A Multi-Criteria Vulnerability of Urban Transportation Systems Analysis Against Earthquake Considering Topological and Geographical Method: A Case Study

The purpose of this article is to develop a method for street network vulnerability using topology analysis and fuzzy multi-criteria decision-making (FMCDM) analysis. In this regard, a deterministic approach based on the worst-case earthquake scenario employed in the central area of Tehran. For this purpose, two main criteria are considered to study the vulnerability of the urban street network. One of them represents the vulnerability due to the interaction of collapsed buildings and urban roads, and the second one indicates the relevant street links with the potential for traffic attraction in terms of the betweenness centrality metric. The first criterion was processed using FMCDM, considering earthquake intensity, a ratio of road-side building height to road width, and type of building. The latter was measured using network centrality analysis representing the built environment in ArcGIS. The final vulnerability index is obtained by combining these criteria. The proposed method is relevant and straightforward, which can identify the critical links and assess the seismic vulnerability of the road networks. The technique enables disaster planners and managers to improve the robustness of the road network and assign the resources at regional and district scales.

[1]  A. Goretti,et al.  Road Network and Damaged Buildings in Urban Areas: Short and Long-term Interaction , 2006 .

[2]  Tessa K Anderson,et al.  Kernel density estimation and K-means clustering to profile road accident hotspots. , 2009, Accident; analysis and prevention.

[3]  Tai,et al.  Urban Disaster Prevention Shelter VulnerabilityEvaluation Considering Road Network Characteristics , 2013 .

[4]  Andres Sevtsuk,et al.  Urban network analysis. A new toolbox for ArcGIS , 2012, Rev. Int. Géomatique.

[5]  Michael G.H. Bell,et al.  Transportation Network Analysis: Bell/Transportation Network Analysis , 1997 .

[6]  Kyriazis Pitilakis,et al.  Systemic Seismic Risk Assessment of Road Networks Considering Interactions with the Built Environment , 2015, Comput. Aided Civ. Infrastructure Eng..

[7]  Kyriazis Pitilakis,et al.  Seismic risk performance of urban transportation systems considering site effects and interaction with the built environment , 2011 .

[8]  Lan Liu,et al.  Game Approach to Vulnerability Analysis of Evacuation Highway Networks , 2019, Journal of Transportation Engineering, Part A: Systems.

[9]  Ferdinando Di Martino,et al.  A New Geospatial Model Integrating a Fuzzy Rule-Based System in a GIS Platform to Partition a Complex Urban System in Homogeneous Urban Contexts , 2018, Geosciences.

[10]  Rawia Ahmed El-Rashidy,et al.  An Assessment Method for Highway Network Vulnerability , 2014 .

[11]  Zhi Xiao,et al.  The trapezoidal fuzzy soft set and its application in MCDM , 2012 .

[12]  Chandra Balijepalli,et al.  Measuring vulnerability of road network considering the extent of serviceability of critical road links in urban areas , 2014 .

[13]  Elise Miller-Hooks,et al.  Measuring the performance of transportation infrastructure systems in disasters: a comprehensive review , 2015 .

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

[15]  A. Rastegar ASSESSING URBAN STREETS NETWORK VULNERABILITY AGAINST EARTHQUAKE USING GIS – CASE STUDY: 6 TH ZONE OF TEHRAN , 2017 .

[16]  Lei Gao,et al.  Measuring Road Network Topology Vulnerability by Ricci Curvature , 2018, Physica A: Statistical Mechanics and its Applications.

[17]  Kyriazis Pitilakis,et al.  Systemic seismic risk assessment of road networks in urban areas , 2015 .

[18]  Y Iida,et al.  Transportation Network Analysis , 1997 .

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

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

[21]  Matthew G. Karlaftis,et al.  Transportation network post-disaster planning and management: a review part I: post-disaster transportation network performance , 2014 .

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

[23]  Michael A. P. Taylor,et al.  Public Transport Networks , 2017 .

[24]  Najmeh Neysani Samany,et al.  Seismic vulnerability assessment of urban buildings and traffic networks using fuzzy ordered weighted average , 2019, Journal of Mountain Science.

[25]  Shuping Huang,et al.  Assessing seismic vulnerability of urban road networks by a Bayesian network approach , 2019 .

[26]  A. Alesheikh,et al.  Spatial Modelling of Urban Physical Vulnerability to Explosion Hazards Using GIS and Fuzzy MCDA , 2017 .

[27]  J. Bates,et al.  The valuation of reliability for personal travel , 2001 .

[28]  Geng Lin,et al.  The location of retail stores and street centrality in Guangzhou, China , 2018, Applied Geography.

[29]  Hong Zhang,et al.  An Integrative Vulnerability Evaluation Model to Urban Road Complex Network , 2019, Wirel. Pers. Commun..

[30]  Seyed Hossein Razavi Hajiagha,et al.  Fuzzy belief structure based VIKOR method: an application for ranking delay causes of Tehran metro system by FMEA criteria , 2016 .

[31]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[32]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[33]  Anders Karlström,et al.  The value of reliability , 2007 .

[34]  Michael A. P. Taylor,et al.  Remoteness and accessibility in the vulnerability analysis of regional road networks , 2012 .

[35]  B. Silverman Density estimation for statistics and data analysis , 1986 .

[36]  Eduardo Miranda,et al.  Significance of residual drifts in building earthquake loss estimation , 2012 .

[37]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Man Mach. Stud..

[38]  Sybil Derrible,et al.  A geographical and multi-criteria vulnerability assessment of transportation networks against extreme earthquakes , 2016, Reliab. Eng. Syst. Saf..

[39]  Elise Miller-Hooks,et al.  Resilience: An Indicator of Recovery Capability in Intermodal Freight Transport , 2012, Transp. Sci..

[40]  Zhe-ming Lu,et al.  Using mapping entropy to identify node centrality in complex networks , 2016 .

[41]  Yasuo Asakura,et al.  Anti-seismic reinforcement strategy for an urban road network , 2012 .

[42]  Michael A. P. Taylor Vulnerability Analysis for Transportation Networks , 2017 .

[43]  Luis Magdalena,et al.  Expert guided integration of induced knowledge into a fuzzy knowledge base , 2006, Soft Comput..

[44]  Paolo Zampieri,et al.  Post-quake urban road network functionality assessment for seismic emergency management in historical centres , 2017 .

[45]  Biswajeet Pradhan,et al.  An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm , 2012, Comput. Geosci..

[46]  Mert Kompil,et al.  A framework to analyze the vulnerability of European road networks due to Sea-Level Rise (SLR) and sea storm surges , 2015 .

[47]  Mohsen Babaei,et al.  Emergency transportation network design problem: Identification and evaluation of disaster response routes , 2018 .

[48]  Kirsi Virrantaus,et al.  A fuzzy multiple-attribute decision-making modelling for vulnerability analysis on the basis of population information for disaster management , 2014, Int. J. Geogr. Inf. Sci..

[49]  Kazushi Sano,et al.  Application for developing countries: Estimating trip attraction in urban zones based on centrality , 2017 .

[50]  Balasem Salem Sumait,et al.  Comparison between the Effects of Different Types of Membership Functions on Fuzzy Logic Controller Performance , 2015 .