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On the selection of relevant historical demand data for revenue management applied to transportation
Universal Prediction AB, Gothenburg, Sweden; Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
Stena Line, Gothenburg, Sweden.
Stena Line, Gothenburg, Sweden.
Stena Line, Gothenburg, Sweden.
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2023 (English)In: Journal of Revenue and Pricing Management, ISSN 1476-6930, E-ISSN 1477-657X, Vol. 22, no 4, p. 266-275Article in journal (Refereed) Published
Abstract [en]

The success of revenue management models depends to a large extent on the quality of historical data used to forecast future bookings. Several theoretical models and best practices of handing historical data have been developed over the years, that all rely on assumptions about underlying distribution and seasonality in the historical data. In this paper, we describe a novel method that compares the fingerprints of the departure to optimise and selects historical departures without making assumptions on data distribution or seasonality. By evaluating the method at the departure level and using the Nemenyi rank test, we show the method’s application in the ferry transportation business and discuss its advantages.

Place, publisher, year, edition, pages
Springer, 2023. Vol. 22, no 4, p. 266-275
Keywords [en]
Departure clustering, Historical demand, Pricing, Revenue management
National Category
Computer Sciences Business Administration
Identifiers
URN: urn:nbn:se:hj:diva-63304DOI: 10.1057/s41272-022-00371-0ISI: 000765660700001Scopus ID: 2-s2.0-85125697770Local ID: HOA;;926496OAI: oai:DiVA.org:hj-63304DiVA, id: diva2:1826396
Available from: 2024-01-11 Created: 2024-01-11 Last updated: 2024-01-11Bibliographically approved

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Carlsson, Lars

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