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Journal of Advanced Transportation
Volume 2017 (2017), Article ID 3802807, 11 pages
Research Article

Detecting Different Road Infrastructural Elements Based on the Stochastic Characterization of Speed Patterns

Telematics Engineering Department, Carlos III University of Madrid, Madrid, Spain

Correspondence should be addressed to Mario Muñoz-Organero

Received 31 March 2017; Revised 14 June 2017; Accepted 27 June 2017; Published 26 July 2017

Academic Editor: Sunder Lall Dhingra

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.


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 -NN approach is used for assigning a class to each unlabelled element.