Freight transport prediction using electronic waybills and machine learning

Proceedings 2014 International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS) ◽

10.1109/iccss.2014.6961829 ◽

2014 ◽

Cited By ~ 2

Author(s):

Shoaib Bakhtyar ◽

Lawrence Henesey

Keyword(s):

Machine Learning ◽

Freight Transport ◽

Transport Prediction

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An ETA Prediction Model for Intermodal Transport Networks Based on Machine Learning

Business & Information Systems Engineering ◽

10.1007/s12599-020-00653-0 ◽

2020 ◽

Vol 62 (5) ◽

pp. 403-416

Author(s):

Andreas Balster ◽

Ole Hansen ◽

Hanno Friedrich ◽

André Ludwig

Keyword(s):

Machine Learning ◽

Prediction Model ◽

Computational Study ◽

Freight Transport ◽

Transport Processes ◽

Process Efficiency ◽

Time Of Arrival ◽

Transport Networks ◽

External Data ◽

Intermodal Freight

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 of complex transport operations and thus has a positive effect on the resilience and profitability of IFTNs.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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