RT info:eu-repo/semantics/conferenceObject
T1 Predicting on-time deliveries in trucking: A model based on the working conditions of drivers
A1 Berrones-Sanz, Luis David
K1 Logística
K1 Logistics
K1 Operaciones
K1 Operations
K1 Transporte de mercancías
K1 Transportation of goods
K1 Transportes
K1 Transportation
K1 Organización del trabajo
K1 Methods engineering
AB Over a period of two years, 26.3 thousand road freight shipments were recorded. Therecords include information about truckload companies, drivers, and the causes of noncomplianceand delays in deliveries. Logistic regression based in working conditions asindependent variables was used to predict non-compliance deliveries attributed to cargodrivers. Results show that vehicle type, medical coverage and social security, level ofstress, work dissatisfaction, and transit time were strongly associated with out-of-timedelaysin deliveries. The proposed model is a promising tool to improve the performanceof truckload companies and it may motivate to benefit working conditions of truckers.
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/10259/6904
UL http://hdl.handle.net/10259/6904
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
DS Repositorio Institucional de la Universidad de Burgos
RD 11-dic-2024