How Topology Changes Under Disruptive Conditions Affect Transportation System Resilience A Case Study of Colombo
The transportation system is a critical infrastructure with profound impacts on the economy and social well-being. Therefore, understanding and enhancing transportation system resilience in the face of disruptions is of utmost importance. This study presents a framework that utilizes critical-link a...
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| Published in | Journal of the Eastern Asia Society for Transportation Studies Vol. 15; pp. 435 - 454 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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Eastern Asia Society for Transportation Studies
2024
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| Abstract | The transportation system is a critical infrastructure with profound impacts on the economy and social well-being. Therefore, understanding and enhancing transportation system resilience in the face of disruptions is of utmost importance. This study presents a framework that utilizes critical-link attacks to disrupt transportation network segments, evaluating their resilience. By utilizing network topological parameters as proxies, the framework holistically captures the network's resilience, considering all potential individual road segment disruptions. In contrast to conventional studies focusing on pre-hazard occurrences, this framework incorporates worst-case scenarios, encompassing all potential individual disruptions. Statistical and spatial analysis techniques are employed to assess transportation network resilience in both macro and micro events, employing topological parameters to evaluate network changes under varying levels of disruption. The results demonstrate the effectiveness of topological parameters in capturing transportation system resilience, enabling the identification of critical road segments from structural and network perspectives. These findings contribute to evaluating network performance and ensuring optimal transportation serviceability. |
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| AbstractList | The transportation system is a critical infrastructure with profound impacts on the economy and social well-being. Therefore, understanding and enhancing transportation system resilience in the face of disruptions is of utmost importance. This study presents a framework that utilizes critical-link attacks to disrupt transportation network segments, evaluating their resilience. By utilizing network topological parameters as proxies, the framework holistically captures the network's resilience, considering all potential individual road segment disruptions. In contrast to conventional studies focusing on pre-hazard occurrences, this framework incorporates worst-case scenarios, encompassing all potential individual disruptions. Statistical and spatial analysis techniques are employed to assess transportation network resilience in both macro and micro events, employing topological parameters to evaluate network changes under varying levels of disruption. The results demonstrate the effectiveness of topological parameters in capturing transportation system resilience, enabling the identification of critical road segments from structural and network perspectives. These findings contribute to evaluating network performance and ensuring optimal transportation serviceability. |
| Author | KATO, Teppei LDCHN, Kalpana SANO, Kazushi |
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| Copyright | 2024 Eastern Asia Society for Transportation Studies |
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| References | Jiang, B., & Liu, C. (2009). Street-based topological representations and analyses for predicting traffic flow in GIS. International Journal of Geographical Information Science, 23(9), 1119-1137. Ma, D., Guo, R., Zheng, Y., Zhao, Z., He, F., & Zhu, W. (2020). Understanding Chinese urban form: The universal fractal pattern of street networks over 298 cities. ISPRS International Journal of Geo-Information, 9(4), 192. Jiang, B., & Claramunt, C. (2004). Topological analysis of urban street networks. Environment and Planning B: Planning and design, 31(1), 151-162. Tsiotas, D., & Polyzos, S. (2017). The topology of urban road networks and its role to urban mobility. Transportation research procedia, 24, 482-490. Jiang, B., & Claramunt, C. (2004). A structural approach to the model generalization of an urban street network. GeoInformatica, 8, 157-171. Taylor, M. A. (2008). Critical