RT Journal Article
T1 Evaluation of outliers detection algorithms for traffic congestion assessment in smart city traffic data from vehicle sensors
A1 Ruiz Blázquez, Ramona
A1 Muñoz Organero, Mario
A1 Sánchez Fernández, Luis
AB On-board sensors in vehicles are able to capture real-time data representations of variables conditioning the traffic flow. Extracting knowledge by combining data from different vehicles, together with machine learning algorithms, will help both to optimise transportation systems and to maximise the drivers' and passengers' comfort. This paper provides a summary of the most common multivariate outlier detection methods and applies them to data captured from sensor vehicles with the aim to find and identify different abnormal driving conditions like traffic jams. Outlier detection represents an important task in discovering useful and valuable information, as has been proven in numerous researches. This study is based on the combination of outlier detection mechanisms together with data classification methods. The output of the outlier detection phase will then be fed into several classifiers, which have been implemented to assess if the multivariate outliers correspond with traffic congestion situations or not.
PB Inderscience
SN 1351-7848
SN 1741-5152 (online)
YR 2018
FD 2018-09-15
LK https://hdl.handle.net/10016/27543
UL https://hdl.handle.net/10016/27543
LA eng
NO The research leading to these results has received funding from the ‘1HERMESSMARTDRIVER’ project TIN2013-46801-C4-2-R (MINECO), funded by the SpanishAgencia Estatal de Investigación (AEI), and the ‘ANALYTICS USING SENSOR DATAFOR FLATCITY’ project TIN2016-77158-C4-1-R (MINECO/ERDF, EU) funded by theSpanish Agencia Estatal de Investigación (AEI) and the European Regional DevelopmentFund (ERDF). And the first author was supported by the MINECO Grant BES-2014-070462.
DS e-Archivo
RD 30 jun. 2024