期刊
IET INTELLIGENT TRANSPORT SYSTEMS
卷 13, 期 6, 页码 1016-1022出版社
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-its.2018.5214
关键词
road traffic; data mining; traffic engineering computing; hidden Markov models; coupled hidden Markov model outlier detection method; Poisson mixture model; traffic congestion level; road flow; traffic flow; traffic outliers; data mining; city management; road level; spatial-temporal traffic outlier detection; upstream roads; road traffic congestion estimation; finer-grained outlier detection method; traffic anomalies
资金
- National Key R&D Program of China [2017YFB1002000]
- National Natural Science Foundation of China [61502320]
- Science Technology and Innovation Commission of Shenzhen Municipality
- State Key Laboratory of Software Development Environment
- Aeronautical Science Foundation of China
Traffic outlier detection is an essential topic in city management and data mining. Most traffic outliers are caused by accidents, control, protests, disasters, and many other events. Recently, traffic outlier detection methods are based on counting traffic flow, where the detection is inherited from the periodical changes of road flow. However, these approaches fail to detect the impact from upstream roads. Furthermore, the mappings between traffic congestion level and traffic flow are distinct from road to road. In this study, a Poisson mixture model (PMM) - coupled hidden Markov model (CHMM) outlier detection method would be introduced for detecting traffic anomalies which are from taxi global positioning system data. To make a finer-grained outlier detection method, road traffic congestion estimation, as well as the impact from upstream roads, are considered. PMM serves as an estimator to determine the congestion level for every road and CHMM is used to couple the road's impact. The experiment applies both semi-synthetic and real outlier data from the Beijing map, and the results reveal the advantages of both datasets.
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