Prediction and analysis of ship traffic flow based on a space-time graph traffic computing framework

Port traffic flow modeling based on big data is an important research direction in the shipping field, having the task of traffic forecasting for ports worldwide. Graph neural networks have a strong ability to capture the spatial topology characteristics and may be combined with recurrent neural networks or dilated convolution methods in time series prediction, producing a large number of spatiotemporal graph convolution models. Such models have been widely and successfully applied in traffic forecasting. Differing from urban traffic flow data, the statistical time span of port vessel flow and throughput data is large, its spatial span is wide, and the data experience significant fluctuations. Consequently, certain spatiotemporal graph convolution traffic prediction models are unsuitable for shipping scenarios. To address this shortcoming, we have created a unique port flow dataset based on automatic identification system (AIS) and port geographic data. Using theoretical analysis and experimental comparison, we have determined the most appropriate model for shipping predictions based on existing spatiotemporal graph models and have proposed model optimization recommendations for the maritime domain. Our experiment based on an open source traffic forecasting framework to compare the results of multiple existing spatiotemporal graph models under fair conditions with the central ports of Rotterdam, Shanghai, Boston, and Singapore. The results show that Graph WaveNet exhibits better performance in shipping scenarios.