transport infrastructure in Urban areas: impacts of traffic incidents assessed using accessibility-based network vulnerability analysis. Growth and Change, 39(4), 593-616. Presto, K., Belden, T., & Lavarato, J. (2019). Visualizing social networks. Cambridge: Cambridge Intelligence. Retrieved from https://info.cambridge-intelligence.com/social-networks-white-paper Batty, M., & Longley, P. A. (1994). ractal cities: a geometry of form and function. Academic press. Jayasinghe, A., Sano, K., Abenayake, C. C., & Mahanama, P. K. (2019). A novel approach to model traffic on road segments of large-scale urban road networks. MethodsX, 6, 1147-1163. Franceschet, M. (2011). PageRank: Standing on the shoulders of giants. Communications of the ACM, 54(6), 92-101. Newman, M. (2010). Networks: an Introduction. New York, USA : Oxford University Press. Porta, S., Crucitti, P., & Latora, V. S. (2006). The network analysis of urban streets: A dual approach. Physica A: Statistical Mechanics and its Applications, 369(2), 853-866. Zhang, X., Miller-Hooks, E., & Denny, K. (2015). Assessing the role of network topology in transportation network resilience. Journal of transport geography, 46, 35-45. Derrible, S. (2017). Complexity in future cities: the rise of networked infrastructure. International Journal of Urban Sciences, 68-86. Taylor, M. A., & D’Este, G. M. (2007). Transport network vulnerability: a method for diagnosis of critical locations in transport infrastructure systems. Critical infrastructure. Heidelberg: Springer, Berlin. Derrible, S., & Kennedy, C. (2010). The complexity and robustness of metro networks. Physica A: Statistical Mechanics and its Applications, 389(17), 3678-3691. Thomson, R. C. (2003). Bending the axial line: smoothly continuous road centre-line segments as a basis for road. 4th International Space Syntax Symposium. London: University College London. Barabási, A. L. (2003). Linked: The New Science of Networks. American Journal of Physics, 71, 409–410. Gao, L., Liu, X., Liu, Y., Wang, P., Deng, M., Zhu, Q., & Li, H. (2019). Measuring road network topology vulnerability by Ricci curvature. Physica A: Statistical Mechanics and its Applications, 527, 121071. Jiang, B., & Claramunt, C. (2002). Integration of space syntax into GIS: new perspectives for urban morphology. Transactions in GIS, 6(3), 295-309. Tu, Y., Yang, C., & Chen, X. (2013). Road network topology vulnerability analysis and application. In Proceedings of the Institution of Civil Engineers-Transport. 166, pp. 95-104. Thomas Telford Ltd. Porta, S., Crucitti, P., & Latora, V. (2006). The network analysis of urban streets: a primal approach. Environment and Planning B: planning and design, 33(5), 705-725. Kermanshah, A., & Derrible, S. (2016). A geographical and multi-criteria vulnerability assessment of transportation networks against extreme earthquakes. Reliability Engineering & System Safety, 153, 39-49. Zhou, Y., Wang, J., & Yang, H. (2019). Resilience of transportation systems: concepts and comprehensive review. IEEE Transactions on Intelligent Transportation Systems, 20(12), 4262-4276. Jiang, B., & Huang, J. T. (2021). A new approach to detecting and designing living structure of urban environments. Computers, Environment and Urban Systems, 88, 101646. Turner, A. (2007). From axial to road-centre lines: a new representation for space syntax and a new model of route choice for transport network analysis. Environment and Planning B: planning and Design, 34(3), 539-555. Jiang, B., Claramunt, C., & Klarqvist, B. (2000). Integration of space syntax into GIS for modelling urban spaces. International Journal of Applied Earth Observation and Geoinformation, 2((3-4)), 161-171. Ma, D., Omer, I., Osaragi, T., Sandberg, M., & Jiang, B. (2019). Why topology matters in predicting human activities. Environment and Planning B: Urban Analytics and City Science, 46(7), 1297-1313. Casali, Y., & Heinimann, H. R. (2019). A topological characterization of flooding impacts on the Zurich road network. PLoS one, 14(7), e0220338. Croope, S. V., & McNeil, S. (2011). Improving resilience of critical infrastructure systems post disaster: recovery and mitigation. Transportation Research Record, 2234(1), 3-13. Hanna, S. (2021). Random walks in urban graphs: A minimal model of movement. Environment and Planning B: Urban Analytics and City Science, 48(6), 1697-1711. Jiang, B., Zhao, S., & Yin, J. (2008). Self-organized natural roads for predicting traffic flow: a sensitivity study. Journal of statistical mechanics: Theory and