Evaluation of outlier detection algorithms for traffic congestion assessment in smart city traffic data from vehicle sensors
by Ramona Ruiz Blázquez; Mario Muñoz Organero; Luis Sánchez Fernández
International Journal of Heavy Vehicle Systems (IJHVS), Vol. 25, No. 3/4, 2018

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.

Online publication date: Mon, 24-Sep-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Heavy Vehicle Systems (IJHVS):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com