In a complex network such as a road infrastructure system, disruptive events have direct consequences (loss of lives, structural damage, economy losses etc.) related to the sub-system level directly affected, as well as indirect consequences on the overall system level such as loss of functionality and related monetary consequences. To quantify the influence of component failures on the network performance, complex network analysis allows representing the relation among its components and the consequences in order to estimate the functionality of the system. The serviceability of a road network is the possibility to use it during a given time period as representing the performance of the system. Thus, the time to restored serviceability after a disruptive event is measuring road transportation resilience. In this paper, network analysis with the help of OSMnx Python package, supplied by road network data from OpenStreetMap, is applied to a Spanish motorway case study. Loss of serviceability index is obtained combining the results of road links failure scenarios based on shortest paths and travel times in order to estimate road network resilience. The proposed methodology allows a suitable evaluation of the influence of road links in the risk assessment and management strategies by road infrastructure owners and managers.