Measuring Road Network Topology Vulnerability by Ricci Curvature

Abstract Describing the basic properties of road network systems, such as their robustness, vulnerability, and reliability, has been a very important research topic in the field of urban transportation. Current research mainly uses several statistical indicators of complex networks to analyze the road network systems. However, these methods are essentially node-based. These node-based methods pay more attention to the number of connections between nodes, and lack of consideration for interactions, leading to the well-known node paradox problem, and their ability of characterizing the local and intrinsic properties of a network is weak. From the perspective of network intrinsic geometry, we propose a method for measuring road network vulnerability using a discrete Ricci curvature, which can identify the key sections of a road network and indicate its fragile elements. The results show that our method performs better than complex network statistics on measuring the vulnerability of a road network. Additionally, it can characterize the evolution of the road network vulnerability among different periods of time in the same city through our method. Finally, we compare our method with the previous method of centrality and show the different between them. This research provides a new perspective on a geometry to analyze the vulnerability of a road network and describes the inherent nature of the vulnerability of a road system from a new perspective. It also contributes to enriching the analytical methods of complex road networks.

[1]  Filippo Santambrogio,et al.  Optimal Transport for Applied Mathematicians , 2015 .

[2]  Ed Reznik,et al.  Graph Curvature for Differentiating Cancer Networks , 2015, Scientific Reports.

[3]  Talel Abdessalem,et al.  Discriminative Distance-Based Network Indices with Application to Link Prediction , 2017, Comput. J..

[4]  Jie Gao,et al.  Ricci curvature of the Internet topology , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[5]  Beom Jun Kim,et al.  Attack vulnerability of complex networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  C. Sander,et al.  Ricci Curvature and Robustness of Cancer Networks , 2015 .

[7]  Alan T. Murray,et al.  Critical network infrastructure analysis: interdiction and system flow , 2007, J. Geogr. Syst..

[8]  David I Blockley,et al.  VULNERABILITY OF SYSTEMS , 2001 .

[9]  S. Mahadevan,et al.  A modified evidential methodology of identifying influential nodes in weighted networks , 2013 .

[10]  C. Sander,et al.  Graph Curvature and the Robustness of Cancer Networks , 2015, 1502.04512.

[11]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[12]  Panagiotis Angeloudis,et al.  Large subway systems as complex networks , 2006 .

[13]  Robert Nowak,et al.  Network Loss Inference Using Unicast End-to-End Measurement , 2000 .

[14]  Katja Berdica,et al.  AN INTRODUCTION TO ROAD VULNERABILITY: WHAT HAS BEEN DONE, IS DONE AND SHOULD BE DONE , 2002 .

[15]  Sara Ballouz,et al.  EGAD: Ultra-fast functional analysis of gene networks , 2016, bioRxiv.

[16]  Peter Nijkamp,et al.  Transport resilience and vulnerability: The role of connectivity , 2015 .

[17]  Robert D. Nowak,et al.  Maximum likelihood network topology identification from edge-based unicast measurements , 2002, SIGMETRICS '02.

[18]  Yong Gao,et al.  Understanding Urban Traffic-Flow Characteristics: A Rethinking of Betweenness Centrality , 2013 .

[19]  Alan T. Murray,et al.  Exploring the vulnerability of network infrastructure to disruption , 2008 .

[20]  Tom Petersen,et al.  Importance and Exposure in Road Network Vulnerability Analysis , 2006 .

[21]  Arindam Banerjee,et al.  Bregman Alternating Direction Method of Multipliers , 2013, NIPS.

[22]  Erik Jenelius,et al.  Beyond a complete failure: the impact of partial capacity degradation on public transport network vulnerability , 2018 .

[23]  Sybil Derrible,et al.  The complexity and robustness of metro networks , 2010 .

[24]  Peter Morters,et al.  Robustness of scale-free spatial networks , 2015, 1504.00618.

[25]  Erik Jenelius,et al.  Road network vulnerability analysis: Conceptualization, implementation and application , 2015, Comput. Environ. Urban Syst..

[26]  Y. Ollivier Ricci curvature of Markov chains on metric spaces , 2007, math/0701886.

[27]  Marco Cuturi,et al.  Sinkhorn Distances: Lightspeed Computation of Optimal Transport , 2013, NIPS.

[28]  Yicheng Zhang,et al.  Identifying influential nodes in complex networks , 2012 .

[29]  Peter Donnelly,et al.  Superfamilies of Evolved and Designed Networks , 2004 .

[30]  Albert-László Barabási,et al.  Error and attack tolerance of complex networks , 2000, Nature.

[31]  Bin Jiang,et al.  Self-organized natural roads for predicting traffic flow: a sensitivity study , 2008, 0804.1630.

[32]  Duanbing Chen,et al.  Vital nodes identification in complex networks , 2016, ArXiv.

[33]  Jianxi Gao,et al.  A mobility network approach to identify and anticipate large crowd gatherings , 2018, Transportation Research Part B: Methodological.

[34]  Fan Zhang,et al.  Understanding coupling dynamics of public transportation networks , 2018, EPJ Data Science.

[35]  M A P Taylor,et al.  Network Vulnerability: An Approach to Reliability Analysis at the Level of National Strategic Transport Networks , 2003 .

[36]  Emil Saucan,et al.  Characterizing complex networks with Forman-Ricci curvature and associated geometric flows , 2016, J. Complex Networks.

[37]  B. Slack,et al.  The Geography of Transport Systems , 2006 .

[38]  Talel Abdessalem,et al.  Discriminative Distance-Based Network Indices and the Tiny-World Property , 2017, ArXiv.

[39]  F. Santambrogio Optimal Transport for Applied Mathematicians: Calculus of Variations, PDEs, and Modeling , 2015 .

[40]  Juan A. Carrasco,et al.  Vulnerability of nodes under controlled network topology and flow autocorrelation conditions , 2017 .

[41]  Hui Gao,et al.  Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering , 2013, PloS one.

[42]  V. Palchykov,et al.  Public transport networks: empirical analysis and modeling , 2008, 0803.3514.

[43]  Erik Jenelius,et al.  Vulnerability and resilience of transport systems : A discussion of recent research , 2015 .

[44]  R. Solé,et al.  Information Theory of Complex Networks: On Evolution and Architectural Constraints , 2004 .

[45]  A. Tannenbaum,et al.  Ricci curvature: An economic indicator for market fragility and systemic risk , 2016, Science Advances.

[46]  Y. Ollivier Ricci curvature of metric spaces , 2007 .

[47]  Feng Lu,et al.  Robustness of city road networks at different granularities , 2014 .

[48]  Kirsi Virrantaus,et al.  Identifying Critical Locations in a Spatial Network with Graph Theory , 2008, Trans. GIS.