A Study of New Inland River Port Throughput Prediction Based on Multi-Source Big Data

Inland river ports are the key node in the intermodal logistics chain of land, river, and sea. However, there is limited research about the forecasting of inland river port throughput. Because the historical data of new inland river ports is scarce, a creative way was proposed to predict its throughput based on multi-source big data. Firstly, forecast the total throughput of all inland river ports in a city, then allocate the predicted total throughput to the target ports according to the competitive situation among all inland ports. The hinterland GDP value of each inland port was calculated to comprehensively reflect the competitive situation of all inland ports after city GDP spatialization based on nighttime lighting data. The case study of SDX port showed effectiveness of the method.