Traffic flow and speed forecasting through a Bayesian deep multi-linear relationship network

Both traffic flow and speed forecasting are of great importance to intelligent transportation systems, which have been studied intensely in the past decades. In recent years, forecasting approaches based on deep learning have attracted the most extensive attention. However, most studies only predict traffic flow and speed separately, without taking advantage of the correlation between the two tasks. Unlike single-task learning, multi-task learning (MTL) approaches can learn related tasks simultaneously. MTL enhances the learning of each task by exploiting useful information from other related tasks. Nevertheless, traditional MTL methods only share transferable features at the feature layers and simply make an assumption that given the shared representation, the multiple tasks can be well correlated with each other, which leads to under-transfer and negative-transfer. In this paper, we propose a new MTL approach to forecast both traffic flow and speed, which shares transferable features at feature layers but learns the correlation between tasks at task-specific layers. By learning the multi-linear relationships between tasks and features as well as the transferable features, the proposed approach can mitigate the above-mentioned issues. Comprehensive experiments from a case study based on real data show that the proposed method steadily achieves the best forecasting results and good robustness.