Detecting different road infrastructural elements based on the stochastic characterization of speed patterns

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dc.contributor.author Muñoz Organero, Mario
dc.contributor.author Ruiz Blázquez, Ramona
dc.date.accessioned 2018-01-29T13:28:21Z
dc.date.available 2018-01-29T13:28:21Z
dc.date.issued 2017-07-26
dc.identifier.bibliographicCitation Journal of Advanced Transformation, vol. 2017 (2017), article ID 3802807, 11 pages.
dc.identifier.issn 0197-6729
dc.identifier.uri http://hdl.handle.net/10016/26165
dc.description.abstract The automatic detection of road related information using data from sensors while driving has many potential applications such as traffic congestion detection or automatic routable map generation. This paper focuses on the automatic detection of road elements based on GPS data from on-vehicle systems. A new algorithm is developed that uses the total variation distance instead of the statistical moments to improve the classification accuracy. The algorithm is validated for detecting traffic lights, roundabouts, and street-crossings in a real scenario and the obtained accuracy (0.75) improves the best results using previous approaches based on statistical moments based features (0.71). Each road element to be detected is characterized as a vector of speeds measured when a driver goes through it. We first eliminate the speed samples in congested traffic conditions which are not comparable with clear traffic conditions and would contaminate the dataset. Then, we calculate the probability mass function for the speed (in 1 m/s intervals) at each point. The total variation distance is then used to find the similarity among different points of interest (which can contain a similar road element or a different one). Finally, a k-NN approach is used for assigning a class to each unlabelled element.
dc.description.sponsorship The research leading to these results has received funding from the “HERMES-Smart Driver” Project TIN2013-46801-C4-2-R (MINECO), funded by the Spanish Agencia Estatal de Investigación (AEI), and the “Analytics Using Sensor Data for Flatcity” Project TIN2016-77158-C4-1-R (MINECO/ERDF, EU) funded by the SpanishAgencia Estatal de Investigación (AEI) and the European Regional Development Fund (ERDF).
dc.format.extent 11
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Hindawi
dc.rights Copyright © 2017 Mario Muñoz-Organero and Ramona Ruiz-Blázquez. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Traffic engineering
dc.subject.other Intelligent transportation systems
dc.subject.other Traffic speed data
dc.subject.other Sensors
dc.subject.other GPS
dc.subject.other Stochastic processes
dc.title Detecting different road infrastructural elements based on the stochastic characterization of speed patterns
dc.type article
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1155/2017/3802807
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TIN2013-46801-C4-2-R
dc.relation.projectID Gobierno de España. TIN2016-77158-C4-1-R
dc.type.version publishedVersion
dc.identifier.publicationtitle Journal of Advanced Transformation
dc.identifier.publicationvolume 2017 (3802807)
dc.identifier.uxxi AR/0000020456
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