Stopping schedule optimization of express/local trains in urban rail transit

In view of the high complexity and unreasonable station classification of the existing stopping schedule optimization models of express/local trains, by utilizing the grey variable weight clustering model to preliminarily cluster stations so as to achieve the decision attributes and by taking the clustering results of each index as the condition attributes, the attribute reduction of the factors influencing the station classification is conducted. Then, the advantage analysis of the reduced attributes is performed to determine the relative grey correlation degree between the condition attributes and the decision ones, and the grey fixed weight clustering model is adopted to classify stations. By setting the principle that express trains must stop at the stations in the first level and they may stop in the second level if it is required but do not stop in the third level, a nonlinear 0-1 programming model of the stopping schedule optimization of express/ local trains is constructed according to the classification results, and the constructed model is solved by using the genetic-annealing algorithm. The results show that the proposed method can eliminate a large number of invalid solutions and greatly narrow the solution space, thus greatly improving the solution efficiency, which is of great significance in quickly establishing the operation scheme of trains.