Detecting Urban Anomalies Using Factor Analysis and One Class Support Vector Machine

Abstract

The detection of anomalies in spatiotemporal traffic data is not only critical for intelligent transportation systems and public safety but also very challenging. Anomalies in traffic data often exhibit complex forms in two aspects, (i) spatiotemporal complexity (i.e. we need to associate individual locations and time intervals formulating a panoramic view of an anomaly) and (ii) multi-source complexity (i.e. we need an algorithm that can model the anomaly degree of the multiple data sources of different densities, distributions and scales). To tackle these challenges, we proposed a three-step method that uses factor analysis to extract features, then uses the goodness-of-fit test to obtain the anomaly score of a single data point and then uses one class support vector machine to synthesize the anomaly score. Finally, we conduct extensive experiments on real-world trip data include taxi and bike data. And these extensive experiments demonstrate the effectiveness of our proposed approach.

Disentangled Dynamic Deviation Transformer Networks for Multivariate Time Series Anomaly Detection

“…Furthermore, we discovered that it is challenging to utilize it to determine the underlying cause of traffic anomalies since the reason for the anomalies expresses itself in dynamic changes in urban traffic, which is reflected in multiple urban traffic data. In order to find the core cause of anomalous traffic events, it is frequently necessary to infer plausible causal relationships from several traffic data sources spanning time and geography [ 50 ]. This challenge has been partially solved by modeling inter-sensor and temporal interdependence together.…”

MTGAE: Graph Autoencoder with Mirror TCN for Traffic Anomaly Detection

“…And learning the relationship of inter-sensor allows us to know which sensor is abnormal and which aspects deviate from normal behavior 4,5 . However, traffic anomalies usually exhibit complex forms due to two aspects: high dimensionality, sparsity, abnormal scarcity (i.e., the need to correlate time and space, including speed or flow), and difficulty in capturing the hidden relationship between nodes (i.e., spatial modeling in the face of different data sources with varying degrees of anomalies in density or distribution and scale) 6 . Thus, how to capture complex inter-sensor relationships and detect anomalies from node relationships is important.…”