Selection and prediction of metro station sites based on spatial data and random forest: a study of Lanzhou, China.

Urban economic development, congestion relief, and traffic efficiency are all greatly impacted by the thoughtful planning of urban metro station layout. with the urban area of Lanzhou as an example, the suitability of the station locations of the built metro stations of the rail transit lines 1 and 2 in the study area have been evaluated using multi-source heterogeneous spatial data through data collection, feature matrix construction, the use of random forest and K-fold cross-validation, among other methods. The average Gini reduction value was used to examine the contribution rate of each feature indicator based on the examination of model truthfulness.