Port Short-Term Truck Flow Forecasting Model Based on Wavelet Neural Network

The gate of the port terminal is a key traffic node connecting the port area and the external road. Trucks for collecting and distributing port enter the gate and form a freight traffic flow. The passing volume of the gates ensures port operation and reflects the freight traffic demand. Being unavailable for timely freight traffic demand information, improper port operation arrangements are often made, which turns trucks become not fully accepted, leading to queues at the gates, blocking traffic and reducing efficiency of port operations. Based on the research theory of freight traffic flow induced by ship operation in the port, this paper takes port distribution as an example, and establishes the short-term truck flow of gate forecasting model based on ships' cargo weight remained to be distributed. The forecast provides a reference of short-term real-time port truck flow to port operation arrangements.