Prof. Dr.-Ing. Carlos Jahn
Adresse
Technische Universität Hamburg
Institut für Maritime Logistik
Am Schwarzenberg-Campus 4 (D)
21073 Hamburg
Kontaktdaten
Büro: Gebäude D Raum 5.002a
Anmeldung bei Fr. Beckmann (Raum 5.003)
Tel.: +49 40 42878 4450
Fax: +49 40 42731 4478
E-Mail: carlos.jahn(at)tuhh(dot)de
ORCiD: 0000-0002-5409-0748
Veröffentlichungen (Auszug)
2024
[182359] |
Title: Investigation of Vessel Waiting Times Using AIS Data. <em>Dynamics in Logistics</em> |
Written by: Franzkeit, Janna and Pache, Hannah and Jahn, Carlos |
in: <em>LDIC 2020</em>. (2020). |
Volume: Number: |
on pages: 70-78 |
Chapter: |
Editor: In Freitag, Michael and Haasis, Hans-Dietrich and Kotzab, Herbert and Pannek, Jürgen (Eds.) |
Publisher: Springer International Publishing: |
Series: Lecture Notes in Logistics |
Address: Cham |
Edition: |
ISBN: 978-3-030-44782-3 |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/978-3-030-44783-0_7 |
URL: |
ARXIVID: |
PMID: |
Note: i3lab
Abstract: The automatic identification system (AIS) enables authorities, shipping companies and researchers all over the world using ever better computer technologies to understand and track vessel movements. This publication focuses on analysing vessels’ waiting times for berth at anchoring places near ports using the example of the port of Rotterdam, Europe’s biggest port. The objective is to define clearly the concept of waiting, i.e. when a vessel waits and when not, and to investigate the amount of waiting vessels and the respective waiting times during a time span of more than two years, using solely AIS data. The indicated anchoring zones in front of the port of Rotterdam, where vessels wait, are clearly detected by visualizing the analysed data. The results of the conducted AIS data analysis show significant differences in waiting times between different vessel types, as well as a correlation between the number of waiting vessels and the average waiting time. The in detail described data pre-processing and statistical analysis are extendable and applicable to other regions and ports all over the world. Additionally, the presented data pre-processing approach is an optimal basis analysis of current waiting conditions and for applying machine learning to AIS data in order to predict future waiting times
2023
[182359] |
Title: Investigation of Vessel Waiting Times Using AIS Data. <em>Dynamics in Logistics</em> |
Written by: Franzkeit, Janna and Pache, Hannah and Jahn, Carlos |
in: <em>LDIC 2020</em>. (2020). |
Volume: Number: |
on pages: 70-78 |
Chapter: |
Editor: In Freitag, Michael and Haasis, Hans-Dietrich and Kotzab, Herbert and Pannek, Jürgen (Eds.) |
Publisher: Springer International Publishing: |
Series: Lecture Notes in Logistics |
Address: Cham |
Edition: |
ISBN: 978-3-030-44782-3 |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/978-3-030-44783-0_7 |
URL: |
ARXIVID: |
PMID: |
Note: i3lab
Abstract: The automatic identification system (AIS) enables authorities, shipping companies and researchers all over the world using ever better computer technologies to understand and track vessel movements. This publication focuses on analysing vessels’ waiting times for berth at anchoring places near ports using the example of the port of Rotterdam, Europe’s biggest port. The objective is to define clearly the concept of waiting, i.e. when a vessel waits and when not, and to investigate the amount of waiting vessels and the respective waiting times during a time span of more than two years, using solely AIS data. The indicated anchoring zones in front of the port of Rotterdam, where vessels wait, are clearly detected by visualizing the analysed data. The results of the conducted AIS data analysis show significant differences in waiting times between different vessel types, as well as a correlation between the number of waiting vessels and the average waiting time. The in detail described data pre-processing and statistical analysis are extendable and applicable to other regions and ports all over the world. Additionally, the presented data pre-processing approach is an optimal basis analysis of current waiting conditions and for applying machine learning to AIS data in order to predict future waiting times
2022
[182359] |
Title: Investigation of Vessel Waiting Times Using AIS Data. <em>Dynamics in Logistics</em> |
Written by: Franzkeit, Janna and Pache, Hannah and Jahn, Carlos |
in: <em>LDIC 2020</em>. (2020). |
Volume: Number: |
on pages: 70-78 |
Chapter: |
Editor: In Freitag, Michael and Haasis, Hans-Dietrich and Kotzab, Herbert and Pannek, Jürgen (Eds.) |
Publisher: Springer International Publishing: |
Series: Lecture Notes in Logistics |
Address: Cham |
Edition: |
