Prediction of estimated time of arrival for multi-airport systems via “Bubble” mechanism

Predicting Estimated Time of Arrival (ETA) for a Multi-Airport System (MAS) is much more challenging than for a single airport system because of complex air route structure, dense air traffic volume and vagaries of traffic conditions in an MAS. In this work, we propose a novel “Bubble” mechanism to accurately predict medium-term ETA for a...

A Neural Network Approach for ETA Prediction in Inland Waterway Transport

Ensuring the accuracy of the estimated time of arrival (ETA) information for ships approaching ports and inland terminals is increasingly critical today. Waterway transportation plays a vital role in freight transportation and has a significant ecological impact. Improving the accuracy of ETA predictions can enhance the reliability of inland...

Proof of Delivery Smart Contract for Performance Measurements

The growth of the enterprise blockchain research supporting supply chain management calls for investigations of their impact and mindfulness of their design, use cases, and pilots. With a blockchain design for the Proof of Delivery (PoD) process management, this paper contributes to learning about performance measurement and the transaction...

ETA prediction for containerships at the Port of Rotterdam using Machine Learning Techniques

The hinterland transportation of incoming containers at container terminals is a complex problem, due to the various actors involved and their often conflicting interests. A promising solution towards the problem for hinterland network operators is that of synchromodality, a concept that refers to on-line network planning for hinterland...