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Online hot spot prediction in road networks

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dc.contributor.authorHäsner, Maik
dc.contributor.authorJunghans, Conny
dc.contributor.authorSengstock, Christian
dc.contributor.authorGertz, Michael
dc.contributor.editorHärder, Theo
dc.contributor.editorLehner, Wolfgang
dc.contributor.editorMitschang, Bernhard
dc.contributor.editorSchöning, Harald
dc.contributor.editorSchwarz, Holger
dc.date.accessioned2019-01-17T10:36:44Z
dc.date.available2019-01-17T10:36:44Z
dc.date.issued2011
dc.description.abstractAdvancements in GPS-technology have spurred major research and development activities for managing and analyzing large amounts of position data of mobile objects. Data mining tasks such as the discovery of movement patterns, classification and outlier detection in the context of object trajectories, and the prediction of future movement patterns have become basic tools in extracting useful information from such position data. Especially the prediction of future movement patterns of vehicles, based on historical or recent position data, plays an important role in traffic management and planning. In this paper, we present a new approach for the online prediction of so-called hot spots, that is, components of a road network such as intersections that are likely to experience heavy traffic in the near future. For this, we employ an efficient path prediction model for vehicle movements that only utilizes a few recent position data. Using an aggregation model for hot spots, we show how regional information can be derived and connected substructures in a road network can be determined. Utilizing the behavior of such hot spot regions over time in terms of movement or growth, we introduce different types of hot spots and show how they can be determined online. We demonstrate the effectiveness of our approach using a real large-scale road network and different traffic simulation scenarios.en
dc.identifier.isbn978-3-88579-274-1
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/19579
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofDatenbanksysteme für Business, Technologie und Web (BTW)
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-180
dc.titleOnline hot spot prediction in road networksen
dc.typeText/Conference Paper
gi.citation.endPage206
gi.citation.publisherPlaceBonn
gi.citation.startPage187
gi.conference.date02.-04.03.2011
gi.conference.locationKaiserslautern
gi.conference.sessiontitleRegular Research Papers

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