Optimization of Rail Transit Alignments considering Vehicle Dynamics

Urban rail transit systems are being extended throughout the world because of their large capacities, avoidance of traffic congestion, and environmental advantages. Various optimization models can help design rail transit alignments satisfying various track geometry constraints, but none of these models can account for the impacts of vehicle dynamics on operational and user costs. This paper presents a practical rail transit alignment optimization method for designing track alignments that accounts for vehicle dynamics. The method can generate alignments that improve the balance between the initial cost and the operation and user costs recurring throughout the system's life cycle. A heuristic based on a genetic algorithm is developed to search for solutions efficiently while interacting with the supporting geographic information system. A hypothetical topography scenario is created to illustrate the impact of vehicle dynamics on the trade-offs among system costs. The Baltimore, Maryland, Red Line is used as a case study to demonstrate that the model can find good solutions in regions with complex topographies.