ISBN: 978-3-030-44782-3 |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/978-3-030-44783-0_7 |
URL: |
ARXIVID: |
PMID: |
Note: i3lab
Abstract: The automatic identification system (AIS) enables authorities, shipping companies and researchers all over the world using ever better computer technologies to understand and track vessel movements. This publication focuses on analysing vessels’ waiting times for berth at anchoring places near ports using the example of the port of Rotterdam, Europe’s biggest port. The objective is to define clearly the concept of waiting, i.e. when a vessel waits and when not, and to investigate the amount of waiting vessels and the respective waiting times during a time span of more than two years, using solely AIS data. The indicated anchoring zones in front of the port of Rotterdam, where vessels wait, are clearly detected by visualizing the analysed data. The results of the conducted AIS data analysis show significant differences in waiting times between different vessel types, as well as a correlation between the number of waiting vessels and the average waiting time. The in detail described data pre-processing and statistical analysis are extendable and applicable to other regions and ports all over the world. Additionally, the presented data pre-processing approach is an optimal basis analysis of current waiting conditions and for applying machine learning to AIS data in order to predict future waiting times
2021
[182359] |
Title: Investigation of Vessel Waiting Times Using AIS Data. <em>Dynamics in Logistics</em> |
Written by: Franzkeit, Janna and Pache, Hannah and Jahn, Carlos |
in: <em>LDIC 2020</em>. (2020). |
Volume: Number: |
on pages: 70-78 |
Chapter: |
Editor: In Freitag, Michael and Haasis, Hans-Dietrich and Kotzab, Herbert and Pannek, Jürgen (Eds.) |
Publisher: Springer International Publishing: |
Series: Lecture Notes in Logistics |
Address: Cham |
Edition: |
ISBN: 978-3-030-44782-3 |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/978-3-030-44783-0_7 |
URL: |
ARXIVID: |
PMID: |
Note: i3lab
Abstract: The automatic identification system (AIS) enables authorities, shipping companies and researchers all over the world using ever better computer technologies to understand and track vessel movements. This publication focuses on analysing vessels’ waiting times for berth at anchoring places near ports using the example of the port of Rotterdam, Europe’s biggest port. The objective is to define clearly the concept of waiting, i.e. when a vessel waits and when not, and to investigate the amount of waiting vessels and the respective waiting times during a time span of more than two years, using solely AIS data. The indicated anchoring zones in front of the port of Rotterdam, where vessels wait, are clearly detected by visualizing the analysed data. The results of the conducted AIS data analysis show significant differences in waiting times between different vessel types, as well as a correlation between the number of waiting vessels and the average waiting time. The in detail described data pre-processing and statistical analysis are extendable and applicable to other regions and ports all over the world. Additionally, the presented data pre-processing approach is an optimal basis analysis of current waiting conditions and for applying machine learning to AIS data in order to predict future waiting times
2020
[182359] |
Title: Investigation of Vessel Waiting Times Using AIS Data. <em>Dynamics in Logistics</em> |
Written by: Franzkeit, Janna and Pache, Hannah and Jahn, Carlos |
in: <em>LDIC 2020</em>. (2020). |
Volume: Number: |
on pages: 70-78 |
Chapter: |
Editor: In Freitag, Michael and Haasis, Hans-Dietrich and Kotzab, Herbert and Pannek, Jürgen (Eds.) |
Publisher: Springer International Publishing: |
Series: Lecture Notes in Logistics |
Address: Cham |
Edition: |
ISBN: 978-3-030-44782-3 |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/978-3-030-44783-0_7 |
URL: |
ARXIVID: |
PMID: |
Note: i3lab
Abstract: The automatic identification system (AIS) enables authorities, shipping companies and researchers all over the world using ever better computer technologies to understand and track vessel movements. This publication focuses on analysing vessels’ waiting times for berth at anchoring places near ports using the example of the port of Rotterdam, Europe’s biggest port. The objective is to define clearly the concept of waiting, i.e. when a vessel waits and when not, and to investigate the amount of waiting vessels and the respective waiting times during a time span of more than two years, using solely AIS data. The indicated anchoring zones in front of the port of Rotterdam, where vessels wait, are clearly detected by visualizing the analysed data. The results of the conducted AIS data analysis show significant differences in waiting times between different vessel types, as well as a correlation between the number of waiting vessels and the average waiting time. The in detail described data pre-processing and statistical analysis are extendable and applicable to other regions and ports all over the world. Additionally, the presented data pre-processing approach is an optimal basis analysis of current waiting conditions and for applying machine learning to AIS data in order to predict future waiting times
