https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Lisner_et_al_2018aLisner et al 2018a - Revision history2026-04-30T20:00:40ZRevision history for this page on the wikiMediaWiki 1.27.0-wmf.10https://www.scipedia.com/wd/index.php?title=Lisner_et_al_2018a&diff=195742&oldid=prevScipediacontent: Scipediacontent moved page Draft Content 475561608 to Lisner et al 2018a2021-01-28T23:28:12Z<p>Scipediacontent moved page <a href="/public/Draft_Content_475561608" class="mw-redirect" title="Draft Content 475561608">Draft Content 475561608</a> to <a href="/public/Lisner_et_al_2018a" title="Lisner et al 2018a">Lisner et al 2018a</a></p>
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</td></tr></table>Scipediacontenthttps://www.scipedia.com/wd/index.php?title=Lisner_et_al_2018a&diff=195741&oldid=prevScipediacontent: Created page with " == Abstract == Good transportation planning requires reliable data. Nowadays many smartphone users record their routes and submit these GPS tracks to servers of smartphone a..."2021-01-28T23:28:07Z<p>Created page with " == Abstract == Good transportation planning requires reliable data. Nowadays many smartphone users record their routes and submit these GPS tracks to servers of smartphone a..."</p>
<p><b>New page</b></p><div><br />
== Abstract ==<br />
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Good transportation planning requires reliable data. Nowadays many smartphone users record their routes and submit these GPS tracks to servers of smartphone application operators. These aggregate tracks are the base for a bunch of tools to close the gap in bicycle planning and evaluation. However, there is only few information about the app users. Therefore the question is if it is possible to derive predictions from the app data that are valid compared to field data. An analysis of field data collections in Dresden with a dataset collected by the smartphone app Strava with an overall of 3,200 cyclists and 70,500 rides was undertaken. The comparison focused on traffic volumes, speed and origin-destination matrices. Overall, the predicted values based on the Strava app sample were comparable to the permanent counting devices, especially in areas with higher traffic flow. Strava app data is with some limitations applicable for bicycle planning. Recommendations for the future use of Strava and similar data sources for bicycle planning and transportation research will be discussed.<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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* [https://zenodo.org/record/2547479 https://zenodo.org/record/2547479] under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode<br />
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* [https://zenodo.org/record/2547479 https://zenodo.org/record/2547479],<br />
: [http://dx.doi.org/10.5281/zenodo.2547478 http://dx.doi.org/10.5281/zenodo.2547478] under the license https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode<br />
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* [https://zenodo.org/record/2547479 https://zenodo.org/record/2547479],<br />
: [http://dx.doi.org/10.5281/zenodo.2547479 http://dx.doi.org/10.5281/zenodo.2547479] under the license https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode<br />
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DOIS: 10.5281/zenodo.2547479 10.5281/zenodo.2547478</div>Scipediacontent