Evaluation of the Correlation Between Ports and Rear Area Logistics Parks Based on Decision Tree Learning

摘要

The cooperation between port and logistics parks is crucial for port to build the collection and distribution system. Using the Decision Tree Learning method, this work establishes evaluation model of correlation between ports and logistics parks in the rear area. Firstly, the correlation factors are analyzed, including the spatial correlation, the functional matching and the service object matching. Secondly, based on a number of industry regulation we generate a training dataset of 105 samples. Thirdly, the Decision Tree is built using the classic ID3 algorithm. All the process is implemented by MATLAB. Last but not least, we analyze the actual statistical data of Shenzhen port and its logistics parks based on the Decision Tree we build, and verify the validity of the evaluation model.