DC FieldValueLanguage
dc.contributor.authorFranzkeit, Janna-
dc.contributor.authorPache, Hannah-
dc.contributor.authorJahn, Carlos-
dc.date.accessioned2020-07-27T07:15:16Z-
dc.date.available2020-07-27T07:15:16Z-
dc.date.issued2020-04-16-
dc.identifier.citationInternational Conference on Dynamics in Logistics (LDIC 2020)de_DE
dc.identifier.isbn978-3-030-44782-3de_DE
dc.identifier.isbn978-3-030-44783-0de_DE
dc.identifier.urihttp://hdl.handle.net/11420/6902-
dc.description.abstractThe 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.en
dc.language.isoende_DE
dc.publisherSpringerde_DE
dc.subject.ddc330: Wirtschaftde_DE
dc.subject.ddc380: Handel, Kommunikation, Verkehrde_DE
dc.titleInvestigation of vessel waiting times using AIS datade_DE
dc.typeinProceedingsde_DE
dc.type.dinicontributionToPeriodical-
dcterms.DCMITypeText-
tuhh.abstract.englishThe 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.de_DE
tuhh.publisher.doi10.1007/978-3-030-44783-0_7-
tuhh.publication.instituteMaritime Logistik W-12de_DE
tuhh.type.opusInProceedings (Aufsatz / Paper einer Konferenz etc.)-
dc.type.drivercontributionToPeriodical-
dc.type.casraiConference Paper-
tuhh.container.startpage70de_DE
tuhh.container.endpage78de_DE
dc.relation.conference7th International Conference on Dynamics in Logistics (LDIC 2020)de_DE
dc.relation.projectI³-Lab - Business Analytics – Optimierungspotenziale und strategische Risiken für maritime logistische Systemede_DE
dc.identifier.scopus2-s2.0-85101978990-
local.status.inpressfalsede_DE
datacite.resourceTypeConference Paper-
datacite.resourceTypeGeneralText-
item.creatorOrcidFranzkeit, Janna-
item.creatorOrcidPache, Hannah-
item.creatorOrcidJahn, Carlos-
item.grantfulltextnone-
item.creatorGNDFranzkeit, Janna-
item.creatorGNDPache, Hannah-
item.creatorGNDJahn, Carlos-
item.mappedtypeinProceedings-
item.openairecristypehttp://purl.org/coar/resource_type/c_5794-
item.fulltextNo Fulltext-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairetypeinProceedings-
crisitem.project.funderTechnische Universität Hamburg-
crisitem.project.funderrorid04bs1pb34-
crisitem.author.deptMaritime Logistik W-12-
crisitem.author.deptMaritime Logistik W-12-
crisitem.author.deptMaritime Logistik W-12-
crisitem.author.orcid0000-0002-7726-3058-
crisitem.author.orcid0000-0001-8023-5755-
crisitem.author.orcid0000-0002-5409-0748-
crisitem.author.parentorgStudiendekanat Management-Wissenschaften und Technologie (W)-
crisitem.author.parentorgStudiendekanat Management-Wissenschaften und Technologie (W)-
crisitem.author.parentorgStudiendekanat Management-Wissenschaften und Technologie (W)-
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