A 3D Convolution-Incorporated Dimension Preserved Decomposition Model for Traffic Data Prediction

Traffic data prediction is a crucial component of Intelligent Transport Systems (ITS) as it contributes significantly to real-time navigation and congestion management. However, due to incomplete deployment of sensor and instability of data transmission, the traffic data that we collect is usually a high-dimensional and incomplete (HDI) matrix or tensor. Currently, there are two challenges with traffic data prediction as follows: a) Most of the existing models are designed for a full set of data, but traffic data are unavoidably missing. b) Most of the existing models often suffer from excessive complexity due to long sequences. To address these issues, we propose a novel 3D Convolution-Incorporated Dimension Preserved Decomposition (3DCIDP) model for traffic data prediction with three main fold ideas: a) enhancing the low-rank property of traffic data to accurately capture its structure, b) learning the constraints of historical sequences to predict sequences through representation modelling and c) capturing spatio-temporal interaction information in traffic data through multidimensional interaction features. To evaluate the performance of the proposed 3DCIDP, we conduct extensive experiments using five publicly available datasets with three different missing rates. When the proposed 3DCIDP is compared to state-of-the-art models, experimental results show that the Root Mean Square Error (RMSE) is reduced by an average of 4.22% and the training time is reduced by an average of 89.76% on the large-scale traffic datasets.