| Autor: |
Matthieu Guillot, Angelo Furno, El-Houssaine Aghezzaf, Nour-Eddin El Faouzi |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Communications in Transportation Research, Vol 2, Iss , Pp 100079- (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2772-4247 |
| DOI: |
10.1016/j.commtr.2022.100079 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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