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.

翻译：目的:由于外部因素,空集装箱仓库的交通量极不稳定; 预测预期的集装箱卡车交通量,同时有一个能预见未来工作量的动态模块,在提高工作效率方面发挥着关键作用; 本文研究相关文献,并设计了一个处理上述问题的预测模型; 方法: 论文开发了一个预测模型,利用一个基于贝耶斯神经网络的模型,预测空集装箱仓库集装箱卡车的每小时工作和交通量; 此外,用具有不同特点的数据集进行纸质试验,以评估模型对各种数据来源的预测范围; 调查结果:利用一个空集装箱仓库的真实数据开发一个预测模型,随后核查模型的能力; 研究结果表明该模型的性能,并为建立一个有效的空集装箱仓库的交通和工作量规划系统奠定基础; 原样:本文提出一个基于学习的深度预测模型,用于利用现实世界数据,对空集装箱仓库的交通和工作量进行深度预测; 这一设计和实施的预测模型提供了解决办法,使集装箱集装箱集装箱运输的每个行为者都能从优化工作量中受益。