Transport network downsizing based on optimal sub-network

Transportation networks are sized to efficiently achieve some set of service objectives.Under particular circumstances,such as the COVID-19 pandemic,the demand for transportation can significantly change,both qualitatively and quantitatively,resulting in an over-capacitated and less efficient network.In this paper,we address this issue by proposing a framework for resizing the network to efficiently cope with the new demand.The framework includes a model to determine an optimal transportation sub-network that guarantees the following:(i)the minimal access time from any node of the urban network to the new sub-network has not excessively increased compared to that of the original transportation network;(ii)the delay induced on any itinerary by the removal of nodes from the original transportation network has not excessively increased;and(iii)the number of removed nodes from the transportation network is within a preset known factor.A solution is optimal if it induces a minimal global delay.We modelled this problem as a Mixed Integer Linear Program and applied it to the public bus transportation network of Lyon,France,in a case study.In order to respond to operational issues,the framework also includes a decision tool that helps the network planners to decide which bus lines to close and which ones to leave open according to specific trade-off preferences.The results on real data in Lyon show that the optimal sub-network from the MILP model can be used to feed the decision tool,leading to operational scenarios for network planners.