The demand for freight rail transportation in North America is anticipated to substantially increase in the foreseeable future. Additionally, government agencies seek to increase the speed and frequency of passenger trains operating on certain freight lines, further adding to demand for new railway capacity. The majority of the North American mainline railway network is single track with passing sidings for meets and passes. Expanding the infrastructure by constructing additional track is necessary to maintain network fluidity under increased rail traffic. The additional track can be constructed in phases over time, resulting in hybrid track configurations during the transition from purely single track to a double-track route. To plan this phased approach, there is a need to understand the incremental capacity benefit as a single-track route transitions to a two-main-track route in the context of shared passenger and freight train operations. Consequently, in this study, the Rail Traffic Controller software is used to simulate various hybrid track configurations. The simulations consider different operating conditions to capture the interaction between traffic volume, traffic composition and speed differences between train types. A nonlinear regression model is then developed to quantify the incremental capacity benefit of double-track construction through exponential delay–volume relationships. Adding sections of double track reduces train delay linearly under constant volume. This linear delay reduction yields a convex increase in capacity as double track is installed. These results allow railway practitioners to make more-informed decisions on the optimal strategy for incremental railway capacity upgrades.

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.

Long term demand for rail transportation in North America is projected to increase considerably in the coming decades. A significant portion of the routes in the United States are single track with passing sidings. Eventually, the second mainline track will become necessary to maintain network fluidity. However, the full funding for the second track may not be available all at once; subsequently the track can be phased in over time creating a hybrid track configuration. Depending upon the traffic characteristics, traffic will transition from a delay characteristic of single track to a delay characteristic of double track. A response surface model was developed that tested various factors including the amount of second main track added, traffic volume, traffic composition, and the speed differential between train types. Design of experiments software (JMP) was used in conjunction with railway simulation software (Rail Traffic Controller) to conduct the analysis. The benefit of full double track can be realized for high priority trains with partial double-track. However, the low priority traffic may not experience double-track-like performance until nearly the entire second mainline track is installed. The results suggest a linear relationship between miles of second mainline track added and reduction in train delay. The maximum speed of the freight train has a great impact on train delays in a congested network. These results further the understandings of key mainline interactions between passenger and freight trains. In addition the models presented will facilitate the development of an optimal incremental upgrade model for capacity expansion. Also, the methodology presented can be adopted to analyze the progression from double to triple track.

Single-track line capacity is limited by the need for trains to decelerate, stop, and accelerate out of sidings to allow other trains to use the intermediate single-track sections. Meeting at these sidings is the largest cause of train-interference delays on single tracks (3). Doubletrack configurations largely eliminate this dynamic and allow the line to operate at a significantly higher capacity. Because of these inherent efficiencies, double-track lines can run more trains at higher average speeds than a single-track configuration. In the absence of meetings, passing conflicts and train spacing become key capacity constraints for a double-track line.For both single-and double-track configurations, previous research has determined that simultaneous operation of different train types consumes more capacity than homogeneous operations. Vromans et al. used simulation to investigate options to improve passenger operations (4). Leilich and Dingler et al. used simulation analysis to determine the delay caused by the interaction of unit trains and intermodal trains and found a capacity loss due to heterogeneous operations (5,6). Harrod used integer programming to show that it can be feasible to run higher-speed trains in a single-track freight network provided there is a lane available (7). Petersen and Taylor used simulation analysis of where to locate sidings in single track to accommodate higher-speed trains (8). Sogin et al. used simulation analyses to show that there is a larger increase in delay to freight trains by adding a passenger train instead of a freight train in both single-(9) and double-track networks (10). METHODOLOGYFour key factors (Table 1) were considered in the simulation experiments. The different permutations of Table 1 can represent various shared-corridor conditions. Traffic volume is defined as the total number of trains per day (TPD). Traffic mixture (heterogeneity) is the percentage of these that are freight trains and describes the traintype heterogeneity of the corridor. The parameters of total TPD and percent freight are also described interchangeably by number of passenger trains and number of freight trains per day. The subsequent analyses use both pairs of parameters. The maximum speed of the passenger train was analyzed at 79 and 110 mph with intermediate speeds used in a correlation analysis. The fourth factor was the number of main tracks on the line.The full factorial of Table 1 was simulated. Each simulation run featured a unique combination of the four factors described in Table 1. RTC was used to simulate a dispatcher making decisions regarding the train movements across a particular line. RTC is commonly used by railroad capacity planners in North America to simulate train traffic. Each simulation outputs the performance of trains over a 3-day period. This simulation was then repeated four times Federal, state, and regional transportation authorities have shown an increased interest in adding or increasing passenger rail service between many city pairs. The most commonly prop...

North American freight railroads are expected to experience increasing capacity constraints across their networks. To help plan for this increased traffic, railroads use simulation software to analyze the benefits of capacity expansion projects. Simultaneous operation of heterogeneous traffic further increases delay relative to additional homogenous traffic. Additional passenger trains can cause more delays to freight trains than additional freight trains. Rail Traffic Controller (RTC) was used to run simulations with varying mixes of unit freight and passenger trains operating at various speeds on a double track configuration. Basic assumptions on the relative difference in priority between train types lead to drastically different results on the impact of adding higher priority trains. This assumption dictates whether the track in the opposing direction should be used for overtake maneuvers. Also, higher speed differentials between train types can result in higher delays as faster trains catch up to slower trains more quickly. These analyses will help planners improve their understanding of the tradeoff in capacity due to operation of different types of trains at different priorities and speeds.