Perceiving spatiotemporal traffic anomalies from sparse representation-modeled city dynamics
| Autor: | Jun Gao, Su Yang, Daqing Zheng |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
media_common.quotation_subject Anomaly (natural sciences) Mobile computing 020206 networking & telecommunications 02 engineering and technology Sparse approximation Management Science and Operations Research Library and Information Sciences Traffic flow computer.software_genre Computer Science Applications Emergency response Hardware and Architecture Dynamics (music) 020204 information systems Perception 0202 electrical engineering electronic engineering information engineering Predictive power Data mining computer media_common |
| Zdroj: | Personal and Ubiquitous Computing. 27:647-660 |
| ISSN: | 1617-4917 1617-4909 |
| DOI: | 10.1007/s00779-020-01474-4 |
| Popis: | Early perception of anomaly traffic patterns, both spatially and temporally, is of importance for emergency response in the smart cities. To capture the spatiotemporal correlations among traffic flows for city dynamics modeling in correspondence with normal states, we conduct sparse representation on taxi activity over spatially partitioned cells in a city. We can perceive the deviation from the normal evolution of traffic flows and find the traffic anomalies. This method roots in the ideal of global traffic flow network detection. Therefore, it is more informative than local statistics since traffic flows evolve in a mutually interacting manner to spread out all over the city. The experimental results confirm its predictive power in detecting spatiotemporal traffic anomalies. |
| Databáze: | OpenAIRE |
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