Energy pipeline networks are critical components of power systems, enabling the efficient transportation of energy resources, and are vital to societal and economic stability. However, natural gas pipelines are vulnerable to various factors, including aging infrastructure, natural disasters, accidents, and cyberattacks. In addition, these vulnerabilities can be caused by infrastructure capacity limits, which can disrupt natural gas supply and lead to electrical outages with significant societal and economic impacts. Traditional methods like Fault Tree Analysis, Event Tree Analysis, and Bayesian Networks provide valuable insights but often fail to address the complex interdependencies and dynamic risks in these systems. Recent advances in machine learning (ML), particularly Graph Neural Networks (GNNs), offer promising capabilities for modeling complex infrastructure interdependencies. This paper provides a comprehensive review comparing traditional methods, non-graph ML approaches, and graph-based techniques across vulnerability and dependency assessment applications in integrated gas-electricity systems. Challenges such as data quality, computational demands, and interpretability persist when dealing with large, complex networks, regardless of the methods employed. To provide quantitative evidence supporting these comparative findings, a controlled benchmark experiment is conducted on the IEEE 24-bus and IEEE 118-bus standard test cases, evaluating nine representative methods under a unified protocol. Future research directions include enhancing data collection, integrating hybrid models, and addressing domain-specific complexities to advance intelligent management and sustainable development of energy systems.

The uncontrolled release of hazardous materials or chemicals poses significant risks to the environment and public health, especially when contaminants infiltrate the soil and potentially reach groundwater. This study introduces a data-driven framework for assessing spill infiltration dynamics across environmental and soil conditions. Using a high-resolution dataset from laboratory infiltration tests with 99,000 observation data points, this research examines five distinct soil types (silt, very fine sand, fine sand, medium sand, and coarse sand) and six hazardous substances (Methanol, Ethanol, Hydrochloric Acid (HCl), Sodium Hydroxide (NaOH), Bakken crude oil, and Niobrara crude oil), offering valuable insights into the factors influencing spill behavior. The analysis combines correlation, regression, quadratic temperature fitting, ANOVA, and Sobol sensitivity analysis. Spill volume was the dominant driver, with Pearson correlations of 0.781–0.972, 39.64% ANOVA contribution, and a pooled Sobol total-order index of 0.971. Chemical–soil interaction ranked second, explaining 22.33% of variance. Temperature showed weak standalone effects but became more influential through interactions with reactive chemistry and soil texture, whereas infiltration time showed only a weak relationship with depth within the initial 0–60 s gravity-driven observation window and does not represent late-stage capillary redistribution. Reactive chemicals such as HCl and NaOH penetrated deeper in coarse soils, whereas heavier crude oils showed limited movement. A Casselton rail-accident case study further demonstrates how laboratory-derived relative-response ratios can support screening-level spill-risk evaluation. This framework supports improved prevention, mitigation, and environmental management for rail-based hazardous-material incidents.

The commercial deployment of autonomous truck (AT) technology represents a potential paradigm shift in freight transportation, yet rigorous economic analysis remains underdeveloped. This study develops empirically grounded cost models to identify critical viability thresholds for autonomous operations. Results indicate that AT has marginal cost per mile savings of 25-36% compared to conventional trucks. Under hub-to-hub operations, potential savings range from 13% for 200-mile routes to 31% for 1,000-mile routes when economies of scale are achieved. Without scale economies, costs may increase 52% for shorter routes while achieving only 8% savings on longer routes. Economic viability depends on four interdependent parameters: hub-to-hub distance, drayage distance, operational volume, and technical support costs. Additional productivity benefits from extended operating hours are projected to increase utilization by 63% in agricultural supply chains. These findings support phased implementation targeting high-volume interstate corridors and provide stakeholders with analytical frameworks for strategic autonomous truck deployment planning.

Adverse weather conditions significantly degrade mobility and safety at rural signalized intersections, where high approach speeds and limited driver expectancy amplify operational and crash risks. While autonomous vehicles (AVs) have the potential to improve traffic performance, it takes a significant duration to penetrate. During this period, mixed traffic with human drivers and AVs will dominate. In this mixed traffic, the impacts of AVs at low penetration levels on adverse weather remain insufficiently understood, particularly in rural contexts. This study presents a simulation-based assessment of the effects of low AV penetration on mobility and safety at a rural signalized intersection under varying weather conditions. A calibrated microsimulation model was developed using PTV VISSIM to represent clear, rain, and snow scenarios with autonomous vehicles introduced at low penetration rates within conventional traffic. Mobility performance was evaluated using delay, travel time, and average speed, while safety impacts were assessed through surrogate safety measures extracted using the Surrogate Safety Assessment Model (SSAM), including time-to-collision and post-encroachment time. Results indicate that low levels of AV penetration of 10% can improve overall mobility performance compared with conventional traffic, particularly under adverse weather conditions. Safety outcomes show a reduction in conflict frequency and severity under low AV penetration, with more pronounced benefits observed during degraded weather scenarios. Further AV penetration from 10% to 25% may not significantly improve in a rural environment. The findings suggest that early-stage AV deployment may offer measurable mobility and safety benefits at rural signalized intersections, even before widespread adoption. This study provides practical insights for transportation agencies and policymakers regarding the potential role of low-penetration AV integration in enhancing rural traffic operations and safety under adverse weather conditions.