2019
[182359] |
Title: Investigation of Vessel Waiting Times Using AIS Data. <em>Dynamics in Logistics</em> |
Written by: Franzkeit, Janna and Pache, Hannah and Jahn, Carlos |
in: <em>LDIC 2020</em>. (2020). |
Volume: Number: |
on pages: 70-78 |
Chapter: |
Editor: In Freitag, Michael and Haasis, Hans-Dietrich and Kotzab, Herbert and Pannek, Jürgen (Eds.) |
Publisher: Springer International Publishing: |
Series: Lecture Notes in Logistics |
Address: Cham |
Edition: |
ISBN: 978-3-030-44782-3 |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/978-3-030-44783-0_7 |
URL: |
ARXIVID: |
PMID: |
Note: i3lab
Abstract: The automatic identification system (AIS) enables authorities, shipping companies and researchers all over the world using ever better computer technologies to understand and track vessel movements. This publication focuses on analysing vessels’ waiting times for berth at anchoring places near ports using the example of the port of Rotterdam, Europe’s biggest port. The objective is to define clearly the concept of waiting, i.e. when a vessel waits and when not, and to investigate the amount of waiting vessels and the respective waiting times during a time span of more than two years, using solely AIS data. The indicated anchoring zones in front of the port of Rotterdam, where vessels wait, are clearly detected by visualizing the analysed data. The results of the conducted AIS data analysis show significant differences in waiting times between different vessel types, as well as a correlation between the number of waiting vessels and the average waiting time. The in detail described data pre-processing and statistical analysis are extendable and applicable to other regions and ports all over the world. Additionally, the presented data pre-processing approach is an optimal basis analysis of current waiting conditions and for applying machine learning to AIS data in order to predict future waiting times
2018
[182359] |
Title: Investigation of Vessel Waiting Times Using AIS Data. <em>Dynamics in Logistics</em> |
Written by: Franzkeit, Janna and Pache, Hannah and Jahn, Carlos |
in: <em>LDIC 2020</em>. (2020). |
Volume: Number: |
on pages: 70-78 |
Chapter: |
Editor: In Freitag, Michael and Haasis, Hans-Dietrich and Kotzab, Herbert and Pannek, Jürgen (Eds.) |
Publisher: Springer International Publishing: |
Series: Lecture Notes in Logistics |
Address: Cham |
Edition: |
ISBN: 978-3-030-44782-3 |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/978-3-030-44783-0_7 |
URL: |
ARXIVID: |
PMID: |
Note: i3lab
Abstract: The automatic identification system (AIS) enables authorities, shipping companies and researchers all over the world using ever better computer technologies to understand and track vessel movements. This publication focuses on analysing vessels’ waiting times for berth at anchoring places near ports using the example of the port of Rotterdam, Europe’s biggest port. The objective is to define clearly the concept of waiting, i.e. when a vessel waits and when not, and to investigate the amount of waiting vessels and the respective waiting times during a time span of more than two years, using solely AIS data. The indicated anchoring zones in front of the port of Rotterdam, where vessels wait, are clearly detected by visualizing the analysed data. The results of the conducted AIS data analysis show significant differences in waiting times between different vessel types, as well as a correlation between the number of waiting vessels and the average waiting time. The in detail described data pre-processing and statistical analysis are extendable and applicable to other regions and ports all over the world. Additionally, the presented data pre-processing approach is an optimal basis analysis of current waiting conditions and for applying machine learning to AIS data in order to predict future waiting times
2017
[182359] |
Title: Investigation of Vessel Waiting Times Using AIS Data. <em>Dynamics in Logistics</em> |
Written by: Franzkeit, Janna and Pache, Hannah and Jahn, Carlos |
in: <em>LDIC 2020</em>. (2020). |
Volume: Number: |
on pages: 70-78 |
