2018
Conference article
Open Access
Weak nodes detection in urban transport systems: planning for resilience in Singapore
Ferretti M., Barlacchi G., Pappalardo L., Lucchini L., Lepri B.
Social and Information Networks (cs.SI) Urban science Data science Multiplex networks FOS: Physical sciences Human mobility Complex systems Network science Resilience Computer Science - Social and Information Networks FOS: Computer and information sciences Physics - Physics and Society Social good Physics and Society (physics.soc-ph)
The availability of massive data-sets describing human mobility offers the possibility to design simulation tools to monitor and improve the resilience of transport systems in response to traumatic events such as natural and man-made disasters (e.g., floods, terrorist attacks, etc...). In this perspective we propose ACHILLES, an application to models people's movements in a given transport mode through a multiplex network representation based on mobility data. ACHILLES is a web-based application which provides an easy-to-use interface to explore the mobility fluxes and the connectivity of every urban zone in a city, as well as to visualize changes in the transport system resulting from the addition or removal of transport modes, urban zones and single stops. Notably, our application allows the user to assess the overall resilience of the transport network by identifying its weakest node, i.e. Urban Achilles Heel, with reference to the ancient Greek mythology. To demonstrate the impact of ACHILLES for humanitarian aid we consider its application to a real-world scenario by exploring human mobility in Singapore in response to flood prevention.
Source: DSAA 2018 - IEEE 5th International Conference on Data Science and Advanced Analytics, pp. 472–480, 01-04 October 2018
[1] Mohammed N Ahmed, Gianni Barlacchi, Stefano Braghin, Francesco Calabrese, Michele Ferretti, Vincent Lonij, Rahul Nair, Rana Novack, Jurij Paraszczak, and Andeep S Toor. A multi-scale approach to datadriven mass migration analysis. In SoGood@ ECML-PKDD, 2016.
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[3] Hugo Barbosa-Filho, Marc Barthelemy, Gourab Ghoshal, Charlotte R. James, Maxime Lenormand, Thomas Louail, Ronaldo Menezes, Jose´ J. Ramasco, Filippo Simini, and Marcello Tomasini. Human Mobility: Models and Applications. Physics Reports, 2018.
[4] Gianni Barlacchi, Marco De Nadai, Roberto Larcher, Antonio Casella, Cristiana Chitic, Giovanni Torrisi, Fabrizio Antonelli, Alessandro Vespignani, Alex Pentland, and Bruno Lepri. A multi-source dataset of urban life in the city of milan and the province of trentino. Scientific data, 2:150055, 2015.
[5] Gianni Barlacchi, Christos Perentis, Abhinav Mehrotra, Mirco Musolesi, and Bruno Lepri. Are you getting sick? predicting influenza-like symptoms using human mobility behaviors. EPJ Data Science, 6(1):27, Oct 2017.
[6] Gianni Barlacchi, Alberto Rossi, Bruno Lepri, and Alessandro Moschitti. Structural semantic models for automatic analysis of urban areas. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 279-291. Springer, 2017.
[7] Paul Barter and Edward Dotson. Urban transport institutions and governance and integrated land use and transport, singapore. Case Study Prepared for Global Report on Human Settlements, 2013.
[8] Marc Barthelemy. The structure and dynamics of cities. Cambridge University Press, 2016.
[9] Michael Batty. The size, scale, and shape of cities. Science, 319(5864):769-771, 2008.
[10] M. Berlingerio, M. Coscia, F. Giannotti, A. Monreale, and D. Pedreschi. Foundations of multidimensional network analysis. In 2011 International Conference on Advances in Social Networks Analysis and Mining, pages 485-489, July 2011.
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[13] Manlio De Domenico, Albert Sole´-Ribalta, Emanuele Cozzo, Mikko Kivela¨, Yamir Moreno, Mason A. Porter, Sergio G o´mez, and Alex Arenas. Mathematical formulation of multilayer networks. Phys. Rev. X, 3:041022, Dec 2013.
