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Robust Berth Scheduling Using Machine Learning for Vessel Arrival Time Prediction - refined dataset
Citation Link: https://doi.org/10.15480/336.4471
Type
Dataset
Date Issued
2022-07-22
Author(s)
Rückert, Nicolas
Language
English
Abstract
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/
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/
Subjects
Berth Allocation Problem
Machine Learning
Uncertainty
Prediction
Robust Optimization
DDC Class
004: Computer Sciences
330: Economics
620: Engineering
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