency based on the SBM model, which is a non-radial model of three types with respect to orientation: input-oriented, output-oriented, and non-oriented.

Discovering the evolution of Beijing Rail Network in fifty years

2020 ◽  
Vol 34 (21) ◽  
pp. 2050212
Author(s):  
Zijia Wang ◽  
Jingqi Li ◽  
Liping Huang ◽  
Zhigang Yang
Keyword(s):  
Pearson Correlation ◽  
Service Level ◽  
Urban Rail Transit ◽  
Transit Network ◽  
Rail Network ◽  
Topological Evolution ◽  
Topological Network ◽  
Urban Rail ◽  
Subway System ◽  
One Year

Urban rail transit (URT) system plays a significant role in daily commuting. The main features of URT could be abstracted into two kinds of networks, topological network and transit network. The correlation between topological network and transit network could represent the service level of transportation which is also a main focus to some extent. In this study, static analysis based on one year or single analysis based on one aspect are abundant, the main reason of which is the deficiency of the consistent demand data. In this regard, a comprehensive analysis of the evolution and their correlation of the two network are carried out in this work. We first revisit the topological evolution of rail network on the basis of URT network statistic indicators with a fifty-year time span. Then, based on the traditional node-place model, a correctional node-place model between demand spatial distribution and closeness centrality is established. Pearson correlation coefficient is also employed for a precise analysis. Finally, the application of the model and the corresponding analysis on Beijing Subway System (BSS) examine and evaluate the development level and service level of URT system in Beijing, and some solid evidence for relative decision-making is provided.

scholarly journals Dynamic network prediction

2020 ◽  
Vol 8 (4) ◽  
pp. 574-595
Author(s):  
Ravi Goyal ◽  
Victor De Gruttola
Keyword(s):  
Social Relationships ◽  
Network Structure ◽  
Broad Class ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Simulation Studies ◽  
Population Composition ◽  
Statistical Framework ◽  
Network Properties ◽  
Network Prediction

AbstractWe present a statistical framework for generating predicted dynamic networks based on the observed evolution of social relationships in a population. The framework includes a novel and flexible procedure to sample dynamic networks given a probability distribution on evolving network properties; it permits the use of a broad class of approaches to model trends, seasonal variability, uncertainty, and changes in population composition. Current methods do not account for the variability in the observed historical networks when predicting the network structure; the proposed method provides a principled approach to incorporate uncertainty in prediction. This advance aids in the designing of network-based interventions, as development of such interventions often requires prediction of the network structure in the presence and absence of the intervention. Two simulation studies are conducted to demonstrate the usefulness of generating predicted networks when designing network-based interventions. The framework is also illustrated by investigating results of potential interventions on bill passage rates using a dynamic network that represents the sponsor/co-sponsor relationships among senators derived from bills introduced in the U.S. Senate from 2003 to 2016.

scholarly journals Modelling community formation driven by the status of individual in a society

2017 ◽  
Vol 5 (6) ◽  
pp. 817-838 ◽  
Author(s):  
Jan E Snellman ◽  
Gerardo Iñiguez ◽  
Tzipe Govezensky ◽  
R A Barrio ◽  
Kimmo K Kaski
Keyword(s):  
Social Status ◽  
Network Size ◽  
Community Formation ◽  
Human Society ◽  
Agent Based ◽  
The Social ◽  
The Status ◽  
Network Properties ◽  
Willingness To Compete ◽  
New Strategies

Abstract In human societies, people’s willingness to compete and strive for better social status, as well as being envious of those perceived in some way superior, lead to social structures that are intrinsically hierarchical. Here, we propose an agent-based, network model to mimic the ranking behaviour of individuals and its possible repercussions in human society. The main ingredient of the model is the assumption that the relevant feature of social interactions is each individual’s keenness to maximize his or her status relative to others. The social networks produced by the model are homophilous and assortative, as frequently observed in human communities, and most of the network properties seem quite independent of its size. However, we see that for a small number of agents the resulting network consists of disjoint weakly connected communities, while being highly assortative and homophilic. On the other hand, larger networks turn out to be more cohesive with larger communities but less homophilic. We find that the reason for these changes is that larger network size allows agents to use new strategies for maximizing their social status, allowing for more diverse links between them.

