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Infrastructure Resilience Conference 2018

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Investigation of Similar Patterns in Urban Network Collapses

Urban transportation networks may be exposed to errors (heavy congestion, infrastructure ‎failure) and attacks (terrorist attacks). From the equilibrium perspective, noticeable surge in ‎demand or drop in supply may cause breakdowns in an urban transportation network. Due to the ‎importance of urban transportation networks, identification of their behavior towards ‎perturbations and fortifying them are of utmost importance.‎ In this paper, we aim to address two questions. The first question is: Are there similar patterns ‎among different cities throughout the world in the collapse of urban networks as the result of ‎attacks? The second question is: Among various node importance measures, which best reveals ‎the critical nodes of an urban network.‎ Urban transportation networks of seven cities including Philadelphia, Chicago, Austin, Berlin, ‎Isfahan, Gold Coast, and Birmingham are abstracted as graphs by adopting the primal approach ‎i.e. nodes represent intersections and edges represent highways and streets. Degree, betweenness, ‎weighted degree with weights being the capacity of incident streets, and a combination of degree ‎and weighted degree of nodes are calculated. The nodes are sequenced based on these criteria ‎and in each stage they are eliminated based on these orders. Moreover, in one stage the nodes are ‎eliminated randomly to represent random failures. In each stage, after elimination of one percent ‎of nodes, the relative size of the giant component is calculated. The nodes whose elimination ‎have dire effects on the performance of a network and lead it to plunge are considered as critical ‎nodes. ‎ It was found that the behavior of investigated urban networks follow an inverse sigmoid pattern. ‎The process of network collapse can be divided to three distinct parts; in the first part for loss of ‎‎15% of nodes, the slope of collapse of a network is mild, then it experiences a severe slope, and ‎finally by reaching loss of 35% ends to a mild slope when network is approximately devastated ‎and collapsed. Removal of approximately 35% of nodes results in diminishing the giant ‎component to about 20% of the initial network. Results show that in the case of minor ‎disruptions, degree of nodes best identifies the critical nodes. ‎ As the main disruptions of urban networks cause minor node removals, nodes with higher ‎degrees are the most critical. This implies that intersections with all two directional approaches ‎are the most critical urban infrastructures.‎

Meisam Akbarzadeh
Isfahan University of Technology
Iran

Soroush Memarmontazerin
Isfahan University of Technology
Iran

 

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