Forecasting estimated times of arrival of US freight trains

Zhen Liu, Qingsong Ma, Haichuan Tang, Jiebo Li, Ping Wang and Qing He

Transportation Planning and Technology, 2022, vol. 45, issue 5, 427-448

Abstract: Due to various reasons, variabilities in freight train travel time may be significant, yielding considerable challenges to forecasting the estimated time of arrival (ETA). Based on historical railway ETA data, this study first analyzes the shared route, then converts the historical data information of a train into multiple time data points, and finally, builds a tree-based model selection framework using the random forest algorithm (RF) and a feature weighted K-nearest neighbor algorithm (FWKNN) to create a phased prediction model. In terms of time, we study the use of different algorithms to predict the ETA of freight trains at various locations on freight train routes. In this study, the proposed method was tested on the dataset of the 2021 The Institute for Operations Research and the Management Sciences (INFORMS) Railway Applications Section (RAS) problem solving competition and won 2nd place.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03081060.2022.2115044 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:transp:v:45:y:2022:i:5:p:427-448

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GTPT20

DOI: 10.1080/03081060.2022.2115044

Access Statistics for this article

Transportation Planning and Technology is currently edited by Dr. David Gillingwater

More articles in Transportation Planning and Technology from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().