2026-07-31T20:26:34Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/68762024-05-20T09:52:07Zcom_10259.4_104com_10259_2604col_10259_6848
Using Data Analytics & Machine Learning to Design Business Interruption Insurance Products for Rail Freight Operators
Cardona, John F.
Castaneda, Juliana
Martins, Leandro do C.
Gandouz, Mariem
Juan, Angel A.
Franco, Guillermo
Ferrocarriles
Railways
Trabajo presentado en: R-Evolucionando el transporte, XIV Congreso de Ingeniería del Transporte (CIT 2021), realizado en modalidad online los días 6, 7 y 8 de julio de 2021, organizado por la Universidad de Burgos
This paper discusses a case study in which publicly available data of a rail freight
transportation firm has been gathered, cleansed, and analyzed in order to: (i) describe the
data using statistical indicators and graphs; (ii) identify patterns regarding several Key
Performance Indicators; (iii) obtain forecasts on the future evolution of these indicators; and
(iv) use the identified patterns and the generated forecasts to propose customized insurance
products that reflect the current and future freight transportation activity. The paper
illustrates the different methodological steps required during the extraction and cleansing of
the data --which required the development of Python scripts--, the use of time series analysis
for obtaining reliable forecasts, and the use of machine learning models for designing
customized insurance coverage from the identified patterns and predicted values.
2022-09-16
2022-09-16
2021-07
info:eu-repo/semantics/conferenceObject
978-84-18465-12-3
http://hdl.handle.net/10259/6876
10.36443/10259/6876
eng
R-Evolucionando el transporte
http://hdl.handle.net/10259/6490
https://doi.org/10.36443/9788418465123
info:eu-repo/semantics/openAccess
Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional