文章基本信息

标题：An ETA Prediction Model for Intermodal Transport Networks Based on Machine Learning
其他标题：An ETA Prediction Model for Intermodal Transport Networks Based on Machine Learning
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作者：Balster, Andreas ; Hansen, Ole ; Friedrich, Hanno 等
期刊名称：Business & Information Systems Engineering
出版年度：2019
卷号：62
期号：5
页码：403-416
DOI：https://doi.org/10.1007/s12599-020-00653-0
出版社：Association for Information Systems
摘要：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.
关键词：Estimated time of arrival (ETA); Freight transport; Hinterland transport; Intermodal transport; Machine learning; Predictive analytics; Scheduled transports; Transport networks