[14] Manlio De Domenico, Albert Sole´-Ribalta, Sergio Gmez, and Alex Arenas. Navigability of interconnected networks under random failures. Proceedings of the National Academy of Sciences, 111(23):8351-8356, 2014.
[15] Marco De Nadai, Jacopo Staiano, Roberto Larcher, Nicu Sebe, Daniele Quercia, and Bruno Lepri. The death and life of great italian cities: A mobile phone data perspective. In Proceedings of the 25th International Conference on World Wide Web, WWW '16, pages 413-423, Republic and Canton of Geneva, Switzerland, 2016. International World Wide Web Conferences Steering Committee.
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[17] Alexander A. Ganin, Maksim Kitsak, Dayton Marchese, Jeffrey M. Keisler, Thomas Seager, and Igor Linkov. Resilience and efficiency in transportation networks. Science Advances, 3(12):1-9, 2017.
[18] Fosca Giannotti, Mirco Nanni, Dino Pedreschi, Fabio Pinelli, Chiara Renso, Salvatore Rinzivillo, and Roberto Trasarti. Unveiling the complexity of human mobility by querying and mining massive trajectory data. The VLDB Journal, 20(5):695-719, October 2011.
[19] Marta C Gonzalez, Cesar A Hidalgo, and Albert-Laszlo Barabasi. Understanding individual human mobility patterns. nature, 453(7196):779, 2008.
[20] M.S. Granovetter. The Strength of Weak Ties. The American Journal of Sociology, 78(6):1360-1380, 1973.
[21] Torsten Ha¨gerstrand. What about people in regional science? Papers of the Regional Science Association, 24(1):6-21, Dec 1970.
[22] W. H. Ip and Dingwei Wang. Resilience evaluation approach of transportation networks. Proceedings of the 2009 International Joint Conference on Computational Sciences and Optimization, CSO 2009, 2:618-622, 2009.
[23] AK Jain, MN Murty, and PJ Flynn. Estimating origin-destination flows using mobile phone location data. ACM Computing Surveys, 31(3):264- 323, 1999.
[24] S. Jiang, J. Ferreira, and M. C. Gonza´lez. Activity-based human mobility patterns inferred from mobile phone data: A case study of singapore. IEEE Transactions on Big Data, 3(2):208-219, June 2017.
[25] Shan Jiang, Yingxiang Yang, Siddharth Gupta, Daniele Veneziano, Shounak Athavale, and Marta C. Gonza´lez. The timegeo modeling framework for urban mobility without travel surveys. Proceedings of the National Academy of Sciences, 113(37):E5370-E5378, 2016.
[26] Matthew James Kelley. The semantic production of space: Pervasive computing and the urban landscape. Environment and Planning A, 46(4):837-851, 2014.
[27] Rob Kitchin and Martin Dodge. Code/space: Software and everyday life. Mit Press, 2011.
[28] Xiaoyue Cathy Liu, Guohui Zhang, et al. An efficient general transit feed specification (gtfs) enabled algorithm for dynamic transit accessibility analysis. PloS one, 12(10):e0185333, 2017.
[29] Tianyi Ma, Gianmario Motta, and Kaixu Liu. Delivering real-time information services on public transit: A framework. IEEE Transactions on Intelligent Transportation Systems, 18(10):2642-2656, 2017.
[30] Luca Pappalardo, Salvatore Rinzivillo, and Filippo Simini. Human mobility modelling: Exploration and preferential return meet the gravity model. Procedia Computer Science, 83:934 - 939, 2016. The 7th International Conference on Ambient Systems, Networks and Technologies (ANT 2016) / The 6th International Conference on Sustainable Energy Information Technology (SEIT-2016) / Affiliated Workshops.
[31] Luca Pappalardo, Giulio Rossetti, and Dino Pedreschi. How well do we know each other? detecting tie strength in multidimensional social networks. In 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pages 1040-1045, Aug 2012.
[32] Luca Pappalardo and Filippo Simini. Data-driven generation of spatiotemporal routines in human mobility. Data Mining and Knowledge Discovery, 32(3):787-829, May 2018.
