Model for Optimal Selection of Projects to Improve Running Time and Operating Cost Efficiency on Passenger Rail Corridors

Abstract

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.

Identifying the Importance of Criteria for Passenger Choice of Sustainable Travel by Train Using ARTIW and IHAMCI Methods

“…In practice this analytical information can be used to determine the optimal passenger train length, to assess the profitability of introducing local trains into operation, to define the optimal number of stops, and to calculate the amount of passenger cars needed to satisfy the demand. The route in the model that was offered by Tang et al [29] is divided into sections that can be independently updated, and the target function is expressed in terms of minimizing driving time. This model can help to quickly and efficiently develop a strategic plan to reduce running time in passenger rail corridors.…”

Information System and Technology Optimization as a Tool for Ensuring the Competitiveness of a Railway Undertaking—Case Study

“…In one of the modules in the IS KASO, the planner creates shifts, compiles the shiftplans of the accompanying staff, and checks the correctness of the shift with the shift schedule. Due to the impossibility of incorporating all alternatives resulting from the restriction of trains, the planner works in cooperation with the team leader and the commanders or other employees to accompany trains during holidays, construction works, and other unusual events of a longer-term nature [30,31].…”

Enhancing Capacity and Managing Demand to Increase Short-Term Throughput on the San Francisco-Oakland Bay Bridge

“…Research from Tang et al points to the idea that the most economical way to decrease rail transit travel time is not increasing the maximum speed, but rather decrease the instances of reduced speeds (Tang, Dick, & Caughron, 2016). As in, eliminating a speed-restricted section of track can be far more effective than increasing the maximum speed on the rest of the line.…”

“…The top chart shows in orange the time saved by eliminating a slow portion of track, versus the less time saved by increasing the top speed in the bottom chart. (Tang, Dick, & Caughron, 2016) A surrogate for a slow area of track to improve is an underused station to skip. The Bay Area has its own perfect case study of this idea put to the test: Caltrain's Baby Bullet service.…”