Abstract
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 language | English |
|---|---|
| Title of host publication | Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics, WIMS 2018 |
| Editors | Costin Badica, Rajendra Akerkar, Mirjana Ivanovic, Milos Savic, Milos Radovanovic, Sang-Wook Kim, Riccardo Rosati, Yannis Manolopoulos |
| Number of pages | 12 |
| Publisher | Association for Computing Machinery |
| Publication date | 25 Jun 2018 |
| Article number | 3 |
| ISBN (Electronic) | 978-1-4503-5489-9 |
| DOIs | |
| Publication status | Published - 25 Jun 2018 |
| Event | 8th International Conference on Web Intelligence, Mining and Semantics - Novi Sad, Serbia Duration: 25 Jun 2018 → 27 Jun 2018 https://wims2018.pmf.uns.ac.rs/ |
Conference
| Conference | 8th International Conference on Web Intelligence, Mining and Semantics |
|---|---|
| Country | Serbia |
| City | Novi Sad |
| Period | 25/06/2018 → 27/06/2018 |
| Internet address |
Fingerprint
Keywords
- Data mining
- Outlier detection
- Urban traffic data
Cite this
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Outlier Detection in Urban Traffic Data. / Djenouri, Youcef; Zimek, Arthur.
Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics, WIMS 2018. ed. / Costin Badica; Rajendra Akerkar; Mirjana Ivanovic; Milos Savic; Milos Radovanovic; Sang-Wook Kim; Riccardo Rosati; Yannis Manolopoulos. Association for Computing Machinery, 2018. 3.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research
TY - GEN
T1 - Outlier Detection in Urban Traffic Data
AU - Djenouri, Youcef
AU - Zimek, Arthur
PY - 2018/6/25
Y1 - 2018/6/25
N2 - 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.
AB - 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.
KW - Data mining
KW - Outlier detection
KW - Urban traffic data
U2 - 10.1145/3227609.3227692
DO - 10.1145/3227609.3227692
M3 - Article in proceedings
BT - Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics, WIMS 2018
A2 - Badica, Costin
A2 - Akerkar, Rajendra
A2 - Ivanovic, Mirjana
A2 - Savic, Milos
A2 - Radovanovic, Milos
A2 - Kim, Sang-Wook
A2 - Rosati, Riccardo
A2 - Manolopoulos, Yannis
PB - Association for Computing Machinery
ER -