Two-track passenger rail lines typically operate with all trains serving every station. Without additional infrastructure, transit planners have limited options to improve travel times. Service could be improved by operating a skip-stop service where trains only serve a subset of all the station stops. A skip-stop pattern must find an optimal balance between faster passenger travel times and lower service frequencies at each station. A mixed integer formulation is proposed to analyze this tradeoff; however, the mixed integer formulation could not scale efficiently to analyze a large scale commuter line. A genetic algorithm is presented to search the solution space incorporating a larger problem scope and complexity. In a case study of a Midwest commuter line, overall passenger travel time could be decreased by 9.5%. Both analyses can give insights to transit operators on how to improve their service to their customers and increase ridership.

Recent proposals for expanded intercity passenger rail service in the United States have included plans for incremental improvements to existing Amtrak service. Improvements to existing services aim to accommodate faster and more frequent passenger train operation, generally on trackage owned and operated by freight railways. There are various projects and approaches to consider when decreasing the running time of passenger trains on a particular corridor. Raising the maximum operating speed can yield different benefits on different sections of the route and conditions on adjacent sections can interact with each other. For instance, the marginal travel time benefit of improving segments of a line from 79 to 110 mph maximum speed is less than the benefit of other improvements to eliminate segments currently restricted to lower speeds. Therefore, to maximize the potential of limited resources, project investments must be selected carefully to improve performance in a cost-effective manner. This paper presents a methodology for optimally selecting projects or establishing program budgets to reduce running time on a passenger rail corridor with consideration of capital, maintenance and operating cost. The proposed project selection model is formulated with Genetic Algorithms (GAs). In the model, a route is divided into sections that can be independently upgraded and the objective function is formulated as minimization of running time along the route. This model can aid in quickly and efficiently developing a strategic plan for improving running time on passenger rail corridors.