experiment, 2008(7), P07008. Lowry, M. (2014). Spatial interpolation of traffic counts based on origin–destination centrality. . Journal of Transport Geography, 36, 98-105. Taylor, M. (2017). Vulnerability analysis for transportation networks. Elsevier. Kenny, C., & Derrible, S. (2011). Applications of Graph Theory and Network Science to Transit Network Design. Transport Reviews: A Transnational Transdisciplinary Journal, 31(4), 495-519. Lin, J., & Ban, Y. (2013). Complex network topology of transportation systems. Transport reviews, 33(6), 658-685. Porta, S., Latora, V., Wang, F., Rueda, S., Strano, E., Scellato, S., . . . Latora, L. (2012). Street centrality and the location of economic activities in Barcelona. Urban Studies, 49(7), 1471-1488. Jiang, B., Yin, J., & Zhao, S. (2009). Characterizing the human mobility pattern in a large street network. Physical Review E, 80(2), 021136. Jenelius, E. (2010). Redundancy importance: Links as rerouting alternatives during road network disruptions. Procedia Engineering, 3, 129-137. Black, W. R. (2003). Transportation: a geographical analysis. Guilford Press. Hillier, B. (1996). Space is the Machine: A Configurational Theory of Architecture. Cambridge: Cambridge University Press. Ma, D., Osaragi, T., Oki, T., & Jiang, B. (2020). Exploring the heterogeneity of human urban movements using geo-tagged tweets. International Journal of Geographical Information Science, 34(12), 2475-2496. |
| References_xml | – reference: Jiang, B., & Liu, C. (2009). Street-based topological representations and analyses for predicting traffic flow in GIS. International Journal of Geographical Information Science, 23(9), 1119-1137. – reference: Batty, M., & Longley, P. A. (1994). ractal cities: a geometry of form and function. Academic press. – reference: Porta, S., Crucitti, P., & Latora, V. (2006). The network analysis of urban streets: a primal approach. Environment and Planning B: planning and design, 33(5), 705-725. – reference: Jiang, B., & Claramunt, C. (2002). Integration of space syntax into GIS: new perspectives for urban morphology. Transactions in GIS, 6(3), 295-309. – reference: Jiang, B., Zhao, S., & Yin, J. (2008). Self-organized natural roads for predicting traffic flow: a sensitivity study. Journal of statistical mechanics: Theory and experiment, 2008(7), P07008. – reference: Kermanshah, A., & Derrible, S. (2016). A geographical and multi-criteria vulnerability assessment of transportation networks against extreme earthquakes. Reliability Engineering & System Safety, 153, 39-49. – reference: Jiang, B., Claramunt, C., & Klarqvist, B. (2000). Integration of space syntax into GIS for modelling urban spaces. International Journal of Applied Earth Observation and Geoinformation, 2((3-4)), 161-171. – reference: Croope, S. V., & McNeil, S. (2011). Improving resilience of critical infrastructure systems post disaster: recovery and mitigation. Transportation Research Record, 2234(1), 3-13. – reference: Derrible, S. (2017). Complexity in future cities: the rise of networked infrastructure. International Journal of Urban Sciences, 68-86. – reference: Jayasinghe, A., Sano, K., Abenayake, C. C., & Mahanama, P. K. (2019). A novel approach to model traffic on road segments of large-scale urban road networks. MethodsX, 6, 1147-1163. – reference: Ma, D., Omer, I., Osaragi, T., Sandberg, M., & Jiang, B. (2019). Why topology matters in predicting human activities. Environment and Planning B: Urban Analytics and City Science, 46(7), 1297-1313. – reference: Porta, S., Latora, V., Wang, F., Rueda, S., Strano, E., Scellato, S., . . . Latora, L. (2012). Street centrality and the location of economic activities in Barcelona. Urban Studies, 49(7), 1471-1488. – reference: Zhou, Y., Wang, J., & Yang, H. (2019). Resilience of transportation systems: concepts and comprehensive review. IEEE Transactions on Intelligent Transportation Systems, 20(12), 4262-4276. – reference: Black, W. R. (2003). Transportation: a geographical analysis. Guilford Press. – reference: Thomson, R. C. (2003). Bending the axial line: smoothly continuous road centre-line segments as a basis for road. 4th International Space Syntax Symposium. London: University College London. – reference: Franceschet, M. (2011). PageRank: Standing on the shoulders of giants. Communications of the ACM, 54(6), 92-101. – reference: Zhang, X., Miller-Hooks, E., & Denny, K. (2015). Assessing the role of network topology in transportation network resilience. Journal of transport geography, 46, 35-45. – reference: