LAUSR

The automatic train operation which integrates knowledge-based intelligent algorithm to develop safe and efficient control system has become one of the most important developing directions in the field of railway transit equipment. Multi-objective optimization is a strictly incompatible problem, and such contradiction is one of the main reasons that lead to the best multi-objective optimization difficult to achieve. In this paper, the multi-objective optimization feature information is transformed into the association function first, and then the matter-element theory is introduced to establish models for the speed trajectory to achieve the multi-objective optimization to fuse knowledge-based safety requirement constrained condition. Performance indices weighting of different performance in different stages are determined with the Hierarchical Mahalanobis distance method, and the decision speeds are calculated with goodness evaluation method. Taking Shanghai Railway Transit Equipment in China as a case study, this paper selected the multi-objective performance indices including passenger comfort, running stability, energy efficiency, and parking accuracy as objectives to support the decision-making. The multi-objective performance indices are evaluated by a field investigation and simulation. The test result shows that the comfort level, running stability, energy saving property, and parking accuracy are better than those derived by the traditional control algorithm. It indicates that the model has the advantage that it conveniently quantifies the qualitative indices, and it can integrate the data source information to improve the multi-objective performance indices, so that it is very useful to apply multi-source data and prior knowledge to multi-objective optimization of the automatic train operation control system.