Concurrent Optimization of Rail Transit Alignments and Station Locations

Designing urban rail transit systems is a complex problem, which involves the determination of station locations, track geometry, and various other system characteristics. Most of the previous rail transit route optimization studies have focused on the alignment design between predetermined stations, whereas a practical design process has to account for the complex interactions among railway alignments and station locations. This paper proposes a methodology for concurrently optimizing station locations and the rail transit alignment connecting those stations, by accommodating multiple system objectives, satisfying various design constraints, and integrating the analysis models with a geographical information system database. The methodology incorporates demand and station costs in the evaluation framework and employs a genetic algorithm for optimizing the decision variables for station locations, station types, and track alignments. It is expected that transit planners may greatly benefit from the proposed methodology, with which they can conveniently and efficiently optimize candidate alternatives. The Baltimore Red Line is used as a case study to demonstrate how the model can find very good solutions in regions with complex geography.