Chapter: |
Editor: In Freitag, Michael and Haasis, Hans-Dietrich and Kotzab, Herbert and Pannek, Jürgen (Eds.) |
Publisher: Springer International Publishing: |
Series: Lecture Notes in Logistics |
Address: Cham |
Edition: |
ISBN: 978-3-030-44782-3 |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/978-3-030-44783-0_7 |
URL: |
ARXIVID: |
PMID: |
Note: i3lab
Abstract: The automatic identification system (AIS) enables authorities, shipping companies and researchers all over the world using ever better computer technologies to understand and track vessel movements. This publication focuses on analysing vessels’ waiting times for berth at anchoring places near ports using the example of the port of Rotterdam, Europe’s biggest port. The objective is to define clearly the concept of waiting, i.e. when a vessel waits and when not, and to investigate the amount of waiting vessels and the respective waiting times during a time span of more than two years, using solely AIS data. The indicated anchoring zones in front of the port of Rotterdam, where vessels wait, are clearly detected by visualizing the analysed data. The results of the conducted AIS data analysis show significant differences in waiting times between different vessel types, as well as a correlation between the number of waiting vessels and the average waiting time. The in detail described data pre-processing and statistical analysis are extendable and applicable to other regions and ports all over the world. Additionally, the presented data pre-processing approach is an optimal basis analysis of current waiting conditions and for applying machine learning to AIS data in order to predict future waiting times
2016
[182359] |
Title: Investigation of Vessel Waiting Times Using AIS Data. <em>Dynamics in Logistics</em> |
Written by: Franzkeit, Janna and Pache, Hannah and Jahn, Carlos |
in: <em>LDIC 2020</em>. (2020). |
Volume: Number: |
on pages: 70-78 |
Chapter: |
Editor: In Freitag, Michael and Haasis, Hans-Dietrich and Kotzab, Herbert and Pannek, Jürgen (Eds.) |
Publisher: Springer International Publishing: |
Series: Lecture Notes in Logistics |
Address: Cham |
Edition: |
ISBN: 978-3-030-44782-3 |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/978-3-030-44783-0_7 |
URL: |
ARXIVID: |
PMID: |
Note: i3lab
Abstract: The automatic identification system (AIS) enables authorities, shipping companies and researchers all over the world using ever better computer technologies to understand and track vessel movements. This publication focuses on analysing vessels’ waiting times for berth at anchoring places near ports using the example of the port of Rotterdam, Europe’s biggest port. The objective is to define clearly the concept of waiting, i.e. when a vessel waits and when not, and to investigate the amount of waiting vessels and the respective waiting times during a time span of more than two years, using solely AIS data. The indicated anchoring zones in front of the port of Rotterdam, where vessels wait, are clearly detected by visualizing the analysed data. The results of the conducted AIS data analysis show significant differences in waiting times between different vessel types, as well as a correlation between the number of waiting vessels and the average waiting time. The in detail described data pre-processing and statistical analysis are extendable and applicable to other regions and ports all over the world. Additionally, the presented data pre-processing approach is an optimal basis analysis of current waiting conditions and for applying machine learning to AIS data in order to predict future waiting times
2015
[182359] |
Title: Investigation of Vessel Waiting Times Using AIS Data. <em>Dynamics in Logistics</em> |
Written by: Franzkeit, Janna and Pache, Hannah and Jahn, Carlos |
in: <em>LDIC 2020</em>. (2020). |
Volume: Number: |
on pages: 70-78 |
Chapter: |
Editor: In Freitag, Michael and Haasis, Hans-Dietrich and Kotzab, Herbert and Pannek, Jürgen (Eds.) |
Publisher: Springer International Publishing: |
Series: Lecture Notes in Logistics |
Address: Cham |
Edition: |
ISBN: 978-3-030-44782-3 |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/978-3-030-44783-0_7 |
URL: |
ARXIVID: |
PMID: |
Note: i3lab
