Transparency in transport processes is becoming increasingly important for transport companies to improve internal processes and to be able to compete for customers. One important element to increase transparency is reliable, up-to-date and accurate arrival time prediction, commonly referred to as estimated time of arrival (ETA). ETAs are not easy to determine, especially for intermodal freight transports, in which freight is transported in an intermodal container, using multiple modes of transportation. This computational study describes the structure of an ETA prediction model for intermodal freight transport networks (IFTN), in which schedule-based and non-schedule-based transports are combined, based on machine learning (ML). For each leg of the intermodal freight transport, an individual ML prediction model is developed and trained using the corresponding historical transport data and external data. The research presented in this study shows that the ML approach produces reliable ETA predictions for intermodal freight transport. These predictions comprise processing times at logistics nodes such as inland terminals and transport times on road and rail. Consequently, the outcome of this research allows decision makers to proactively communicate disruption effects to actors along the intermodal transportation chain. These actors can then initiate measures to counteract potential critical delays at subsequent stages of transport. This approach leads to increased process efficiency for all actors in the realization of complex transport operations and thus has a positive effect on the resilience and profitability of IFTNs.

中文翻译：

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基于机器学习的联运网络ETA预测模型

运输流程的透明度对于运输公司改进内部流程和争夺客户变得越来越重要。提高透明度的一个重要因素是可靠、最新和准确的到达时间预测，通常称为预计到达时间 (ETA)。ETA 不容易确定，特别是对于多式联运货物运输，其中货物在多式联运集装箱中运输，使用多种运输方式。这项计算研究描述了多式联运货运网络 (IFTN) 的 ETA 预测模型的结构，其中基于机器学习 (ML) 将基于时间表和非基于时间表的运输相结合。对于多式联运的每个航段，使用相应的历史运输数据和外部数据开发和训练单个 ML 预测模型。本研究中提出的研究表明，ML 方法可为多式联运货物运输提供可靠的 ETA 预测。这些预测包括物流节点（例如内陆码头）的处理时间以及公路和铁路的运输时间。因此，这项研究的结果使决策者能够主动向多式联运链中的参与者传达中断影响。然后，这些参与者可以采取措施来抵消后续运输阶段潜在的严重延误。