Predicting on-time deliveries in trucking: a model based on the working conditions of drivers

• Luis David Berrones Sanz ^[1]
  1. [1] Universidad Autónoma de la Ciudad de México

     Universidad Autónoma de la Ciudad de México

     México

     
• Localización: R-evolucionando el transporte [Recurso electrónico]: XIV Congreso de Ingeniería del Transporte. Universidad de Burgos 6, 7 y 8 de julio 2021 / coord. por Hernán Gonzalo Orden, Marta Rojo Arce, 2021, ISBN 978-84-18465-12-3, págs. 961-972
• Idioma: inglés
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• Resumen

  • 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 non-compliance 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-time-delays 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.