Traffic station classification based on deep spatio-temporal network. (January 2022)

• Record Type: Journal Article
• Authors: Hu, Zhiqiu; Sun, Rencheng; Shao, Fengjing; Sui, Yi; Lv, Zhihan
• Is Part Of: Computers & electrical engineering. Volume 97(2022)
• Publication Date: 2022-01
• Subjects: Traffic station classification -- Deep learning -- Dilated convolution -- Long short-term memory -- Attention mechanism; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107558
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Employing existing traffic data for the accurate classification of roads can provide significant references for the planning and construction of urban traffic infrastructure. This paper devises a novel deep learning framework for traffic station classification (DeepTSC) which classifies the traffic monitoring stations to in turn classify the road where these stations are located. The monitoring data of these traffic stations are interdependent with regards to both their spatial and temporal dimensions. We design a one-dimensional convolution layer for preliminary feature extraction and feature fusion. Following this, we employ a hybrid combination of the multilayer dilated convolution, long short-term memory network (LSTM) and attention mechanism, to extract the spatio-temporal features of the traffic data. Experiments on a public California freeway dataset show that the DeepTSC model's classification results outperform other existing state-of-the-art methods, with its accuracy being at least 1.4% higher than other models.