Elias, D, Nadler, F, Cornwell, I et al. (2 more authors) (2016) UNIETD – Assessment of Third Party Data as Information Source for Drivers and Road Operators. Transportation Research Procedia, 14. pp. 2035-2043. ISSN 2352-1465
Abstract
The paper deals with the assessment of third party data such as crowd sourced/social media and floating vehicle data as information source for road operators in addition to traditional infrastructure-based techniques. For purposes of quality assessment of different types of data and available ground truths existing test/evaluation methodologies have been assessed. A new methodology has been designed for assessment of speeds and travel times using normalized (between 0 and 1) quality indicators that can distinguish between “detection rate” and “false alarm rate” concepts. In terms of harvesting social media the relevance of social media content has been assessed against a range of traffic management requirements. Furthermore the level of content that will be available has been estimated as well as commercial sources and business models for road authorities. Analyses cover unstructured data from Twitter and Facebook both historical data and three months of contemporary data. In addition surveys are conducted in England and Austria to retrieve information from the public in terms of which social media platforms are commonly used to share information about traffic related incidents.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | social media; traffic information; data fusion; traffic prediction |
Dates: |
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Institution: | The University of Leeds |
Depositing User: | Symplectic Publications |
Date Deposited: | 06 Sep 2016 12:29 |
Last Modified: | 05 Oct 2017 16:12 |
Published Version: | https://doi.org/10.1016/j.trpro.2016.05.171 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.trpro.2016.05.171 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:104416 |