https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Krisp_et_al_2018aKrisp et al 2018a - Revision history2026-05-06T15:15:05ZRevision history for this page on the wikiMediaWiki 1.27.0-wmf.10https://www.scipedia.com/wd/index.php?title=Krisp_et_al_2018a&diff=216748&oldid=prevScipediacontent: Scipediacontent moved page Draft Content 176125214 to Krisp et al 2018a2021-02-16T11:19:57Z<p>Scipediacontent moved page <a href="/public/Draft_Content_176125214" class="mw-redirect" title="Draft Content 176125214">Draft Content 176125214</a> to <a href="/public/Krisp_et_al_2018a" title="Krisp et al 2018a">Krisp et al 2018a</a></p>
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</td></tr></table>Scipediacontenthttps://www.scipedia.com/wd/index.php?title=Krisp_et_al_2018a&diff=216747&oldid=prevScipediacontent: Created page with " == Abstract == Traffic congestion in urban environments has severe influences on the daily life of people. Due to typical recurrent mobility patterns of commuters and transp..."2021-02-16T11:19:53Z<p>Created page with " == Abstract == Traffic congestion in urban environments has severe influences on the daily life of people. Due to typical recurrent mobility patterns of commuters and transp..."</p>
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== Abstract ==<br />
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Traffic congestion in urban environments has severe influences on the daily life of people. Due to typical recurrent mobility patterns of commuters and transport fleets, we can detect traffic congestion events on selected hours of the day, so called rush hours. Besides the mentioned recurrent traffic congestion, there are non-recurrent events that may be caused by accidents or newly established building sites. We want to inspect this appearance using a massive Floating Taxi Data (FTD) set of Shanghai from 2007. We introduce a simple method for detecting and extracting congestion events on selected rush hours and for distinguishing between their recurrence and non-recurrence. By preselecting of similar velocity and driving direction values of the nearby situated FTD points, we provide the first part for the Shared Nearest Neighbour (SNN) clustering method, which follows with a density-based clustering. After the definition of our traffic congestion clusters, we try to connect ongoing events by quer...<br />
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== Original document ==<br />
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The different versions of the original document can be found in:<br />
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* [http://dx.doi.org/10.1080/17489725.2017.1420256 http://dx.doi.org/10.1080/17489725.2017.1420256]<br />
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* [http://mediatum.ub.tum.de/node?id=1429275 http://mediatum.ub.tum.de/node?id=1429275],<br />
: [https://dblp.uni-trier.de/db/journals/jlbs/jlbs11.html#KelerKD17 https://dblp.uni-trier.de/db/journals/jlbs/jlbs11.html#KelerKD17],<br />
: [https://www.tandfonline.com/doi/full/10.1080/17489725.2017.1420256 https://www.tandfonline.com/doi/full/10.1080/17489725.2017.1420256],<br />
: [https://academic.microsoft.com/#/detail/2782194476 https://academic.microsoft.com/#/detail/2782194476]<br />
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* [https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/50164 https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/50164],<br />
: [https://doi.org/10.1080/17489725.2017.1420256 https://doi.org/10.1080/17489725.2017.1420256]<br />
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* [https://www.tandfonline.com/doi/pdf/10.1080/17489725.2017.1420256 https://www.tandfonline.com/doi/pdf/10.1080/17489725.2017.1420256],<br />
: [http://dx.doi.org/10.1080/17489725.2017.1420256 http://dx.doi.org/10.1080/17489725.2017.1420256]</div>Scipediacontent