Nonrecurrent traffic congestion detection with a coupled scalable Bayesian robust tensor factorization model

Abstract

Nonrecurrent traffic congestion (NRTC) usually brings unexpected delays to commuters. Hence, it is critical to accurately detect and recognize the NRTC in a real-time manner. The advancement of road traffic detectors provides researchers with a large-scale multivariable temporal-spatial traffic data, which allows the deep research on NRTC to be conducted. However, it remains a challenging task to construct an analytical framework through which the natural temporal-spatial structural properties of multivariable traffic information can be effectively represented and exploited to better understand and detect NRTC. In this paper, we present a novel analytical training-free framework based on the coupled scalable Bayesian robust tensor factorization (Coupled SBRTF). The framework can couple multivariable traffic variables including traffic flow, road speed, and occupancy through sharing the same sparse structure. Moreover, it naturally captures the high-dimensional temporal-spatial patterns of the traffic data by tensor factorization. With its entries revealing the distribution and magnitude of NRTC, the shared sparse structure of the framework compasses sufficiently abundant information about NRTC. While the low-rank part of the framework, expresses the distribution of general expected traffic conditions as an auxiliary product. Experimental results on real-world traffic data show that the proposed method outperforms the NRTC detection models based on the coupled Bayesian robust principal component analysis (coupled BRPCA), the rank sparsity tensor decomposition (RSTD), and standard normal deviates (SND). The proposed method performs even better when only traffic data in weekdays are utilized, and hence can provide more precise estimations of NRTC for daily commuters.