LAUSR

Analyzing the traffic state of large citywide networks is an inherently difficult task. Various data issues, traffic signals, stops signs and other flow inhibitors of the network-level traffic state make the analysis more difficult than that under the small-scale local traffic state. To address this challenge, we propose a method based on spatio-temporal non-negative matrix factorization (ST-NMF), which is used for road network traffic pattern analysis. The method can be further extended to traffic data reconstruction and traffic prediction. In order to analyze traffic patterns, the proposed spatio-temporal non-negative matrix factorization model represents the network traffic as a linear combination of several basic patterns, which is also interpreted as the dynamics of spatial traffic characteristics over time in low-dimensional space. By the visual display of the spatial and temporal patterns and the assistance of clustering methods, the traffic pattern features are extracted. In the extended applications, data reconstruction relies on the sampling representation of missing data by ST-NMF, and data prediction is based on the prediction of the temporal patterns by ST-NMF. Through our method, we can not only obtain a high-quality data foundation, but also explore typical spatio-temporal patterns and general predictions of the future traffic state. The analysis results have important guiding significance on the management of intelligent transportation systems. Experiments on real-world traffic data are provided to verify the validity of our proposed approach.