The Taiwan High Speed Rail (THSR) has recently added three additional stations to its original network. Although the three additional stations can improve accessibility to the system, these new stations can present difficulties in the transportation planning process, particularly for planning of train stops. The additional stations may benefit some passengers, but may also lengthen the travel time for the other passengers. Therefore, the main challenge faced by THSR is finding an efficient way to design appropriate stopping patterns. Past studies on stop planning usually adopted meta-heuristics or decomposition methods to solve this complex problem. Although these solution techniques can improve solution efficiency, none of them can guarantee the optimality of the solution and capture the transfer movement of different stopping patterns. In this research, we proposed an innovative network structure to address complex stop planning problems for high-speed rail systems. Given its special network structure, two binary integer programming models were developed to simultaneously form and determine the optimal stopping patterns for real-world THSR stop planning problems. An optimization process was also developed to accurately estimate the station transfer time corresponding to the variation in stopping patterns and passenger flow. Results of the case studies suggest that the proposed binary integer programming models exhibit superior solution quality and efficiency over existing exact optimization models. Consequently, using this stop planning optimization process can help high-speed rail system planners in designing optimal stopping patterns that correspond to passenger demand.

SUMMARYNorth American Freight Railroads are approaching the limits of practical capacity because of substantial future demand. In this research, we develop a Stochastic Multi-period Investment Selection Model (S-MISM) to assist railroads best allocate their capital investments in the long-term strategic capacity planning process. The novel optimization framework uses stochastic programming and Benders decomposition and provides a means to cope with unfulfilled demand and demand uncertainty in a long-term multi-period investment selection problem. S-MISM can determine which portions of a rail network need to be upgraded with what kind of expansion options at each defined period in the planning horizon. Experimental results show that the inclusion of demand uncertainty results in a better and more robust capacity plan. Using this decision support tool will help railroads maximize their return from capacity expansion projects and minimize the risk in strategic capacity planning subject to demand uncertainty.

The demand for railway transportation is expected to increase significantly worldwide and railway agencies are looking for better tools to allocate their capital investments in capacity planning in the best possible way. A capacity model has been developed to evaluate the network capacity of a conventional railway system with predominantly passenger trains. A capacity planning process is presented to help planners enumerate possible expansion options and to determine the optimal network investment plan for meeting future demand. Use of this capacity evaluation tool and capacity planning process will help railway agencies provide satisfactory service to their customers and pleasing returns on shareholder investments.

Recent railway industry campaigns have highlighted the relative average fuel efficiency of freight and passenger trains as a key benefit of the railway transportation mode. These efficiencies are anticipated to increase rail market share as rising energy costs make less efficient competing modes less attractive. However, the fuel consumption and energy efficiency of a specific passenger or freight rail system, and even individual trains, depend on many factors. Changes in these factors can have various effects on the overall fuel consumption and efficiency of the system. One of these factors is the amount of congestion and delay due to increased traffic on the line. Thus, it is possible that the additional traffic anticipated to shift to the rail mode due to its energy benefits may increase congestion and actually have a negative impact on overall network energy efficiency. Such a case would tend to dampen the future shift of traffic to the rail mode. While simple train performance calculators can evaluate the energy efficiency of a train for an ideal run, more powerful train dispatching simulation software is required to simulate the performance of trains in realistic operating scenarios on congested single-track lines. Using this software, the relative impact of congestion on efficiency can be analyzed and compared to changes in factors related to fuel consumption. In this study, several factors affecting the efficiency of both passenger and freight rail systems were selected for analysis. Rail Traffic Controller (RTC), a train dispatching software, simulated representative single-track rail subdivisions to determine the performance of specific passenger and freight trains under different combinations of factor level settings. For passenger rail, the effects of traffic volume and station spacing on fuel consumption were analyzed while the effects of traffic volume and average speed were analyzed for freight rail. Each system was analyzed on level track and on territory with grades. Preliminary results suggest that passenger trains, if given priority, maintain their efficiency until large numbers of passenger trains are present on the network, while freight trains experience degradation in energy efficiency as congestion increases. These results will be used to develop a factorial experiment to evaluate the relative sensitivity of freight and passenger rail efficiency to congestion and other system parameters. The paper concludes with a brief discussion of possible technologies to improve efficiency and offset potential losses due to future congestion.