Predict Vessel Traffic with Weather Conditions Based on Multimodal Deep Learning
Hu Xiao, Yan Zhao, Hao Zhang
Abstract
Vessel management calls for real-time traffic flow prediction, which is difficult under complex circumstances (incidents, weather, etc.). In this paper, a multimodal learning method named Prophet-and-GRU (P&G) considering weather conditions is proposed. This model can learn both features of the long-term and interdependence of multiple inputs. There are three parts of our model: first, the Decomposing Layer uses an improved Seasonal and Trend Decomposition Using Loess (STL) based on Prophet to decompose flow data; second, the Processing Layer uses a Sequence2Sequence (S2S) module based on Gated Recurrent Units (GRU) and attention mechanism with a special mask to extract nonlinear correlation features; third, the Joint Predicting Layer produces the final prediction result. The experimental results show that the proposed model predicts traffic with an accuracy of over 90%, which outperforms advanced models. In addition, this model can trace real-time traffic flow when there is a sudden drop.