Mobility-as-a-Service (MaaS) is one of the sustainable solutions proposed and implemented by transportation planners and policy-makers to cater for the rising issue of congestion and associated external social and environmental costs. However, it is seldom evaluated how a planned provision of the MaaS system can impact the transport network performance. This research is carried out as a part of the Green MaaS for Adaptive Urban Planning (GeTUP) Project. It aims to critically evaluate the road network structure of the city of Genova and skim out the network’s most congested components (nodes and links), thus highlighting the much-needed places for an efficient MaaS provision based on adaptive planning that relies on various reliability measures quantified here. The network structure is analysed by using demand-based dynamic network centrality measures for its components (nodes and links). Whereas, the critical points of the network in each component cluster are skimmed out using a mixed-machine learning approach. Later, various demand elasticity scenarios are analysed for each cluster given adaptive green MaaS system presence and demand shift to that system. It is revealed from the analysis that in some parts of the road network the demand is as much as 78% higher than the provided capacity. Thus generating large travel times. However, when MaaS system is provided in terms of different demand elasticity scenarios the travel times are reduced improving the traffic network performance.