Attention Mechanism Based Multi-task Learning Framework for Transportation Time Prediction

Transportation time prediction (TIP) of a truck is one of key tasks for supporting the services in bulk logistics like route planning. But TIP prediction is challenging as it involves travel time prediction and dwell time prediction, which are influenced by various complex factors. Besides, there exists mutually constrained effects between travel time prediction and dwell time prediction. In this paper, we propose an Attention Mechanism based Multi-Task prediction framework consisting of travel pattern learning, stay pattern learning and transportation time modeling, called AMP. In view of that low prediction performance resulted by uncertain dwell time and mutually constrained effects between travel time and dwell time, we put forward a stay pattern learning module based on transformer and multi-factor attention mechanism. Furthermore, we design a multi-task learning based prediction module embedded with a mutual cross-attention mechanism to enhance overall prediction performance. Experimental results on a large-scale logistics data set demonstrate that our proposal can reduce MAPE by an average of 9.2%, MAE by an average of 19.5%, and RMSE by an average of 23.0% as compared to the baselines.