Transport network downsizing based on optimal sub-network

Abstract

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.

Keywords

• Public transportation network
• Linear programming
• Decision tool
• Downsizing networks
• Case study