Abstract: The automatic identification system (AIS) enables authorities, shipping companies and researchers all over the world using ever better computer technologies to understand and track vessel movements. This publication focuses on analysing vessels’ waiting times for berth at anchoring places near ports using the example of the port of Rotterdam, Europe’s biggest port. The objective is to define clearly the concept of waiting, i.e. when a vessel waits and when not, and to investigate the amount of waiting vessels and the respective waiting times during a time span of more than two years, using solely AIS data. The indicated anchoring zones in front of the port of Rotterdam, where vessels wait, are clearly detected by visualizing the analysed data. The results of the conducted AIS data analysis show significant differences in waiting times between different vessel types, as well as a correlation between the number of waiting vessels and the average waiting time. The in detail described data pre-processing and statistical analysis are extendable and applicable to other regions and ports all over the world. Additionally, the presented data pre-processing approach is an optimal basis analysis of current waiting conditions and for applying machine learning to AIS data in order to predict future waiting times
2014
[182359] |
Title: Investigation of Vessel Waiting Times Using AIS Data. <em>Dynamics in Logistics</em> |
Written by: Franzkeit, Janna and Pache, Hannah and Jahn, Carlos |
in: <em>LDIC 2020</em>. (2020). |
Volume: Number: |
on pages: 70-78 |
Chapter: |
Editor: In Freitag, Michael and Haasis, Hans-Dietrich and Kotzab, Herbert and Pannek, Jürgen (Eds.) |
Publisher: Springer International Publishing: |
Series: Lecture Notes in Logistics |
Address: Cham |
Edition: |
ISBN: 978-3-030-44782-3 |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/978-3-030-44783-0_7 |
URL: |
ARXIVID: |
PMID: |
Note: i3lab
Abstract: The automatic identification system (AIS) enables authorities, shipping companies and researchers all over the world using ever better computer technologies to understand and track vessel movements. This publication focuses on analysing vessels’ waiting times for berth at anchoring places near ports using the example of the port of Rotterdam, Europe’s biggest port. The objective is to define clearly the concept of waiting, i.e. when a vessel waits and when not, and to investigate the amount of waiting vessels and the respective waiting times during a time span of more than two years, using solely AIS data. The indicated anchoring zones in front of the port of Rotterdam, where vessels wait, are clearly detected by visualizing the analysed data. The results of the conducted AIS data analysis show significant differences in waiting times between different vessel types, as well as a correlation between the number of waiting vessels and the average waiting time. The in detail described data pre-processing and statistical analysis are extendable and applicable to other regions and ports all over the world. Additionally, the presented data pre-processing approach is an optimal basis analysis of current waiting conditions and for applying machine learning to AIS data in order to predict future waiting times
2013
[182359] |
Title: Investigation of Vessel Waiting Times Using AIS Data. <em>Dynamics in Logistics</em> |
Written by: Franzkeit, Janna and Pache, Hannah and Jahn, Carlos |
in: <em>LDIC 2020</em>. (2020). |
Volume: Number: |
on pages: 70-78 |
Chapter: |
Editor: In Freitag, Michael and Haasis, Hans-Dietrich and Kotzab, Herbert and Pannek, Jürgen (Eds.) |
Publisher: Springer International Publishing: |
Series: Lecture Notes in Logistics |
Address: Cham |
Edition: |
ISBN: 978-3-030-44782-3 |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/978-3-030-44783-0_7 |
URL: |
ARXIVID: |
PMID: |
Note: i3lab
Abstract: The automatic identification system (AIS) enables authorities, shipping companies and researchers all over the world using ever better computer technologies to understand and track vessel movements. This publication focuses on analysing vessels’ waiting times for berth at anchoring places near ports using the example of the port of Rotterdam, Europe’s biggest port. The objective is to define clearly the concept of waiting, i.e. when a vessel waits and when not, and to investigate the amount of waiting vessels and the respective waiting times during a time span of more than two years, using solely AIS data. The indicated anchoring zones in front of the port of Rotterdam, where vessels wait, are clearly detected by visualizing the analysed data. The results of the conducted AIS data analysis show significant differences in waiting times between different vessel types, as well as a correlation between the number of waiting vessels and the average waiting time. The in detail described data pre-processing and statistical analysis are extendable and applicable to other regions and ports all over the world. Additionally, the presented data pre-processing approach is an optimal basis analysis of current waiting conditions and for applying machine learning to AIS data in order to predict future waiting times