Data Mining on Index of Static Load of Freight Cars

Static load of freight vehicles is one of the important indices to evaluate the efficiency of railway wagon usage, as well an important basis for predicting coefficient of static load in railway line design. Based on sorting out the data structure of static load, clustering algorithm of K-medorids is employed for data mining to find the significant and relatively stable classification characteristics between static load of railway administrations. It is different delivery proportions of heavy goods that are indicated by index factor analysis to cause the static load classification of railway administrations. The data-mining also indicates the differences of static load between different grading railway administrations which cannot be ignored, therefore determining the coefficient of static load in railway design based on divided region is recommended.