Spatial-temporal traffic outlier detection by coupling road level of service

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.