Kauppakorkeakoulu | Taloustieteen laitos | Kansantaloustiede | 2015
Tutkielman numero: 14021
Demand forecasting in a railway revenue management system
|Otsikko:||Demand forecasting in a railway revenue management system|
|Vuosi:||2015 Kieli: eng|
|Asiasanat:||taloustieteet; economic science; ennusteet; forecasts; kysyntä; demand; liiketalous; business economics; rautatiet; railways; rautatieliikenne; railway transport; tuotto; rate of return|
» hse_ethesis_14021.pdf koko: 2 MB (1570798)
|Avainsanat:||revenue management; willingness-to-pay; price differentiation; capacity allocation; demand forecasting; railways|
The research in Revenue Management has tightly focused on airline markets and somewhat neglected other similar markets. The purpose of this thesis is to offer an extensive overview on RM in the railway context. The backgrounds and concepts of RM are discussed and the applicability of RM in railway markets is evaluated. The differences between railways and airlines are also explored. I am especially focused on the demand forecasting process and different methods that can be used to forecast uncertain demand for a specific train. I also discuss how demand forecasting relates to other RM components, such as capacity allocation.
Relevant RM theories and demand forecasting methods are compiled based on the existing literature. Because of the limited availability of real demand data, I use hypothetical demand data to illustrate how different forecasting methods can be applied and how the performance of each method can be evaluated. I also compile an illustrative capacity allocation example using EMSR -model.
I conclude that the applicability of RM in railway markets is evident. I find four significant differences between railways and airlines that are relevant to RM. Railways tend to have more complex networks, less price differentiation, shorter booking lead times, and less competitive markets. Illustrative demand forecasting examples indicate that the evaluation of different forecasting methods is essential, since the performance of different methods might vary substantially, depending on the available data and the time horizon of desired forecast. Capacity allocation examples suggest that it is particularly important that demand forecasts would provide the accurate predictions of total demand and demand distribution between fare classes. However, it should be taken into account that the findings of illustrative examples cannot be generalized, since the hypothetical data was used in the analysis. Thus further examination with real demand data should be required. Additionally, the issues of constrained data and network effect are omitted from the analysis.
Verkkojulkaisut ovat tekijänoikeuden alaista aineistoa. Teokset ovat vapaasti luettavissa ja tulostettavissa henkilökohtaista käyttöä varten. Aineiston käyttö kaupallisiin tarkoituksiin on kielletty.