The biomass supply chain is one of the most critical elements of large-scale bioenergy production and in many cases a key barrier for procuring initial funding for new developments on specific energy crops. Most productions rely on complex transforming chains linked to feed and food markets. The term ‘supply chain’ covers various aspects from cultivation and harvesting of the biomass, to treatment, transportation, and storage. After energy conversion, the product must be delivered to final consumption, whether it is in the form of electricity, heat, or more tangible products, such as pellets and biofuels. Effective supply chains are of utmost importance for bioenergy production, as biomass tends to possess challenging seasonal production cycles and low mass, energy and bulk densities. Additionally, the demand for final products is often also dispersed, further complicating the supply chain. The goal of this paper is to introduce key components of biomass supply chains, examples of related modeling applications, and if/how they address aspects related to environmental metrics and management. The paper will introduce a concept of integrated supply systems for sustainable biomass trade and the factors influencing the bioenergy supply chain landscape, including models that can be used to investigate the factors. The paper will also cover various aspects of transportation logistics, ranging from alternative modal and multi-modal alternatives to introduction of support tools for transportation analysis. Finally gaps and challenges in supply chain research are identified and used to outline research recommendations for the future direction in this area of study.

A growing demand for passenger and freight transportation, combined with limited capital to expand the United States (U.S.) rail infrastructure, is creating pressure for a more efficient use of the current line capacity. This is further exacerbated by the fact that most passenger rail services operate on corridors that are shared with freight traffic. A capacity analysis is one alternative to address the situation and there are various approaches, tools, and methodologies available for application. As the U.S. continues to develop higher speed passenger services with similar characteristics to those in European shared-use lines, understanding the common methods and tools used on both continents grows in relevance. There has not as yet been a detailed investigation as to how each continent approaches capacity analysis, and whether any benefits could be gained from cross-pollination. This paper utilizes more than 50 past capacity studies from the U.S. and Europe to describe the different railroad capacity definitions and approaches, and then categorizes them, based on each approach. The capacity methods are commonly divided into analytical and simulation methods, but this paper also introduces a third, “combined simulation–analytical” category. The paper concludes that European rail studies are more unified in terms of capacity, concepts, and techniques, while the U.S. studies represent a greater variation in methods, tools, and objectives. The majority of studies on both continents use either simulation or a combined simulation–analytical approach. However, due to the significant differences between operating philosophy and network characteristics of these two rail systems, European studies tend to use timetable-based simulation tools as opposed to the non-timetable-based tools commonly used in the U.S. rail networks. It was also found that validation of studies against actual operations was not typically completed or was limited to comparisons with a base model.

Increasing the efficiency, frequency, and speed of rail defect detection can reduce maintenance costs and improve the sustainability of railways. The non-contact eddy current (EC) system can be operated along with a railcar for detecting rail flaws. Even if the EC can be utilized for rail defect identification and characterization, current commercial devices are not sufficient for defect classification on rails by providing highly sensitive signals for post-processing. In this study, we established an efficient and expandable eddy current rail inspection system and verified its capability for classification of different defect signals. The integrated hardware and software EC measurement system was firstly applied to detect notched cracks in steel samples with different crack depths and angles. The measured voltage and current analog inputs from the eddy current sensor were acquired and processed with a fast Fourier transformation (FFT) algorithm in the LabVIEW platform. The real-time impedance was then obtained by transferring signals to a normalized impedance plane plot. The processed EC signals showed adequate sensitivity and efficiency with changes of notched crack depths and angles during the sensor movement. A comparative case study on field rail samples was then conducted to examine the feasibility and capability of the established system on different types of actual rail defects. The experimental analysis and case study results demonstrate that the integrated eddy current system could possibly be used for non-destructive rail crack inspection and classification. The enhanced detection capability (especially on subsurface cracks) and real-time post-processing technique could further contribute to improving rail-life sustainability.

In 2022, 2034 incidents occurred at highway-rail grade crossings (HRGCs) in the United States, posing significant risks such as fatalities, injuries, and property damage. These incidents underscore the need for effective prevention and mitigation strategies. With over 212,000 public and private HRGCs nationwide, safety monitoring is challenging, as traditional inspections primarily rely on manual assessments. Obstructed sightlines at HRGCs further increase safety risks by limiting road users' ability to see approaching trains. Although previous studies have addressed behavioral and safety issues, the literature currently lacks quantitative data analysis of sightlines at HRGCs. This study aims to address this gap by utilizing remote sensing techniques to identify and quantify sightline visibility. We studied 12 HRGCs using geospatial analysis in ArcGIS Pro through Viewshed and Observer Points analysis, integrating United States Geological Survey (USGS) Light Detection and Ranging (LiDAR) data with United States Federal Railroad Administration (FRA) crossing reports. Our findings indicate that sightline issues in the case studies reviewed are primarily linked to traffic control devices, environmental factors, and geometric conditions. Additionally, the results suggest that USGS LiDAR data and geospatial analysis offer potentially rapid and cost-effective methods for identifying sightline safety issues at HRGCs.