Spatio-temporal Anomaly Detection in Intelligent Transportation Systems
| Autor: | Mai H. Hassan, Alberto Leon-Garcia, Ali Tizghadam |
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| Rok vydání: | 2019 |
| Předmět: |
Scheme (programming language)
Computer science Data stream mining 020206 networking & telecommunications 02 engineering and technology Flow network computer.software_genre Section (archaeology) 0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences 020201 artificial intelligence & image processing Anomaly detection Data mining Road traffic Intelligent transportation system computer General Environmental Science computer.programming_language |
| Zdroj: | ANT/EDI40 |
| ISSN: | 1877-0509 |
| Popis: | With the increased availability of real-time data streams in different domains comes the opportunity of using this data to provide valuable insights into the performance of the systems generating such data. In this paper, we are proposing an anomaly detection method to be applied on road traffic data in intelligent transportation systems. The proposed scheme is based on multi-channel singular spectrum analysis (MSSA), and aims to characterize the spatio-temporal properties of the transportation network. By simultaneously analyzing the spatial and temporal attributes of the network, the proposed anomaly detection scheme is able to detect contextual and collective anomalies that are otherwise undetectable using only spatial or temporal anomaly detection techniques. This is indicated through the results shown in the experiments and results section. |
| Databáze: | OpenAIRE |
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