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Conference papers
An optimization approach for long planning of multimodal freight transportation systems
Abstract : In this communication is considered the problem of long term forecasting of the freight traffic growth in a large transportation network. This problem is crucial when planning the necessary investments in terminals, transportation links and fleets. One of the main difficulties of this task is related with the estimation of future demand over the network which has direct influence on the operational conditions and costs. The proposed approach makes use of two different optimization models : one model is devoted to freight demand forecasting, the other one defines the global transport supply according with a profit maximization behavior for the whole freight transport sector. The freight demand forecasting process is based on a new entropy maximization approach to determine the distribution of origin-destination matrices. A two level solution technique considering vehicle flows at the first level and the freight flows at the second level is introduced. The proposed solution scheme is composed of an iterative process between the current solution for freight demand forecasting and the supply optimization problem : the entropy maximizing freight distribution problem provides the freight origin-destination matrix given a fare structure, while the supply optimization problem provides the fare structure given a freight origin-destination matrix.
Cited literature [9 references]
https://hal-enac.archives-ouvertes.fr/hal-00938288
Contributor : Laurence Porte Connect in order to contact the contributor
Submitted on : Thursday, May 22, 2014 - 4:25:53 PM
Last modification on : Thursday, June 10, 2021 - 3:05:41 AM
Long-term archiving on: : Friday, August 22, 2014 - 10:42:05 AM
Contributor : Laurence Porte Connect in order to contact the contributor
Submitted on : Thursday, May 22, 2014 - 4:25:53 PM
Last modification on : Thursday, June 10, 2021 - 3:05:41 AM
Long-term archiving on: : Friday, August 22, 2014 - 10:42:05 AM
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