As autonomous trucking systems in global logistics networks increase, their cyber-physical architecture introduces vulnerabilities that demand strategically prioritized security interventions. However, existing studies address technical threat vectors but lack frameworks for operationally contextualized cybersecurity decision-making. Therefore, this study employs a two-round Fuzzy Consensus Building method and Fuzzy CRiteria Importance Through Intercriteria Correlation method to systematically identify, validate, and prioritize 13 cybersecurity strategies specific to autonomous trucks. Thus, integrating interdisciplinary expert judgment and statistical variation, leveraging fuzzy set theory to model uncertainty, the study produces a prioritization strategy roadmap. Access Control, Blockchain-Based Integrity, and End-to-End Encryption emerged as top-tier strategies, reflecting a strategic shift toward proactive, architecture-level cybersecurity. The results offer actionable guidance for original equipment manufacturers (OEMs), fleet operators, and regulators, and highlight the need for adaptive policies that support the adoption of emerging technologies in freight automation. This study provides a validated roadmap to enhance cyber resilience in the autonomous logistics ecosystem. This study introduces a hybrid method combining FCB and F-CRITIC to prioritize cybersecurity strategies. It focuses on autonomous trucks, filling a gap often overlooked in general AV cybersecurity research. Access control, blockchain integrity, and encryption ranked as the top three strategies. Practical and managerial implications are provided for OEMs, fleet operators, and system integrators. The study proposes updates to ISO/SAE 21434 and UNECE WP.29 to support safer freight automation.

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.

Despite advances in highway-rail grade crossing (HRGC) safety, including widespread use of active control devices, crashes at these intersections still lead to severe outcomes. Conventional crash prediction models often fail to capture severity-level dependencies, rely on assumption-driven and computationally intensive methods, and overlook links between severity and time between events. This study introduces a predictive framework based on positive monotonic neural networks (MNNs) for modeling time-to-crash outcomes with severity at HRGCs. Considering the relative newness of the time-to-crash paradigm in HRGC safety, the Neural Fine-Gray model is adopted as a core MNN implementation to estimate severity-specific crash likelihoods. This approach eliminates the numerical integration required in traditional time-to-event models, substantially reducing computational burden and accelerating training for large datasets. The framework naturally handles imbalanced HRGC data by treating event-free records as right-censored, avoiding the resampling required in traditional machine-learning approaches. To examine severity-level dependencies-an aspect largely overlooked in the literature-four MNN architectures are developed and evaluated. Using a 29-year North Dakota HRGC dataset, results show trade-offs between predictive accuracy and computational efficiency. The cause-specific MNN performs best for medium- and long-term horizons, whereas the multi-head MNN converges faster and excels at short horizons. Moreover, benchmarking against traditional time-to-event models-cause-specific Cox and Fine-Gray-shows modest calibration gains and 2%-50% stronger discrimination, reflecting the alignment between MNNs and the nonlinear, high-dimensional HRGC data. The framework also enhances interpretability by revealing paradoxical effects, including the "adding flashing lights paradox" and the "adding stop signs paradox."

Rural transportation systems face disproportionate mobility and safety challenges during emergency evacuations due to limited infrastructure, complex terrain, and extended emergency response times. Despite growing interest in automated vehicles (AVs), their performance in rural evacuation contexts remains poorly understood. This study evaluates the mobility and safety impacts of AV integration in rural emergency evacuation scenarios using microsimulation analysis. Little Tujunga Canyon Road-a narrow, mountainous two-lane corridor in Los Angeles County, California- was selected as a representative wildfire evacuation route. Traffic simulations were conducted in PTV VISSIM with safety performance evaluated using the Surrogate Safety Assessment Model (SSAM). Three automation levels (Level 1 driver assistance, Level 3 conditional automation, and Level 5 full automation) were examined across AVs market penetration rates of 25%, 50%, 75%, and 100%, benchmarked against a 100% human-driver baseline. Results demonstrate substantial mobility improvements with increasing AVs adoption. At 100% market penetration, average speeds increased by 37% for Level 1, 40% for Level 3, and 46% for Level 5 automation, with total travel time reductions of approximately 30%. Network delays exhibited nonlinear patterns, increasing at intermediate penetration rates (25-50%) due to behavioral conflicts in mixed traffic before decreasing below baseline levels at full automation. Safety analysis revealed elevated traffic conflicts during transitional phases, while Level 5 automation eliminated traffic conflicts entirely at 100% penetration. These findings highlight both the transitional challenges and long-term benefits of AVs deployment in rural emergency evacuations. The results provided actionable insights for transportation planners and emergency managers, particularly for improving evacuation outcomes in remote, hazard-prone regions and for mobility-constrained populations.

