An ETA Prediction Model for Intermodal Transport Networks Based on Machine Learning
- PDF / 1,186,798 Bytes
- 14 Pages / 595.276 x 790.866 pts Page_size
- 60 Downloads / 218 Views
RESEARCH PAPER
An ETA Prediction Model for Intermodal Transport Networks Based on Machine Learning Andreas Balster • Ole Hansen • Hanno Friedrich • Andre´ Ludwig
Received: 15 January 2020 / Accepted: 23 April 2020 The Author(s) 2020
Abstract 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
Accepted after one revision by Witold Abamowicz. A. Balster (&) O. Hansen H. Friedrich A. Ludwig Ku¨hne Logistics University, Großer Grasbrook 17, 20457 Hamburg, Germany e-mail: [email protected]
of complex transport operations and thus has a positive effect on the resilience and profitability of IFTNs. Keywords Estimated time of arrival (ETA) Freight transport Hinterland transport Intermodal transport Machine learning Predictive analytics Scheduled transports Transport networks
1 Introduction Driven by supply chains with a more and more global reach, today’s freight transport networks must connect increasingly distant production and sales regions, and such global competition leads to increasing demands for service, delivery times and cost efficiency. Simultaneously, constraints such as limited space in facilities and regulations (e.g., environmental protection and customs) must be considered. These factors create greater dynamics and complexity in global freight transport networks, resulting in increased vulnerability. This increased vulnerability becomes apparent in supply chains when companies simultaneously try to to reduce risk buffers as a result of rising cost pressure. Interviews revealed that instead of short but unstab
Data Loading...
