RT info:eu-repo/semantics/conferenceObject
T1 Using Data Analytics & Machine Learning to Design Business Interruption Insurance Products for Rail Freight Operators
A1 Cardona, John F.
A1 Castaneda, Juliana
A1 Martins, Leandro do C.
A1 Gandouz, Mariem
A1 Juan, Angel A.
A1 Franco, Guillermo
K1 Ferrocarriles
K1 Railways
K1 Ingeniería civil
K1 Civil engineering
K1 Transporte
K1 Transportation
AB This paper discusses a case study in which publicly available data of a rail freighttransportation firm has been gathered, cleansed, and analyzed in order to: (i) describe thedata using statistical indicators and graphs; (ii) identify patterns regarding several KeyPerformance Indicators; (iii) obtain forecasts on the future evolution of these indicators; and(iv) use the identified patterns and the generated forecasts to propose customized insuranceproducts that reflect the current and future freight transportation activity. The paperillustrates the different methodological steps required during the extraction and cleansing ofthe data --which required the development of Python scripts--, the use of time series analysisfor obtaining reliable forecasts, and the use of machine learning models for designingcustomized insurance coverage from the identified patterns and predicted values.
PB Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional
SN 978-84-18465-12-3
YR 2021
FD 2021-07
LK http://hdl.handle.net/10259/6876
UL http://hdl.handle.net/10259/6876
LA eng
NO 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
NO This study was collectively completed and supported by Guy Carpenter & Company, LLC, and the Universitat Oberta de Catalunya.
DS Repositorio Institucional de la Universidad de Burgos
RD 16-abr-2024