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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) |
| Appears in Collections: | Research Data TUHH |
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