Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/325148 
Year of Publication: 
2021
Citation: 
[Journal:] EURO Journal on Transportation and Logistics (EJTL) [ISSN:] 2192-4384 [Volume:] 10 [Issue:] 1 [Article No.:] 100034 [Year:] 2021 [Pages:] 1-13
Publisher: 
Elsevier, Amsterdam
Abstract: 
The line-planning problem seeks to determine the set of fixed routes (or lines) a transit operator should run, along with associated operation frequencies. We propose an optimization algorithm for the transit-line-planning problem based on bi-level programming that exploits the problem's structure in conjunction with the range estimation of demands. The issue of conservativeness due to the range estimation is mitigated by adopting the robust optimization approach. The model was inspired by a real-world application that leverages big data available from telecommunications operators to estimate city-wide mobility patterns. Demand estimates from such sources are based on large sample sizes (often orders of magnitude larger than those used in survey-based approaches), and capture day-to-day variability in travel demand as ranges. The validity of the proposed algorithm is demonstrated by using real-world data derived from 2.5 billion call data records from Abidjan, Côte d'Ivoire.
Subjects: 
Column generation
Mobile phone data
Robust network design
Transit line planning
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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