scholarly journals Road Traffic Anomaly Detection via Collaborative Path Inference from GPS Snippets

Sensors ◽  
2017 ◽  
Vol 17 (3) ◽  
pp. 550 ◽  
Author(s):  
Hongtao Wang ◽  
Hui Wen ◽  
Feng Yi ◽  
Hongsong Zhu ◽  
Limin Sun
Keyword(s):  
Anomaly Detection ◽  
Road Traffic ◽  
Traffic Anomaly ◽  
Traffic Anomaly Detection

scholarly journals Road Traffic Anomaly Detection Based on Fuzzy Theory

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 40281-40288 ◽  
Author(s):  
Yanshan Li ◽  
Tianyu Guo ◽  
Rongjie Xia ◽  
Weixin Xie
Keyword(s):  
Anomaly Detection ◽  
Road Traffic ◽  
Fuzzy Theory ◽  
Traffic Anomaly ◽  
Traffic Anomaly Detection

scholarly journals Spatiotemporal Road Traffic Anomaly Detection: A Tensor-Based Approach

2021 ◽  
Vol 11 (24) ◽  
pp. 12017
Author(s):  
Leo Tišljarić ◽  
Sofia Fernandes ◽  
Tonči Carić ◽  
João Gama
Keyword(s):  
Anomaly Detection ◽  
Urban Areas ◽  
Road Network ◽  
Road Traffic ◽  
Urban Traffic ◽  
Traffic Patterns ◽  
Transition Matrices ◽  
On The Road ◽  
Traffic Anomaly ◽  
Traffic Anomaly Detection

The increased development of urban areas results in a larger number of vehicles on the road network, leading to traffic congestion, which often leads to potentially dangerous situations that can be described as anomalies. The tensor-based methods emerged only recently in applications related to traffic anomaly detection. They outperform other models regarding simultaneously capturing spatial and temporal components, which are of immense importance in traffic dataset analysis. This paper presents a tensor-based method for extracting the spatiotemporal road traffic patterns represented with the speed transition matrices, with the goal of anomaly detection. A novel anomaly detection approach is presented, which relies on computing the center of mass of the observed traffic patterns. The method was evaluated on a large road traffic dataset and was able to detect the most anomalous parts of the urban road network. By analyzing spatial and temporal components of the most anomalous traffic patterns, sources of anomalies can be identified. Results were validated using the extracted domain knowledge from the Highway Capacity Manual. The anomaly detection model achieved a precision score of 92.88%. Therefore, this method finds its usages for safety experts in detecting potentially dangerous road segments, urban traffic planners, and routing applications.

Robust feature selection and robust PCA for internet traffic anomaly detection

2012 ◽  
Author(s):  
Claudia Pascoal ◽  
M. Rosario de Oliveira ◽  
Rui Valadas ◽  
Peter Filzmoser ◽  
Paulo Salvador ◽  
...  
Keyword(s):  
Feature Selection ◽  
Anomaly Detection ◽  
Internet Traffic ◽  
Robust Pca ◽  
Traffic Anomaly ◽  
Traffic Anomaly Detection
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