Outlier Detection in Urban Traffic Data

Youcef Djenouri, Arthur Zimek

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearch


This paper provides a summary of the tutorial on outlier detection in urban traffic data. We present existing solutions in three main categories: Statistical techniques, similarity-based techniques, and techniques based on pattern analysis. The first category groups solutions employing statistical models to identify anomalies in traffic data. The second category groups solutions using distance measures and neighborhoods to derive local density estimates. The third category explores the correlation between traffic flow values by using concepts from pattern analysis. We explain and discuss example solutions for each category, and we outline perspectives on open questions and research challenges. We relate the solutions to a general view on the notion of locality, i.e., the context and reference used in the definition and comparison of outlierness, in order to gain a better understanding of the intuition, limitations, and benefits for the various outlier detection methods for urban traffic. This way, we hope to provide some guidance to practitioners for selecting the most suitable methods for their case.

Original languageEnglish
Title of host publicationProceedings of the 8th International Conference on Web Intelligence, Mining and Semantics, WIMS 2018
EditorsCostin Badica, Rajendra Akerkar, Mirjana Ivanovic, Milos Savic, Milos Radovanovic, Sang-Wook Kim, Riccardo Rosati, Yannis Manolopoulos
Number of pages12
PublisherAssociation for Computing Machinery
Publication date25. Jun 2018
Article number3
ISBN (Electronic)978-1-4503-5489-9
Publication statusPublished - 25. Jun 2018
Event8th International Conference on Web Intelligence, Mining and Semantics - Novi Sad, Serbia
Duration: 25. Jun 201827. Jun 2018


Conference8th International Conference on Web Intelligence, Mining and Semantics
CityNovi Sad
Internet address


  • Data mining
  • Outlier detection
  • Urban traffic data


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