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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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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