Quantify Resilience Enhancement of UTS through Exploiting Connected Community and Internet of Everything Emerging Technologies

This work aims at investigating and quantifying the Urban Transport System (UTS) resilience enhancement enabled by the adoption of emerging technology such as Internet of Everything (IoE) and the new trend of the Connected Community (CC). A conceptual extension of Functional Resonance Analysis Method (FRAM) and its formalization have been proposed and used to model UTS complexity. The scope is to identify the system functions and their interdependencies with a particular focus on those that have a relation and impact on people and communities. Network analysis techniques have been applied to the FRAM model to identify and estimate the most critical community-related functions. The notion of Variability Rate (VR) has been defined as the amount of output variability generated by an upstream function that can be tolerated/absorbed by a downstream function, without significantly increasing of its subsequent output variability. A fuzzy-based quantification of the VR based on expert judgment has been developed when quantitative data are not available. Our approach has been applied to a critical scenario as flash flooding considering two cases: when UTS has CC and IoE implemented or not. However, the method can be applied in different scenarios and critical infrastructures. The results show a remarkable VR enhancement if CC and IoE are deployed.

[1]  Ronald R. Yager,et al.  Modeling the Concept of Fuzzy Majority , 2003, IFSA.

[2]  Danny Holten,et al.  Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data , 2006, IEEE Transactions on Visualization and Computer Graphics.

[3]  D. Chandler.  The Rise of Resilience , 2017 .

[4]  Louise K. Comfort,et al.  Designing resilience : preparing for extreme events , 2010 .

[5]  Cristina Dutra de Aguiar Ciferri,et al.  Cube Algebra: A Generic User-Centric Model and Query Language for OLAP Cubes , 2013, Int. J. Data Warehous. Min..

[6]  U. Brandes.  A faster algorithm for betweenness centrality , 2001 .

[7]  Tom A. B. Snijders,et al.  Social Network Analysis , 2011, International Encyclopedia of Statistical Science.

[8]  Pierfrancesco Bellini,et al.  Km4City ontology building vs data harvesting and cleaning for smart-city services , 2014, J. Vis. Lang. Comput..

[9]  Barry W. Johnson,et al.  Dependability metrics to assess safety-critical systems , 2005, IEEE Transactions on Reliability.

[10]  Ernesto Damiani,et al.  Adding a Peer-to-Peer Trust Layer to Metadata Generators , 2005, OTM Workshops.

[11]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[12]  Paolo Nesi,et al.  Towards resilience operationalization in urban transport system: The RESOLUTE project approach , 2017 .

[13]  I. Linkov,et al.  Changing the resilience paradigm , 2014 .

[14]  T. Stocker,et al.  Managing the risks of extreme events and disasters to advance climate change adaptation. Special report of the Intergovernmental Panel on Climate Change. , 2012 .

[15]  Siegfried Gottwald,et al.  Fuzzy Sets and Fuzzy Logic , 1993 .

[16]  Erik Hollnagel,et al.  Disaster Management, Control, and Resilience , 2015 .

[17]  Nancy G. Leveson,et al.  A new accident model for engineering safer systems , 2004 .

[18]  George J. Klir,et al.  Fuzzy sets and fuzzy logic , 1995 .

[19]  Piet Van Mieghem,et al.  Classification of graph metrics , 2015 .

[20]  Martin H. Weik D.Sc..  Computer Science and Communications Dictionary , 2001, Springer US.

[21]  José A. Pino,et al.  Improving the support to decision making in medium-sized urban emergencies , 2013, Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[22]  Stephen P. Borgatti,et al.  Centrality and network flow , 2005, Soc. Networks.

[23]  I. Linkov,et al.  Integrating Risk and Resilience Approaches to Catastrophe Management in Engineering Systems , 2013, Risk analysis : an official publication of the Society for Risk Analysis.

[24]  BelliniEmanuele,et al.  Quantify Resilience Enhancement of UTS through Exploiting Connected Community and Internet of Everything Emerging Technologies , 2017 .

[25]  G. McCarthy.  FRAM: the functional resonance analysis method, modelling complex socio-technical systems , 2013 .

[26]  E. Hollnagel.  FRAM: The Functional Resonance Analysis Method: Modelling Complex Socio-technical Systems , 2012 .

[27]  Francisco Herrera,et al.  Aggregation operators for linguistic weighted information , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[28]  Witold Pedrycz,et al.  A review of soft consensus models in a fuzzy environment , 2014, Inf. Fusion.

[29]  PedryczWitold,et al.  A review of soft consensus models in a fuzzy environment , 2014 .

[30]  T. Stocker,et al.  Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of IPCC Intergovernmental Panel on Climate Change , 2012 .

[31]  Pierfrancesco Bellini,et al.  Analysis and assessment of a knowledge based smart city architecture providing service APIs , 2017, Future Gener. Comput. Syst..

[32]  Stefan Thalmann,et al.  An Integrated Risk Management Framework: Measuring the Success of Organizational Knowledge Protection , 2014, Int. J. Knowl. Manag..

[33]  Scott Jackson,et al.  Architecting Resilient Systems: Accident Avoidance and Survival and Recovery from Disruptions , 2008 .

[34]  Ernesto Damiani,et al.  Bottom-Up Extraction and Trust-Based Refinement of Ontology Metadata , 2007, IEEE Transactions on Knowledge and Data Engineering.

[35]  N. Leveson,et al.  A Comparative Look at MBU Hazard Analysis Techniques , 2006 .

[36]  Erik Hollnagel,et al.  Resilience Engineering in Practice: A Guidebook , 2012 .

[37]  Dimitrios Tzovaras,et al.  A Decision Support System for Real-Time Evacuation Management and Rescue Team Planning during Hazardous Events in Public Infrastructures , 2012, TST.

[38]  Julia Pitsopoulos,et al.  Understanding safety and production risks in rail engineering planning and protection , 2009, Ergonomics.

[39]  Alistair Barros,et al.  Business Processes in Connected Communities , 2014, Business Process Management Workshops.

[40]  Pierfrancesco Bellini,et al.  A Smart Decision Support System for Smart City , 2015, 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity).

[41]  David S. Ebert,et al.  Visualization and computer graphics , 2007 .

[42]  Pierfrancesco Bellini,et al.  AP Positioning for Estimating People Flow as Origin Destination Matrix for Smart Cities , 2016, DMS.

[43]  Rogier Woltjer,et al.  Resilience assessment based on models of functional resonance , 2008 .

[44]  Ernesto Damiani,et al.  A toward Framework for Generic Uncertainty Management , 2009, IFSA/EUSFLAT Conf..

[45]  Ciro Cattuto,et al.  Evaluating similarity measures for emergent semantics of social tagging , 2009, WWW '09.

[46]  Murali Raman,et al.  Knowledge management systems in support of disasters management: A two decade review , 2013, Technological Forecasting and Social Change.