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                <term xml:lang="en">APPROXIMATION ALGORITHMS</term>
                <term xml:lang="en">PETROLEUM RESERVOIR EVALUATION</term>
                <term xml:lang="en">COMPLEX NETWORKS</term>
                <term xml:lang="en">GRAPHIC METHODS</term>
                <term xml:lang="en">MOTOR TRANSPORTATION</term>
                <term xml:lang="en">BIG DATA</term>
                <term xml:lang="en">VULNERABILITY ASSESSMENTS</term>
                <term xml:lang="en">SMART TRANSPORTATIONS</term>
                <term xml:lang="en">ROADS AND STREETS</term>
                <term xml:lang="en">BETWEENNESS CENTRALITY</term>
                <term xml:lang="en">CONTINGENCY ANALYSIS</term>
                <term xml:lang="en">NETWORK RESILIENCE</term>
                <term xml:lang="en">TRANSPORTATION NETWORK</term>
                <term xml:lang="en">OPERATIONAL MONITORING</term>
                <term xml:lang="en">GRAPH-BASED MODELING</term>
                <term xml:lang="fr">TRAITEMENT EN TEMPS REEL</term>
                <term xml:lang="fr">RESEAU ROUTIER</term>
                <term xml:lang="fr">ROUTE</term>
                <term xml:lang="fr">REPRESENTATION GRAPHIQUE</term>
                <term xml:lang="fr">MODELISATION</term>
                <term xml:lang="fr">RESILIENCE</term>
                <term xml:lang="fr">SURVEILLANCE</term>
                <term xml:lang="fr">SYSTEME DE TRANSPORT INTELLIGENT</term>
                <term xml:lang="fr">RESEAU DE TRANSPORT</term>
                <term xml:lang="fr">VULNERABILITE</term>
                <term xml:lang="fr">EVALUATION</term>
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              <p>The ability to detect critical spots in transportation networks is fundamental to improve traffic operations and road-network resilience in smart cities. Real-Time monitoring of these networks, especially in very large metropolitan areas, is a compelling challenge due to the complexity of computing robustness metrics. This paper presents a framework for identifying vulnerabilities in very-large road networks. The framework adopts graph-based modeling of road networks and exploits big-data techniques and technologies for processing such large and complex graphs. First, we use the framework to prove the existence of a significant correlation between global efficiency and betweenness centrality. Then, we focus on an efficient algorithm, integrated in the framework, to rank the nodes according to this metric for finding potential vulnerabilities of a road network. To keep computation time under a 'quasi' real-Time threshold, a fast, requirement-driven, approximated strategy for computing betweenness centrality is adopted. The evaluation shows that the algorithm, integrated in the framework, exhibits a very good approximation for the most critical nodes, thus being well-suited for on-line operational monitoring.</p>
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