FR-MTL: Traffic Flow Prediction Using Fused Ridge Denoising and Multi-Task Learning

Abstract

Traffic flow prediction is one of the fundamental components in Intelligent Transportation Systems (ITS). Many traffic flow prediction models have been developed, but with limitation of noise sensitivity, which will result in poor generalization. Fused Lasso, also known as total variation denoising, penalizes L1-norm on the model coefficients and pairwise differences between neighboring coefficients, has been widely used to analyze highly correlated features with a natural order, as is the case with traffic flow. It denoises data by encouraging both sparsity of coefficients and their differences, and estimates the coefficients of highly correlated variables to be equal to each other. However, for traffic data, the same coefficients will lead to overexpression of features, and losing the trend of time series of traffic flow. In this work, we propose a Fused Ridge multi-task learning (FR-MTL) model for multi-road traffic flow prediction. It introduces Fused Ridge for traffic data denoising, imposes penalty on L2-norm of the coefficients and their differences. The penalty of L2-norm proportionally shrinks coefficients, and generates smooth coefficient vectors with non-sparsity. It has both capability of trend preservation and denoising. In addition, we jointly consider multi-task learning (MTL) for training shared spatiotemporal information among traffic roads. The experiments on real traffic data show the advantages of the proposed model over other four regularized baseline models, and on traffic data with Gaussian noise and missing data, the FR-MTL model demonstrates potential and promising capability with satisfying accuracy and effectiveness.

Short-term traffic flow prediction based on combination of SSA and LSTM

“…Polson et al [21] developed a traffic flows prediction model based on deep learning and found that the future traffic conditions are more similar to current ones as compared to those from previous days. Yang et al [22] proposed a Fused Ridge Multi-Task Learning (FR-MTL) model for forecasting traffic flow in multiple roads. Experimental results on actual traffic datasets confirm that the model has satisfying accuracy and effectiveness.…”