[33] Luca Pappalardo, Filippo Simini, Salvatore Rinzivillo, Dino Pedreschi, Fosca Giannotti, and Albert-La´szl o´ Baraba´si. Returners and explorers dichotomy in human mobility. Nature Communications, 6, 09 2015.
[34] K. T. Pham, P. Sattigeri, A. Dhurandhar, A. C. Jacob, M. Vukovic, P. Chataigner, J. Freire, A. Mojsilovi, and K. R. Varshney. Real-time understanding of humanitarian crises via targeted information retrieval. IBM Journal of Research and Development, 61(6):7:1-7:12, Nov 2017.
[35] Aura Reggiani, Thomas De Graaff, and Peter Nijkamp. Resilience: an evolutionary approach to spatial economic systems. Networks and Spatial Economics, 2(2):211-229, 2002.
[36] Horst W J Rittel and Melvin M. Webber. Dilemmas in a general theory of planning. Policy Sciences, 4(2):155-169, 1973.
[37] Alberto Rossi, Gianni Barlacchi, Monica Bianchini, and Bruno Lepri. Modeling taxi drivers' behaviour for the next destination prediction. arXiv preprint arXiv:1807.08173, 2018.
[38] Matthias C Scheffel, Claus G Gruber, Thomas Schwabe, and Robert G Prinz. Optimal multi-topology routing for ip resilience. AEUInternational Journal of Electronics and Communications, 60(1):35-39, 2006.
[39] Filippo Simini, Marta C. Gonzalez, Amos Maritan, and Albert-Laszlo Barabasi. A universal model for mobility and migration patterns. Nature, 484(7392):96-100, 4 2012.
[40] United Nations. World Urbanization Prospects: The 2014 Revision. Technical report, Department of Economic and Social Affairs Population Division, New York, NY, USA, 2014.
[41] Yanyan Xu and Marta C. Gonza´lez. Collective benefits in traffic during mega events via the use of information technologies. Journal of The Royal Society Interface, 14(129), 2017.
[42] George Kingsley Zipf. The p 1 p 2/d hypothesis: on the intercity movement of persons. American sociological review, 11(6):677-686, 1946.
[2] Jacopo A. Baggio, Shauna B. BurnSilver, Alex Arenas, James S. Magdanz, Gary P. Kofinas, and Manlio De Domenico. Multiplex social ecological network analysis reveals how social changes affect community robustness more than resource depletion. Proceedings of the National Academy of Sciences, 113(48):13708-13713, 2016.
[3] Hugo Barbosa-Filho, Marc Barthelemy, Gourab Ghoshal, Charlotte R. James, Maxime Lenormand, Thomas Louail, Ronaldo Menezes, Jose´ J. Ramasco, Filippo Simini, and Marcello Tomasini. Human Mobility: Models and Applications. Physics Reports, 2018.
[4] Gianni Barlacchi, Marco De Nadai, Roberto Larcher, Antonio Casella, Cristiana Chitic, Giovanni Torrisi, Fabrizio Antonelli, Alessandro Vespignani, Alex Pentland, and Bruno Lepri. A multi-source dataset of urban life in the city of milan and the province of trentino. Scientific data, 2:150055, 2015.
[5] Gianni Barlacchi, Christos Perentis, Abhinav Mehrotra, Mirco Musolesi, and Bruno Lepri. Are you getting sick? predicting influenza-like symptoms using human mobility behaviors. EPJ Data Science, 6(1):27, Oct 2017.
[6] Gianni Barlacchi, Alberto Rossi, Bruno Lepri, and Alessandro Moschitti. Structural semantic models for automatic analysis of urban areas. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 279-291. Springer, 2017.
[7] Paul Barter and Edward Dotson. Urban transport institutions and governance and integrated land use and transport, singapore. Case Study Prepared for Global Report on Human Settlements, 2013.
[8] Marc Barthelemy. The structure and dynamics of cities. Cambridge University Press, 2016.
