Publication: A Fast, Scalable, Unsupervised Approach to Real-time Traffic Incident Detection
Issued Date
2018-09-06
Resource Type
Other identifier(s)
2-s2.0-85057765047
Rights
Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
Proceeding of 2018 15th International Joint Conference on Computer Science and Software Engineering, JCSSE 2018. (2018)
Suggested Citation
Majeed Thaika, Songwong Tasneeyapant, Sunsern Cheamanunkul A Fast, Scalable, Unsupervised Approach to Real-time Traffic Incident Detection. Proceeding of 2018 15th International Joint Conference on Computer Science and Software Engineering, JCSSE 2018. (2018). doi:10.1109/JCSSE.2018.8457338 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/45576
Research Projects
Organizational Units
Authors
Journal Issue
Thesis
Title
A Fast, Scalable, Unsupervised Approach to Real-time Traffic Incident Detection
Other Contributor(s)
Abstract
© 2018 IEEE. Traffic congestion is occasionally caused by an unusual traffic incident such as a road accident or a big sporting event. The congestion could have been avoided if the traffic authority had detected and responded to it quickly and appropriately. This article explores a machine learning approach for detecting anomalous traffic incidents in real-time using GPS data collected from thousands of taxicabs in Bangkok Metropolitan area. The detection model is based on applying Principal Component Analysis (PCA) on various features extracted from overlapping fixed-length time windows over a target region. After the model has been trained, it is validated on past data and is able to discover meaningful anomalous incidents that have been verified by cross-checking with other information sources. Our approach does not require any street layout information, is computationally efficient, and can be deployed to monitor realtime traffic over large areas at scales.