Title: Evaluation of outlier detection algorithms for traffic congestion assessment in smart city traffic data from vehicle sensors

Authors: Ramona Ruiz Blázquez; Mario Muñoz Organero; Luis Sánchez Fernández

Addresses: Department of Telematic Engineering, Carlos III University of Madrid, Av. Universidad 30, 28911, Leganés, Madrid, Spain ' Department of Telematic Engineering, Carlos III University of Madrid, Av. Universidad 30, 28911, Leganés, Madrid, Spain ' Department of Telematic Engineering, Carlos III University of Madrid, Av. Universidad 30, 28911, Leganés, Madrid, Spain

Abstract: 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.

Keywords: multivariate outliers; traffic jams; outlier detection methods; vehicles telemetry; machine learning.

DOI: 10.1504/IJHVS.2018.094826

International Journal of Heavy Vehicle Systems, 2018 Vol.25 No.3/4, pp.308 - 321

Received: 02 May 2017
Accepted: 03 Jul 2017
Published online: 24 Sep 2018 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article