[9] Michael Batty. The size, scale, and shape of cities. Science, 319(5864):769-771, 2008.
[10] M. Berlingerio, M. Coscia, F. Giannotti, A. Monreale, and D. Pedreschi. Foundations of multidimensional network analysis. In 2011 International Conference on Advances in Social Networks Analysis and Mining, pages 485-489, July 2011.
[11] Serdar C¸ olak, Antonio Lima, and Marta C Gonza´lez. Understanding congested travel in urban areas. Nature Communications, 7:10793, 2016.
[12] Manlio De Domenico, Antonio Lima, Marta C Gonza´lez, and Alex Arenas. Personalized routing for multitudes in smart cities. EPJ Data Science, 4(1):1, 2015.
[13] Manlio De Domenico, Albert Sole´-Ribalta, Emanuele Cozzo, Mikko Kivela¨, Yamir Moreno, Mason A. Porter, Sergio G o´mez, and Alex Arenas. Mathematical formulation of multilayer networks. Phys. Rev. X, 3:041022, Dec 2013.
[14] Manlio De Domenico, Albert Sole´-Ribalta, Sergio Gmez, and Alex Arenas. Navigability of interconnected networks under random failures. Proceedings of the National Academy of Sciences, 111(23):8351-8356, 2014.
[15] Marco De Nadai, Jacopo Staiano, Roberto Larcher, Nicu Sebe, Daniele Quercia, and Bruno Lepri. The death and life of great italian cities: A mobile phone data perspective. In Proceedings of the 25th International Conference on World Wide Web, WWW '16, pages 413-423, Republic and Canton of Geneva, Switzerland, 2016. International World Wide Web Conferences Steering Committee.
[16] Brian Ferris, Kari Watkins, and Alan Borning. Location-aware tools for improving public transit usability. IEEE Pervasive Computing, (1):13- 19, 2009.
[17] Alexander A. Ganin, Maksim Kitsak, Dayton Marchese, Jeffrey M. Keisler, Thomas Seager, and Igor Linkov. Resilience and efficiency in transportation networks. Science Advances, 3(12):1-9, 2017.
[18] Fosca Giannotti, Mirco Nanni, Dino Pedreschi, Fabio Pinelli, Chiara Renso, Salvatore Rinzivillo, and Roberto Trasarti. Unveiling the complexity of human mobility by querying and mining massive trajectory data. The VLDB Journal, 20(5):695-719, October 2011.
[19] Marta C Gonzalez, Cesar A Hidalgo, and Albert-Laszlo Barabasi. Understanding individual human mobility patterns. nature, 453(7196):779, 2008.
[20] M.S. Granovetter. The Strength of Weak Ties. The American Journal of Sociology, 78(6):1360-1380, 1973.
[21] Torsten Ha¨gerstrand. What about people in regional science? Papers of the Regional Science Association, 24(1):6-21, Dec 1970.
[22] W. H. Ip and Dingwei Wang. Resilience evaluation approach of transportation networks. Proceedings of the 2009 International Joint Conference on Computational Sciences and Optimization, CSO 2009, 2:618-622, 2009.
[23] AK Jain, MN Murty, and PJ Flynn. Estimating origin-destination flows using mobile phone location data. ACM Computing Surveys, 31(3):264- 323, 1999.
[24] S. Jiang, J. Ferreira, and M. C. Gonza´lez. Activity-based human mobility patterns inferred from mobile phone data: A case study of singapore. IEEE Transactions on Big Data, 3(2):208-219, June 2017.
[25] Shan Jiang, Yingxiang Yang, Siddharth Gupta, Daniele Veneziano, Shounak Athavale, and Marta C. Gonza´lez. The timegeo modeling framework for urban mobility without travel surveys. Proceedings of the National Academy of Sciences, 113(37):E5370-E5378, 2016.
[26] Matthew James Kelley. The semantic production of space: Pervasive computing and the urban landscape. Environment and Planning A, 46(4):837-851, 2014.
[27] Rob Kitchin and Martin Dodge. Code/space: Software and everyday life. Mit Press, 2011.