scholarly journals Application of Complex Network Principles to Key Station Identification in Railway Network Efficiency Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Li Wang ◽  
Min An ◽  
Limin Jia ◽  
Yong Qin
Keyword(s):  
Complex Network ◽  
Policy Development ◽  
Network Models ◽  
Efficiency Analysis ◽  
Physical Structure ◽  
Influential Factors ◽  
Clustering Coefficient ◽  
Network Efficiency ◽  
Railway Network ◽  
Rail Network

Network efficiency analysis becomes important in railways in order to contribute towards improving the safety and capacity of the rail network, making rail travel more attractive for passengers, and improving industry practice and informing policy development. However, a physical railway network structure is a complicated system, and the operation, maintenance, and management of such a network is a difficult task which may be affected by many influential factors. By using efficiency analysis technology for a railway network, combining physical structure with operation functions can help railway industry to optimize the railway network while improving its efficiency and reliability. This paper presents a new methodology based on complex network principles that combines the physical railway structure with railway operation strategy for a railway network efficiency analysis. In this method, two network models of railway physical and train flow networks are developed for the identification of key stations in the railway network based on network efficiency contribution in which the terms of degree, strength, betweenness, clustering coefficient, and a comprehensive factor are taken into consideration. Once the key stations have been identified and analysed, the railway network efficiency is then studied on the basis of selective and random modes of the station failures. A case study is presented in this paper to demonstrate the application of the proposed methodology. The results show that the identified key stations in the railway network play an important role in improving the overall railway network efficiency, which can provide useful information to railway designers, engineers, operators and maintainers to operate and maintain railway network effectively and efficiently.

A Learning Model of Passenger’s Path Choice in Urban Rail Network Based on Bayesian Method

2012 ◽  
Vol 253-255 ◽  
pp. 1782-1786
Author(s):  
Shi Min Du ◽  
Qin Luo ◽  
Yu Zheng ◽  
Ling Yang Kong
Keyword(s):  
Bayesian Method ◽  
Learning Model ◽  
Operating Conditions ◽  
Urban Rail Transit ◽  
Sample Mean ◽  
Rail Network ◽  
Urban Rail ◽  
Flow Formation ◽  
Knowledge Updating ◽  
Formation And Evolution

To determine passenger’s path choice is the base for research on ridership distribution in urban rail network. Considering passenger’s multi-days’ trips as a process to continuously update path information expressed by its sample mean and variance of travel cost, the paper establishes a self-learning model of passenger’s path choice in urban rail network based on Bayesian method by analyzing the acquisition of prior information beforetrip and knowledge updating after trip, as well as taking the adjustment of Urban Rail Transit operating conditions into account.The model proposed can be used to analyze the passenger flow formation and evolution mechanism in urban rail network.

Influence of Emergency Passenger Flow Distribution in Urban Rail Network

2012 ◽  
Vol 450-451 ◽  
pp. 295-301 ◽  
Author(s):  
Ling Hong ◽  
Jia Gao ◽  
Rui Hua Xu
Keyword(s):  
Flow Distribution ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Abstract Model ◽  
Flow Volume ◽  
Rail Network ◽  
Passenger Flow ◽  
Urban Rail ◽  
Passenger Flows ◽  
Urban Rail Transit Network

The emergency disposal of urban rail transit needs to accurately estimate the emergency range and total affected passenger flow volume. The urban rail transit network could be simplified to an abstract model which is easy to be analyst based on the graph theory method. Considering the actual network back-turning lines and vehicle storage tracks of urban rail network, the emergency range could be estimated effectively. The affected passenger flow could be classified as different kinds based on the different paths of passenger flow. The classification of passenger flow mainly includes “delay passenger flow”, “detour passenger flow” and “loss passenger flow”. Considering the emergency range, the different affected passenger flows could be superposed over time based on the abstract model, then the affected passenger flow volume and virtual loss time could be calculated out. The results could provide basis for the emergency disposal in urban rail transit. The example analysis is verified the feasibility of this method.

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