Abstract
In an urban city, its transportation network supports efficient flow of people between different parts of the city. Failures in the network can cause major disruptions to commuter and business activities which can result in both significant economic and time losses. In this paper, we investigate the use of centrality measures to determine critical nodes in a transportation network so as to improve the design of the network as well as to devise plans for coping with the network failures. Most centrality measures in social network analysis research unfortunately consider only topological structure of the network and are oblivious of transportation factors. This paper proposes new centrality measures that incorporate travel time delay and commuter flow volume. We apply the proposed measures on the Singapore’s subway network involving 89 stations and about 2 million commuter trips per day, and compare them with traditional topology based centrality measures. Several interesting insights about the network are derived from the new measures. We further develop a visual analytics tool to explore the different centrality measures and their changes over time.
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Berche B, von Ferber C, Holovatch T, Holovatch Y (2009) Resilience of public transport networks against attacks. Eur Phys J B 71(1):125–137
Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang D (2006) Complex networks: structure and dynamics. Phys Rep 424(4–5):175–308
Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. In: The seven international conference on world wide web, pp 107–117
De Montis A, Barthelemy M, Chessa A, Vespignani A (2005) The structure of inter-urban traffic: a weighted network analysis
Derrible S (2012) Network centrality of metro systems. PLoS One 7(7): e40575
Dijkstra E (1959) Dijkstra’s algorithm. A note on two problems in connexion with graphs. Numerische Mathematik 1:269–271
Freeman L (1977) A set of measures of centrality based on betweenness. Sociometry 40:35–41
Freeman L (1978/79) Centrality in social networks: conceptual clarification. Soc Netw 1:215–239
Gao M, Lim E-P, Lo D (213) R-energy for evaluating robustness of dynamic networks. In: ACM conference on web science
Goh K-I, Oh E, Jeong H, Kahng B, Kim D (2002) Classification of scale-free networks. PNAS 99(20):12583–12588
Guimer R, Mossa S, Turtschi A, Amaral LAN (2005) The worldwide air transportation network: anomalous centrality, community structure, and cities’ global roles. Proc Natl Acad Sci USA 102(22):7794–7799
Newman M (2008) The mathematics of networks. In: Blume L, Durlauf S (eds) The new Palgrave encyclopedia of economics, 2nd edn. Palgrave Macmillan, Canada
Opsahl T, Agneessens F, Skvoretz J (2010) Node centrality in weighted networks: generalizing degree and shortest paths. Soc Netw 32(3):245–251
Scheurer J, Curtis C, Porta S (2008) Spatial network analysis of multimodal transport systems: developing a strategic planning tool to assess the congruence of movement and urban structure. In: Technical report GAMUT2008/JUN/02, GAMUT Australasian centre for the governance and management of urban transport, The University of Melbourne
Sevtsuk A, Mekonnen M (2012) Urban network analysis toolbox. Int J Geomat Spat Anal 22(2):287–305
Acknowledgments
We would like to thank the Land Transport Authority (LTA) of Singapore for sharing with us the MRT dataset. This work is supported by the National Research Foundation under its International Research Centre@Singapore Funding Initiative and administered by the IDM Programme Office, and National Research Foundation (NRF).
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Cheng, YY., Lee, R.KW., Lim, EP., Zhu, F. (2015). Measuring Centralities for Transportation Networks Beyond Structures. In: Kazienko, P., Chawla, N. (eds) Applications of Social Media and Social Network Analysis. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-19003-7_2
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DOI: https://doi.org/10.1007/978-3-319-19003-7_2
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