  Purpose: Traffic volume in empty container depots has been highly volatile
due to external factors. Forecasting the expected container truck traffic along
with having a dynamic module to foresee the future workload plays a critical
role in improving the work efficiency. This paper studies the relevant
literature and designs a forecasting model addressing the aforementioned
issues. Methodology: The paper develops a forecasting model to predict hourly
work and traffic volume of container trucks in an empty container depot using a
Bayesian Neural Network based model. Furthermore, the paper experiments with
datasets with different characteristics to assess the model's forecasting range
for various data sources. Findings: The real data of an empty container depot
is utilized to develop a forecasting model and to later verify the capabilities
of the model. The findings show the performance validity of the model and
provide the groundwork to build an effective traffic and workload planning
system for the empty container depot in question. Originality: This paper
proposes a Bayesian deep learning-based forecasting model for traffic and
workload of an empty container depot using real-world data. This designed and
implemented forecasting model offers a solution with which every actor in the
container truck transportation benefits from the optimized workload.