[28] Xiaoyue Cathy Liu, Guohui Zhang, et al. An efficient general transit feed specification (gtfs) enabled algorithm for dynamic transit accessibility analysis. PloS one, 12(10):e0185333, 2017.
[29] Tianyi Ma, Gianmario Motta, and Kaixu Liu. Delivering real-time information services on public transit: A framework. IEEE Transactions on Intelligent Transportation Systems, 18(10):2642-2656, 2017.
[30] Luca Pappalardo, Salvatore Rinzivillo, and Filippo Simini. Human mobility modelling: Exploration and preferential return meet the gravity model. Procedia Computer Science, 83:934 - 939, 2016. The 7th International Conference on Ambient Systems, Networks and Technologies (ANT 2016) / The 6th International Conference on Sustainable Energy Information Technology (SEIT-2016) / Affiliated Workshops.
[31] Luca Pappalardo, Giulio Rossetti, and Dino Pedreschi. How well do we know each other? detecting tie strength in multidimensional social networks. In 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pages 1040-1045, Aug 2012.
[32] Luca Pappalardo and Filippo Simini. Data-driven generation of spatiotemporal routines in human mobility. Data Mining and Knowledge Discovery, 32(3):787-829, May 2018.
[33] Luca Pappalardo, Filippo Simini, Salvatore Rinzivillo, Dino Pedreschi, Fosca Giannotti, and Albert-La´szl o´ Baraba´si. Returners and explorers dichotomy in human mobility. Nature Communications, 6, 09 2015.
[34] K. T. Pham, P. Sattigeri, A. Dhurandhar, A. C. Jacob, M. Vukovic, P. Chataigner, J. Freire, A. Mojsilovi, and K. R. Varshney. Real-time understanding of humanitarian crises via targeted information retrieval. IBM Journal of Research and Development, 61(6):7:1-7:12, Nov 2017.
[35] Aura Reggiani, Thomas De Graaff, and Peter Nijkamp. Resilience: an evolutionary approach to spatial economic systems. Networks and Spatial Economics, 2(2):211-229, 2002.
[36] Horst W J Rittel and Melvin M. Webber. Dilemmas in a general theory of planning. Policy Sciences, 4(2):155-169, 1973.
[37] Alberto Rossi, Gianni Barlacchi, Monica Bianchini, and Bruno Lepri. Modeling taxi drivers' behaviour for the next destination prediction. arXiv preprint arXiv:1807.08173, 2018.
[38] Matthias C Scheffel, Claus G Gruber, Thomas Schwabe, and Robert G Prinz. Optimal multi-topology routing for ip resilience. AEUInternational Journal of Electronics and Communications, 60(1):35-39, 2006.
[39] Filippo Simini, Marta C. Gonzalez, Amos Maritan, and Albert-Laszlo Barabasi. A universal model for mobility and migration patterns. Nature, 484(7392):96-100, 4 2012.
[40] United Nations. World Urbanization Prospects: The 2014 Revision. Technical report, Department of Economic and Social Affairs Population Division, New York, NY, USA, 2014.
[41] Yanyan Xu and Marta C. Gonza´lez. Collective benefits in traffic during mega events via the use of information technologies. Journal of The Royal Society Interface, 14(129), 2017.
[42] George Kingsley Zipf. The p 1 p 2/d hypothesis: on the intercity movement of persons. American sociological review, 11(6):677-686, 1946.
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BibTeX entry
@inproceedings{oai:it.cnr:prodotti:404566,
title = {Weak nodes detection in urban transport systems: planning for resilience in Singapore},
author = {Ferretti M. and Barlacchi G. and Pappalardo L. and Lucchini L. and Lepri B.},
doi = {10.1109/dsaa.2018.00061 and 10.48550/arxiv.1809.07839},
booktitle = {DSAA 2018 - IEEE 5th International Conference on Data Science and Advanced Analytics, pp. 472–480, 01-04 October 2018},
year = {2018}
}CNR authorsLaboratories
0000-0002-1547-6007
