Compressed prediction of large-scale urban traffic

Traffic prediction lies at the core of many intelligent transport systems (ITS). Commonly deployed prediction methods such as support vector regression and neural networks achieve good performance by explicitly predicting the traffic variables (e.g., traffic speed or volume) at each road segment in the network. For large traffic networks, predicting traffic variable at each road segment may be unwieldy, especially in the setting of real-time prediction. To tackle this problem, we propose an alternative approach in this paper. We first generate low-dimensional representation of the network, leveraging on the column-based (CX) decomposition of matrices. The low-dimensional model represents the large network in terms of a small subset of road segments. The future state of the low-dimensional network is predicted by standard procedures, i.e., support vector regression. The future state of the entire network is then inferred by extrapolating the predictions of the subnetwork, using the CX decomposition. Numerical results for a large-scale road network in Singapore demonstrate the efficiency and accuracy of the proposed algorithm.