Abstract
Traffic dynamics in the urban interstate system are critical in terms of highway safety and mobility. This paper proposes a systematic data mining technique to detect traffic system-level anomalies in a batch-processing fashion. Built on the concepts of symbolic dynamics, a spatiotemporal pattern network (STPN) architecture is developed to capture the system characteristics. This novel spatiotemporal graphical modeling approach is shown to be able to extract salient time series features and discover spatial and temporal patterns for a traffic system. An information-theoretic metric is used to quantify the causal relationships between sub-systems. By comparing the structural similarity of the information-theoretic metrics of the STPNs learnt from each day, a day with anomalous system characteristics can be identified. A case study is conducted on an urban interstate in Iowa, USA, with 11 roadside radar sensors collecting 20-second resolution speed and volume data. After applying the proposed methods on one-month data (Feb. 2017), several system-level anomalies are detected. The potential causes that include inclement weather condition and non-recurring congestion are also verified to demonstrate the efficacies of the proposed technique. Compared to the traditional predefined performance measures for the traffic systems, the proposed framework has advantages in capturing spatiotemporal features in a fast and scalable manner.
Highlights
IntroductionBoth temporal and spatial relationships that exist among multiple
Traffic systems are complex, interactive and dynamic
Since traffic system is closely related to the physical world, to reflect the relationship between traffic data and public knowledge, a customized uniform partitioning (UP) was proposed to transform all the time series into symbol sequences with 6 partitions
Summary
Both temporal and spatial relationships that exist among multiple. Attributes and different sub-systems in a traffic system need to be extracted for effective performance monitoring. Margreiter (2016) used Bluetooth reidentification techniques to estimate travel time and further detected congestion/incident by a thresholding method. The authors used 80 km/h as speed threshold for warning and combined both number of warnings and 60 km/h speed threshold to detect incidents. Chakraborty, Hess, Sharma and Knickerbocker (2017) used an outlier-based method to explore more from historical data set up a dynamic threshold of speed for detection. Other than threshold-based method, Tang and Gao (2005) proposed a combined method of the nonparametric regression and standard deviation algorithm to detect incidents and tested it in simulation. Other than threshold-based method, Tang and Gao (2005) proposed a combined method of the nonparametric regression and standard deviation algorithm to detect incidents and tested it in simulation. Jin and Ran (2009) utilized the fundamental diagrams in traffic flow theory to identify the freeway incidents, and improved it by introducing uncongested and congested regime shifts in the diagrams
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