Complex network analysis of public transportation networks: A comprehensive study

Háznagy, Andor; Fi, István; London, András; Németh, Tamás

doi:10.1109/mtits.2015.7223282

Cited by 60 publications

(32 citation statements)

References 24 publications

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“…This has led to efforts to incorporate PT specific features into complex network analysis of PTNs, such as travel demand and service attributes (e.g., passenger flows, transfers, service frequency, travel times, etc.). More weighted complex network analyses have emerged to account for demand and supply patterns in PTNs (e.g., Soh et al, 2010;Haznagy et al, 2015;Feng et al, 2017). Furthermore, investigations into the vulnerability, robustness and (node and link) criticality of PTNs have explicitly considered passenger demand and flow assignment (e.g., Cats and Jenelius, 2014;Cats, 2016;Cats et al, 2016Cats et al, , 2017.…”

Section: Network Science Analysis Of Ptnsmentioning

confidence: 99%

Integrating network science and public transport accessibility analysis for comparative assessment

Luo

Cats

Lint

et al. 2019

Journal of Transport Geography

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Network science offers powerful concepts and methods for studying complex systems, such as public transport networks. However, many existing studies on complex network analysis of public transport networks were primarily motivated to test network science concepts using real-life networks. Consequently, important properties related to public transport services in these studies have been overlooked. This has led to claims made by transport researchers that these works do not necessarily improve the understanding of system properties and performance. To bridge this gap, this study integrates network science and public transport accessibility analysis for comparative assessment across multiple public transport networks. The primary contribution of this study pertains to developing a method based on network science for computing public transport accessibility measured as the average travel impedance. The travel impedance metric is defined based on the generalized travel cost between stop pairs, containing initial and transfer waiting times, in-vehicle travel times and time-equivalent transfer penalty costs. To perform efficient computation, we propose a new type of weighted graph representation of public transport networks, which explicitly incorporates travel costs that are derived from general transit feed specification (GTFS) data. Besides the methodological contribution, the secondary one of this study consists in performing a comparative assessment of worldwide tram networks' accessibility. The analysis shows insights into how different travel components (e.g., in-vehicle travel times and waiting and transfer times) specifically account for the variance in accessibility across different networks. Additional references for improving the designing and planning of public transport networks can be obtained from such comparative assessments.

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Section: Network Science Analysis Of Ptnsmentioning

confidence: 99%

Integrating network science and public transport accessibility analysis for comparative assessment

Luo

Cats

Lint

et al. 2019

Journal of Transport Geography

View full text Add to dashboard Cite

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“…Single layer studies on transportation systems are extensive covering air transport (Lordan et al, 2016(Lordan et al, , 2015Lordan, 2014;Lordan et al, 2014b;Sun et al, 2017;Zanin et al, 2009;Zanin and Lillo, 2013), maritime networks (Bartholdi and Jarumaneeroj, 2014;Ducruet et al, 2010a,b;Ducruet and Notteboom, 2012;Ducruet, 2016;González Laxe et al, 2012;Hu and Zhu, 2009;Fraser et al, 2014;Mohamed-Chérif and Ducruet, 2016;Pais Montes et al, 2012;Tsiostas and Polyzos, 2015;Viljoen and Joubert, 2016), train, bus, tram and subway systems (Criado et al, 2007;Kurant and Thiran, 2006a;Mouronte and Benito, 2012;Ouyang et al, 2014;Sen et al, 2003), and the structural properties and vulnerability of road networks (Crucitti et al, 2006;Duan and Lu, 2014;Gudmundsson and Mohajeri, 2013;Háznagy et al, 2015;Jiang, 2007;Jiang and Claramunt, 2004;Porta et al, 2012;Reggiani et al, 2011;Rupi et al, 2015;Strano et al, 2009;Zadeh and Rajabi, 2013).…”

Section: Transportation Applicationsmentioning

confidence: 99%

The Road most Travelled: The Impact of Urban Road Infrastructure on Supply Chain Network Vulnerability

Viljoen

Joubert

2017

Netw Spat Econ

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Making a supply chain more resilient and making it more efficient are often diametrically opposed objectives. Managers have to make informed trade-offs when designing their supply chain networks. There are many methods available to quantify and optimise efficiency. Unfortunately the same cannot be said for vulnerability and resilience. We propose a method to quantify the impact that a supply chain's dependence on the underlying transport infrastructure has on its vulnerability. The dependence relationship is modelled using multilayered complex network theory. We develop two metrics relating to the unique collection of shortest path sets namely redundancy and overlap. To test the relationships between these metrics and supply chain vulnerability we simulate progressive random link disruption of the urban road network and assess the impact this has on Fully Connected, Single Hub and Double Hub network archetypes. The results show that redundancy and overlap of the collection of shortest paths are significantly related to supply chain resilience, however under a purely random disturbance regime they hold no predictive power. This paper builds a foundation for a new field of inquiry into supply chain vulnerability by presenting a flexible mathematical formulation of the multilayered network and defining and testing two novel metrics that could be incorporated into supply chain network design decisions. Keywords supply chain vulnerability • urban road network • multilayer complex network • shortest paths • link betweenness This article has been published and should be cited as: Viljoen, N. M., & Joubert, J. W. (2018). The road most travelled: The impact of urban road infrastructure on supply chain network vulnerability. Networks and Spatial Economics, 18(1), 85-113.

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“…For urban bus service systems, a body of literature has utilized the sophisticated complex network theory to uncover the raw statistics of bus networks by calculating a set of indicators such as degree, centrality, and clustering coefficient [9,[25][26][27]. For example, researchers can compare bus networks in different cities by quantifying their configurations with these indicators [28][29][30]. In statistical analysis, an important measurement method is to examine the degree distribution of nodes to determine whether the bus network is a scale-free network [31,32].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Characteristics of an Intra-Urban Bus Service Network: A Case Study of Shenzhen, China

Yang

Zhao

et al. 2019

IJGI

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The urban bus service system is one of the most important components of a public transport system. Thus, exploring the spatial configuration of the urban bus service system promotes an understanding of the quality of bus services. Such an understanding is of great importance to urban transport planning and policy making. In this study, we investigated the spatial characteristics of an urban bus service system by using the complex network approach. First, a three-step workflow was developed to collect a bus operating dataset from a public website. Then, we utilized the P-space method to represent the bus service network by connecting all bus stop pairs along each bus line. With the constructed bus network, a set of network analysis indicators were calculated to quantify the role of nodes in the network. A case study of Shenzhen, China was implemented to understand the statistical properties and spatial characteristics of the urban bus network configuration. The empirical findings can provide insights into the statistical laws and distinct convenient areas in a bus service network, and consequently aid in optimizing the allocation of bus stops and routes.

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Complex network analysis of public transportation networks: A comprehensive study

Cited by 60 publications

References 24 publications

Integrating network science and public transport accessibility analysis for comparative assessment

Integrating network science and public transport accessibility analysis for comparative assessment

The Road most Travelled: The Impact of Urban Road Infrastructure on Supply Chain Network Vulnerability

Exploring the Characteristics of an Intra-Urban Bus Service Network: A Case Study of Shenzhen, China

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