<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-08-01T03:12:08Z</responseDate><request verb="GetRecord" identifier="oai:riubu.ubu.es:10259/6876" metadataPrefix="dim">https://riubu.ubu.es/oai/request</request><GetRecord><record><header><identifier>oai:riubu.ubu.es:10259/6876</identifier><datestamp>2024-05-20T09:52:07Z</datestamp><setSpec>com_10259.4_104</setSpec><setSpec>com_10259_2604</setSpec><setSpec>col_10259_6848</setSpec></header><metadata><dim:dim xmlns:dim="http://www.dspace.org/xmlns/dspace/dim" xmlns:doc="http://www.lyncode.com/xoai" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.dspace.org/xmlns/dspace/dim http://www.dspace.org/schema/dim.xsd">
<dim:field mdschema="dc" element="contributor" qualifier="author" authority="4a62739c-37f5-4fe0-8a2c-df05d64588f0" confidence="600" orcid_id="">Cardona, John F.</dim:field>
<dim:field mdschema="dc" element="contributor" qualifier="author" authority="68e1b60c-42bc-47f3-b62d-05a1f9db9841" confidence="600" orcid_id="">Castaneda, Juliana</dim:field>
<dim:field mdschema="dc" element="contributor" qualifier="author" authority="8170d7bf-a1b4-4978-a9b5-90c4b61dd000" confidence="600" orcid_id="">Martins, Leandro do C.</dim:field>
<dim:field mdschema="dc" element="contributor" qualifier="author" authority="5b8b6979-3655-45b8-afb4-1531a1029496" confidence="600" orcid_id="">Gandouz, Mariem</dim:field>
<dim:field mdschema="dc" element="contributor" qualifier="author" authority="4061eb58-350a-4b1b-9e74-ac153f2e863b" confidence="600" orcid_id="">Juan, Angel A.</dim:field>
<dim:field mdschema="dc" element="contributor" qualifier="author" authority="150d5e2c-85a6-4f84-a222-e59735cb5437" confidence="600" orcid_id="">Franco, Guillermo</dim:field>
<dim:field mdschema="dc" element="date" qualifier="accessioned">2022-09-16T07:08:08Z</dim:field>
<dim:field mdschema="dc" element="date" qualifier="available">2022-09-16T07:08:08Z</dim:field>
<dim:field mdschema="dc" element="date" qualifier="issued">2021-07</dim:field>
<dim:field mdschema="dc" element="identifier" qualifier="isbn">978-84-18465-12-3</dim:field>
<dim:field mdschema="dc" element="identifier" qualifier="uri">http://hdl.handle.net/10259/6876</dim:field>
<dim:field mdschema="dc" element="identifier" qualifier="doi">10.36443/10259/6876</dim:field>
<dim:field mdschema="dc" element="description" lang="es">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</dim:field>
<dim:field mdschema="dc" element="description" qualifier="abstract" lang="en">This paper discusses a case study in which publicly available data of a rail freight&#xd;
transportation firm has been gathered, cleansed, and analyzed in order to: (i) describe the&#xd;
data using statistical indicators and graphs; (ii) identify patterns regarding several Key&#xd;
Performance Indicators; (iii) obtain forecasts on the future evolution of these indicators; and&#xd;
(iv) use the identified patterns and the generated forecasts to propose customized insurance&#xd;
products that reflect the current and future freight transportation activity. The paper&#xd;
illustrates the different methodological steps required during the extraction and cleansing of&#xd;
the data --which required the development of Python scripts--, the use of time series analysis&#xd;
for obtaining reliable forecasts, and the use of machine learning models for designing&#xd;
customized insurance coverage from the identified patterns and predicted values.</dim:field>
<dim:field mdschema="dc" element="description" qualifier="sponsorship" lang="en">This study was collectively completed and supported by Guy Carpenter &amp; Company, LLC, and the Universitat Oberta de Catalunya.</dim:field>
<dim:field mdschema="dc" element="format" qualifier="mimetype">application/pdf</dim:field>
<dim:field mdschema="dc" element="language" qualifier="iso" lang="es">eng</dim:field>
<dim:field mdschema="dc" element="publisher" lang="es">Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional</dim:field>
<dim:field mdschema="dc" element="relation" qualifier="ispartof" lang="es">R-Evolucionando el transporte</dim:field>
<dim:field mdschema="dc" element="relation" qualifier="uri">http://hdl.handle.net/10259/6490</dim:field>
<dim:field mdschema="dc" element="relation" qualifier="publisherversion" lang="es">https://doi.org/10.36443/9788418465123</dim:field>
<dim:field mdschema="dc" element="subject" lang="es">Ferrocarriles</dim:field>
<dim:field mdschema="dc" element="subject" lang="en">Railways</dim:field>
<dim:field mdschema="dc" element="subject" qualifier="other" lang="es">Ingeniería civil</dim:field>
<dim:field mdschema="dc" element="subject" qualifier="other" lang="es">Transportes</dim:field>
<dim:field mdschema="dc" element="subject" qualifier="other" lang="en">Civil engineering</dim:field>
<dim:field mdschema="dc" element="subject" qualifier="other" lang="en">Transportation</dim:field>
<dim:field mdschema="dc" element="title" lang="en">Using Data Analytics &amp; Machine Learning to Design Business Interruption Insurance Products for Rail Freight Operators</dim:field>
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<dim:field mdschema="dc" element="page" qualifier="initial" lang="es">487</dim:field>
<dim:field mdschema="dc" element="page" qualifier="final" lang="es">504</dim:field>
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