基于数据特征挖掘的公铁联运枢纽运行状态评价研究OA

Road-rail intermodal transportation hubs are the main components of China's comprehensive freight hubs,and the operational status of such hubs can reflect the overall operation of transportation and logistics in the country.A single type of operational data from a hub is difficult to use to accurately evaluate its overall operational status,and evaluation methods relying on manual experience are also greatly limited due to the influence of subjective factors.The evaluation method for the operational status of road-rail intermodal transportation hubs based on data feature mining selected nine indicators from three dimensions,including freight volume completion,hub busyness degree,and hub operational efficiency,to construct a historical data set.The K-means algorithm was used to classify the historical data into different states,and different categories of data were interpreted as different operational states of the hubs.Then,the classified historical data were used to train and test a support vector machine,and a support vector machine model capable of evaluating the operational status of hubs was obtained.In the empirical study,the test results of the historical dataset show that this evaluation method can achieve a high accuracy rate of status recognition and has good reliability.It can help industry management departments improve their ability to perceive the situation of transportation and logistics.