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.

A body of studies has proposed to obtain high-quality images from low-dose and noisy Computed Tomography (CT) scans for radiation reduction. However, these studies are designed for population-level data without considering the variation in CT devices and individuals, limiting the current approaches' performance, especially for ultra-low-dose CT imaging. Here, we proposed PIMA-CT, a physical anthropomorphic phantom model integrating an unsupervised learning framework, using a novel deep learning technique called Cyclic Simulation and Denoising (CSD), to address these limitations. We first acquired paired low-dose and standard-dose CT scans of the phantom and then developed two generative neural networks: noise simulator and denoiser. The simulator extracts real low-dose noise and tissue features from two separate image spaces (e.g., low-dose phantom model scans and standard-dose patient scans) into a unified feature space. Meanwhile, the denoiser provides feedback to the simulator on the quality of the generated noise. In this way, the simulator and denoiser cyclically interact to optimize network learning and ease the denoiser to simultaneously remove noise and restore tissue features. We thoroughly evaluate our method for removing both real low-dose noise and Gaussian simulated low-dose noise. The results show that CSD outperforms one of the state-of-the-art denoising algorithms without using any labeled data (actual patients' low-dose CT scans) nor simulated low-dose CT scans. This study may shed light on incorporating physical models in medical imaging, especially for ultra-low level dose CT scans restoration.