Tsiotas, D., & Polyzos, S. (2017). The topology of urban road networks and its role to urban mobility. Transportation research procedia, 24, 482-490. – reference: Ma, D., Guo, R., Zheng, Y., Zhao, Z., He, F., & Zhu, W. (2020). Understanding Chinese urban form: The universal fractal pattern of street networks over 298 cities. ISPRS International Journal of Geo-Information, 9(4), 192. – reference: Jenelius, E. (2010). Redundancy importance: Links as rerouting alternatives during road network disruptions. Procedia Engineering, 3, 129-137. – reference: Casali, Y., & Heinimann, H. R. (2019). A topological characterization of flooding impacts on the Zurich road network. PLoS one, 14(7), e0220338. – reference: Kenny, C., & Derrible, S. (2011). Applications of Graph Theory and Network Science to Transit Network Design. Transport Reviews: A Transnational Transdisciplinary Journal, 31(4), 495-519. – reference: Jiang, B., & Claramunt, C. (2004). A structural approach to the model generalization of an urban street network. GeoInformatica, 8, 157-171. – reference: Jiang, B., & Claramunt, C. (2004). Topological analysis of urban street networks. Environment and Planning B: Planning and design, 31(1), 151-162. – reference: Taylor, M. A., & D’Este, G. M. (2007). Transport network vulnerability: a method for diagnosis of critical locations in transport infrastructure systems. Critical infrastructure. Heidelberg: Springer, Berlin. – reference: Derrible, S., & Kennedy, C. (2010). The complexity and robustness of metro networks. Physica A: Statistical Mechanics and its Applications, 389(17), 3678-3691. – reference: Presto, K., Belden, T., & Lavarato, J. (2019). Visualizing social networks. Cambridge: Cambridge Intelligence. Retrieved from https://info.cambridge-intelligence.com/social-networks-white-paper – reference: Hillier, B. (1996). Space is the Machine: A Configurational Theory of Architecture. Cambridge: Cambridge University Press. – reference: Jiang, B., & Huang, J. T. (2021). A new approach to detecting and designing living structure of urban environments. Computers, Environment and Urban Systems, 88, 101646. – reference: Lowry, M. (2014). Spatial interpolation of traffic counts based on origin–destination centrality. . Journal of Transport Geography, 36, 98-105. – reference: Porta, S., Crucitti, P., & Latora, V. S. (2006). The network analysis of urban streets: A dual approach. Physica A: Statistical Mechanics and its Applications, 369(2), 853-866. – reference: Hanna, S. (2021). Random walks in urban graphs: A minimal model of movement. Environment and Planning B: Urban Analytics and City Science, 48(6), 1697-1711. – reference: Tu, Y., Yang, C., & Chen, X. (2013). Road network topology vulnerability analysis and application. In Proceedings of the Institution of Civil Engineers-Transport. 166, pp. 95-104. Thomas Telford Ltd. – reference: Lin, J., & Ban, Y. (2013). Complex network topology of transportation systems. Transport reviews, 33(6), 658-685. – reference: Barabási, A. L. (2003). Linked: The New Science of Networks. American Journal of Physics, 71, 409–410. – reference: Taylor, M. A. (2008). Critical transport infrastructure in Urban areas: impacts of traffic incidents assessed using accessibility-based network vulnerability analysis. Growth and Change, 39(4), 593-616. – reference: Newman, M. (2010). Networks: an Introduction. New York, USA : Oxford University Press. – reference: Turner, A. (2007). From axial to road-centre lines: a new representation for space syntax and a new model of route choice for transport network analysis. Environment and Planning B: planning and Design, 34(3), 539-555. – reference: Taylor, M. (2017). Vulnerability analysis for transportation networks. Elsevier. – reference: Gao, L., Liu, X., Liu, Y., Wang, P., Deng, M., Zhu, Q., & Li, H. (2019). Measuring road network topology vulnerability by Ricci curvature. Physica A: Statistical Mechanics and its Applications, 527, 121071. – reference: Ma, D., Osaragi, T., Oki, T., & Jiang, B. (2020). Exploring the heterogeneity of human urban movements using geo-tagged tweets. International Journal of Geographical Information Science, 34(12), 2475-2496. – reference: Jiang, B., Yin, J., & Zhao, S. (2009). Characterizing the human mobility pattern in a large street network. Physical Review E, 80(2), 021136. |
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| Title | How Topology Changes Under Disruptive Conditions Affect Transportation System Resilience A Case Study of Colombo |
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