Understanding road network dynamics: Link-based topological patterns



Road network interruptions caused by natural disasters are becoming more frequent and their consequences are becoming of a wider range. The main goal of this work is to identify the most important roads in a network. Herein, a new model is proposed to evaluate the most important roads in the network through the application of biclustering technique, identifying patterns of attributes (road performance measures) and patterns of roads (connectivity patterns). Thereafter the model presented here is compared with the mean geodesic distance variation. Both methodologies are applied to a case study and the pros and cons are discussed as well. Results point out the alpha index as the topological measure more relevant in the normal network flow; moreover the interruption of the links with highest values of connectivity will have larger consequences in the normal functioning of the network than the links with the lowest levels of connectivity. The approach here proposed is a useful insight of the network dynamics, which allows optimizing the worst-case performance of the system. This work can be useful for risk management actors, for civil protection agents, who need to decide on the effective allocation of human and physical resources and define priority areas, and for the government institutions which design the network of facilities.


Road network; Interruption; Graph theory; Dual network analysis; Biclustering analysis; Network performance


Pattern Recognition


Journal of Transport Geography, Elsevier, Vol. 46, pp. 55-66, Elsevier, June 2015


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