Anomaly Detection for Road Traffic: A Visual Analytics Framework

被引：63

作者：

Riveiro, Maria ^{[1
]}

Lebram, Mikael ^{[1
]}

Elmer, Marcus ^{[2
]}

机构：

[1] Univ Skovde, Sch Informat, SE-54128 Skovde, Sweden

[2] Volvo Grp Trucks Technol GTT, Adv Technol & Res, SE-40508 Gothenburg, Sweden

来源：

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS | 2017年 / 18卷 / 08期

关键词：

Anomaly detection; visual analytics; normal traffic model; intelligent transport systems; MODEL; VISUALIZATION; ACCIDENTS; MOVEMENT;

D O I：

10.1109/TITS.2017.2675710

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

The analysis of large amounts of multidimensional road traffic data for anomaly detection is a complex task. Visual analytics can bridge the gap between computational and human approaches to detecting anomalous behavior in road traffic, making the data analysis process more transparent. In this paper, we present a visual analytics framework that provides support for: 1) the exploration of multidimensional road traffic data; 2) the analysis of normal behavioral models built from data; 3) the detection of anomalous events; and 4) the explanation of anomalous events. We illustrate the use of this framework with examples from a large database of real road traffic data collected from several areas in Europe. Finally, we report on feedback provided by expert analysts from Volvo Group Trucks Technology, regarding its design and usability.

引用

页码：2260 / 2270

页数：11

共 47 条

[1] A conceptual framework and taxonomy of techniques for analyzing movement [J].

Andrienko, G. ;

Andrienko, N. ;

Bak, P. ;

Keim, D. ;

Kisilevich, S. ;

Wrobel, S. .

JOURNAL OF VISUAL LANGUAGES AND COMPUTING, 2011, 22 (03) :213-232

[2]

Andrienko Gennady, 2010, 2010 Proceedings of IEEE Symposium on Visual Analytics Science and Technology (VAST 2010), P59, DOI 10.1109/VAST.2010.5652478

[3] An event-based conceptual model for context-aware movement analysis [J].