Neural Network Multitask Learning for Traffic Flow Forecasting
This is an incremental improvement for traffic management systems, potentially enhancing prediction accuracy.
The paper tackles traffic flow forecasting by applying multitask learning (MTL) in neural networks, showing it improves generalization over single-task learning (STL) through experiments on urban vehicular traffic data.
Traditional neural network approaches for traffic flow forecasting are usually single task learning (STL) models, which do not take advantage of the information provided by related tasks. In contrast to STL, multitask learning (MTL) has the potential to improve generalization by transferring information in training signals of extra tasks. In this paper, MTL based neural networks are used for traffic flow forecasting. For neural network MTL, a backpropagation (BP) network is constructed by incorporating traffic flows at several contiguous time instants into an output layer. Nodes in the output layer can be seen as outputs of different but closely related STL tasks. Comprehensive experiments on urban vehicular traffic flow data and comparisons with STL show that MTL in BP neural networks is a promising and effective approach for traffic flow forecasting.