Optimizing Frequencies in a Transit Network: a Nonlinear Bi-level Programming Approach.

Gespeichert in:
Bibliographische Detailangaben
Titel: Optimizing Frequencies in a Transit Network: a Nonlinear Bi-level Programming Approach.
Autoren: Constantin, Isabelle, Florian, Michael
Quelle: International Transactions in Operational Research; Mar1995, Vol. 2 Issue 2, p149, 16p, 2 Diagrams, 2 Charts, 4 Maps
Schlagwörter: URBAN transportation, PUBLIC transit, ALGORITHMS, MATHEMATICAL optimization
Abstract: We consider the problem of optimizing the frequencies of transit lines in an urban transportation network. The problem is formulated first as a nonlinear nonconvex mixed integer programming problem and then it is converted into a bi-level Min–Min nonconvex optimization problem. This problem is solved by a projected (sub)gradient algorithm, where a (sub)gradient is obtained at each iteration by solving the lower level problem. Computational results obtained with this algorithm are presented for the transit networks of the cities of Stockholm, Sweden, Winnipeg, Man., Canada and Portland, OR, U.S.A. [ABSTRACT FROM AUTHOR]
Copyright of International Transactions in Operational Research is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Datenbank: Complementary Index
Beschreibung
Abstract:We consider the problem of optimizing the frequencies of transit lines in an urban transportation network. The problem is formulated first as a nonlinear nonconvex mixed integer programming problem and then it is converted into a bi-level Min–Min nonconvex optimization problem. This problem is solved by a projected (sub)gradient algorithm, where a (sub)gradient is obtained at each iteration by solving the lower level problem. Computational results obtained with this algorithm are presented for the transit networks of the cities of Stockholm, Sweden, Winnipeg, Man., Canada and Portland, OR, U.S.A. [ABSTRACT FROM AUTHOR]
ISSN:09696016
DOI:10.1111/j.1475-3995.1995.tb00011.x