Application of machine learning techniques in railway demand forecasting
by Neda Etebari Alamdari; Miguel F. Anjos; Gilles Savard
International Journal of Revenue Management (IJRM), Vol. 12, No. 1/2, 2021

Abstract: Demand forecasting lies at the heart of any revenue management system. It aims to estimate the quantity of a product or service that will be purchased in the future. In this paper, we perform railway demand forecasting for a major European railroad company by taking various contributing parameters into account. To have multipurpose results, the current problem is explored in two different aggregation levels. At the high level, the problem is defined as prediction of the total number of bookings for all trains departing on a specific departure date and within a certain time range. Moreover, in a more disaggregated level, the prediction models aim to compute the total number of bookings within each booking period for all trains leaving in a specific time range of a certain departure date. Using state-of-the-art machine learning methods and various heuristic feature construction techniques, remarkable results with high forecast accuracy and reasonable computational complexity are achieved in both aggregation levels. This paper aims to contribute to the application of ML techniques in RM by introducing new heuristic feature engineering techniques, exploring the importance of accurate clustering, and implementing state-of-the-art machine learning methods in the context of railway industry.

Online publication date: Wed, 12-May-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Revenue Management (IJRM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com