Please use this identifier to cite or link to this item: https://doi.org/10.15480/336.4471
Title: Robust Berth Scheduling Using Machine Learning for Vessel Arrival Time Prediction - refined dataset
Language: English
Authors: Kolley, Lorenz  
Rückert, Nicolas  
Kastner, Marvin  
Jahn, Carlos  
Fischer, Kathrin  
Keywords: Berth Allocation Problem; Machine Learning; Uncertainty; Prediction; Robust Optimization
Issue Date: 22-Jul-2022
Abstract (english): 
The refined dataset consists of edited data from raw AIS data.
The data is organised as follows: There is one csv file per cargo ship (271 files in total from 1.csv to 271.csv). This file contains all calls of the respective ship at the Port of Miami for the given period from 2018 to 2020. To anonymize the data, the identifying ship data, i.e. the MMSI, was removed. Further information can be found in the paper in Section 3.1. (paper is under review and will be linked here as soon as available)

The raw AIS data which are used are available on the websites of the National Oceanic and Atmospheric Administration (NOAA) Office for Coastal Management. An overview is published here: https://marinecadastre.gov/ais/
URI: http://hdl.handle.net/11420/13169
DOI: 10.15480/336.4471
Institute: Quantitative Unternehmensforschung und Wirtschaftsinformatik W-4 
Maritime Logistik W-12 
Document Type: Dataset
Project: I³-Lab - Business Analytics – Optimierungspotenziale und strategische Risiken für maritime logistische Systeme 
Funded by: Behörde für Wissenschaft, Forschung und Gleichstellung 
License: CC0 1.0 (Public Domain Dedication) CC0 1.0 (Public Domain Dedication)
Is supplement to: 10.1007/s10696-022-09462-x
Appears in Collections:Research Data TUHH

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