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 qualitat...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.展开更多
基金supported by the Smart Lab LABILITY of the University Gustave Eiffel,funded by the Region Ile de France(Grant No.20012741)by the French ANR research project PROMENADE(Grant No.ANR-18-CE22-0008).
文摘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.
