Background: Due to the growing demand for energy and environmental issues related to using fossil fuels, it is becoming tremendously important to find alternative energy sources. Bioethanol produced from switchgrass is considered as one of the best alternatives to fossil fuels. Methods: This study develops a two-stage supply chain modeling approach that first determines feasible locations for constructing switchgrass-based biorefineries in the state of North Dakota by using Geographic Information Systems (GIS) analysis. In the second stage, the profit of the corresponding switchgrass-based bioethanol supply chain is maximized by developing a mixed-integer linear program that aims to commercialize the bioethanol production while impacts of energy use and carbon emission costs on the supply chain decisions and siting of biorefineries are included. Results: The numerical results show that carbon emissions and energy consumption penalties affect optimal biorefinery selections and supply chain decisions. Conclusions: We conclude that there is no need to penalize both emissions and energy use simultaneously to achieve desirable environmental benefits, otherwise, the supply chain becomes non-profitable. Moreover, imposing emissions or energy consumption penalties makes the optimization model closer to supply sources while having higher land rental costs. Such policies would promote sustainable second-generation biomass production, thus decreasing reliance on fossil fuels.

Constructing 3-D rail models from observed lidar point clouds has gained growing interest in the railroad industry: 1) to create digital twins of infrastructure asset inventories; and 2) to continuously monitor infrastructure health conditions. Performance of the existing threshold-based and rules-based extraction methods not only depends on feature-specific properties such as retro-intensity, geometric design rules, and defined feature shapes but also relies on data quality and prior knowledge such as data resolution, scanning angle, and feature orientation. This research proposes a hybrid approach, combining both data-driven filtering algorithms and artificial intelligence (AI) model-based classification approaches for automatically generating 3-D rail models. The main objective is to develop an automatic and computationally efficient procedure for rail 3-D model reconstruction in a massive, noisy, and unevenly distributed railroad scene with high accuracy but without requiring: 1) prior knowledge; 2) high-density point data clouds; or 3) feature-specific global features. In this paper, we develop and compare three hybrid procedures by using data collected by the Federal Railroad Administration (FRA) with a point density of 293 pt/m ² . Both pointwise and lengthwise evaluations were used to evaluate the robustness of the proposed methods. The pointwise evaluation shows an average precision, recall, f1, and intersection over union (IoU) of 0.989, 0.747, 0.852, and 0.741, respectively. The lengthwise evaluation shows average correctness, completeness, and quality of 99.32%, 94.69%, and 94.06%, respectively. The proposed automated, configuration-independent, and global-feature-free method shows its efficient and effective rail extraction capabilities with low-density point clouds for complicated railroad terrains.

Highway–rail grade crossings (HRGCs) are locations where roadways and railway tracks intersect at the same level. Due to the shared level of travel and the substantial mass disparity between trains and highway users, collisions at these crossings tend to be catastrophic. As a result, HRGC crashes represent a major public safety concern in the United States. While previous studies have evaluated contributing factors to crash severity, there has been limited focus on the role of highway users’ action and its influence on crash severity. This study aims to examine all relevant factors, with a particular focus on highway user actions. The dataset, sourced from the Federal Railroad Administration’s database, includes data from six states between 2013 and 2022, specifically addressing severity and contributing factors. The proportional analysis highlights that highway user actions such as “went around the gate”, “did not stop”, and “stopped on the crossing” dominantly contribute to crash severity. A multinomial logistic regression was employed to identify significant determinants of crash severity. Odds ratio analysis reveals that “went around the gate” significantly increases the risk of fatal injuries across all six states, with odds ratios ranging from 3.45 in California to 4.55 in Georgia. The findings provide data-driven insights that can support the development of targeted safety countermeasures and intelligent traffic management strategies to enhance safety at HRGCs.