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Traffic State Estimation and Anomaly Detection Using Tensor-Based Method

The increased development of the urban areas consequently results in a larger number of vehicles on the road network, leading to traffic congestion, especially in the rush hours. Intelligent Transport Systems (ITS) solutions present the applications that can be useful in detecting and dealing with the problems that are related to congestion. This paper presents a method for traffic state estimation and anomaly detection on urban roads using the tensor decomposition method. The method is composed of several steps: (i) computation of the Speed Transition Matrices (STMs) for every observed transition on the observed traffic road network, (ii) dividing the observed area (the city) using the grid, (iii) composing the tensor for every cell in the grid, (iv) tensor decomposition to extract the traffic patterns and its spatial and temporal characteristics, (v) classification of the traffic patterns to estimate the traffic state and (vi) clustering of the characteristic spatiotemporal patterns.

Road traffic anomaly detection ; Tensor decomposition methods ; Speed probability distribution ; Intelligent transport systems ; Traffic state estimation

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