2026-06-30T04:09:24Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/69042024-05-17T07:54:39Zcom_10259.4_104com_10259_2604col_10259_6848
Berrones-Sanz, Luis David
2022-09-19T07:35:33Z
2022-09-19T07:35:33Z
2021-07
978-84-18465-12-3
http://hdl.handle.net/10259/6904
10.36443/10259/6904
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
Over a period of two years, 26.3 thousand road freight shipments were recorded. The
records include information about truckload companies, drivers, and the causes of noncompliance
and delays in deliveries. Logistic regression based in working conditions as
independent variables was used to predict non-compliance deliveries attributed to cargo
drivers. Results show that vehicle type, medical coverage and social security, level of
stress, work dissatisfaction, and transit time were strongly associated with out-of-timedelays
in deliveries. The proposed model is a promising tool to improve the performance
of truckload companies and it may motivate to benefit working conditions of truckers.
application/pdf
eng
Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional
R-Evolucionando el transporte
http://hdl.handle.net/10259/6490
https://doi.org/10.36443/9788418465123
Logística
Operaciones
Transporte de mercancías
Logistics
Operations
Transportation of goods
Transportes
Organización del trabajo
Transportation
Methods engineering
Predicting on-time deliveries in trucking: A model based on the working conditions of drivers
info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/openAccess
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