Advanced driving technologies have the potential to transform the transportation sector. Specifically, the progress of autonomous vehicles (AVs) has caught the interest of governmental authorities, industrial groups, and academic institutions, with the goal of improving the driving experience, effectiveness, and comfort while also improving safety and flexibility and lowering vehicle emissions. Considering these facts, the purpose of this study is to assess the possible effects and advantages of AVs under diverse traffic situations in urban and rural environments. Knowledge of traffic behavior inside a certain road network is made easier by traffic microsimulation. PTV VISSIM (Verkehr In Städten—SIMulationsmodell) is among the microsimulation software programs that has attracted great interest because of its remarkable capacity to faithfully simulate traffic conditions. This review helps researchers choose the best methodological strategy for their individual study objectives and restrictions while using VISSIM. This research assesses the effect of AVs in different driving behavior and weather conditions in urban and rural situations using VISSIM and introduces traffic safety using the surrogate safety assessment model (SSAM). The study focuses on 10 parameters from the Wiedemann 99 car-following model and speed distribution to establish the correlation between weather conditions and surrogate safety measures (SSMs). The findings could lead to more accurate and authentic models of driving behavior and encourage the automotive industry to further equip AVs to operate efficiently in various environmental and driving conditions.

Digital Twin (DT) technology is revolutionizing the railway sector by providing a virtual replica of physical systems, enabling real-time monitoring, predictive maintenance, and enhanced decision-making. This systematic literature review examines the status, enabling technologies, case studies, and frameworks for DT applications in railway systems with 91 selected papers from Scopus, Web of Science, IEEE, and the Snowballing Technique. The review focuses on four primary subsystems: tracks, civil structures, vehicles, and overhead contact line structures. Key findings reveal that DT has successfully optimized maintenance strategies, improved operational efficiency, and enhanced system safety. Internet of Things (IoT) devices, Artificial Intelligence (AI), machine learning, and cloud computing are critical in implementing DT models. However, challenges like data integration, high implementation costs, and cybersecurity risks remain, necessitating the discussed implications. Future research should focus on improving data interoperability, reducing costs through scalable cloud-based solutions, and addressing cybersecurity vulnerabilities. DT technology has the potential to revolutionize railway infrastructure management, ensuring greater efficiency, safety, and sustainability.

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.

Understanding automated vehicle (AV) behavior in complex road environments and user attitudes in such contexts is critical for their safe and effective integration into smart cities. Despite growing deployment, limited public data exist on AV performance in construction zones; highly dynamic settings marked by irregular lane markings, shifting detours, and unpredictable human presence. This study investigates AV behavior in these conditions through qualitative, video-based analysis of user-documented experiences on YouTube, focusing on Tesla’s supervised Full Self-Driving (FSD) and Waymo systems. Spoken narration, captions, and subtitles were examined to evaluate AV perception, decision-making, control, and interaction with humans. Findings reveal that while AVs excel in structured tasks such as obstacle detection, lane tracking, and cautious speed control, they face challenges in interpreting temporary infrastructure, responding to unpredictable human actions, and navigating low-visibility environments. These limitations not only impact performance but also influence user trust and acceptance. The study underscores the need for continued technological refinement, improved infrastructure design, and user-informed deployment strategies. By addressing current shortcomings, this research offers critical insights into AV readiness for real-world conditions and contributes to safer, more adaptive urban mobility systems.

The growing demand for equitable and efficient transportation solutions has positioned autonomous vehicles (AVs) as a transformative technology with significant potential for rural areas. This literature review examines the challenges and opportunities associated with AV deployment in rural environments, characterized by sparse infrastructure, diverse road conditions, and aging populations. Using a systematic analysis of field tests, simulation-based studies, and survey research, key obstacles are identified, including limited lane markings, unpaved roads, digital connectivity gaps, and user acceptance issues. The results highlight the critical role of advancements in sensor technology, localization methods, and edge computing in addressing these barriers. Additionally, strategic infrastructure modifications, such as enhanced road signage and reliable communication systems, are essential for AV integration. This paper emphasizes the need for tailored AV solutions to meet the specific requirements of rural settings, including adaptability to adverse weather conditions and mixed traffic environments. Insights into public perception reveal the importance of trust-building initiatives and community engagement to foster widespread acceptance. The findings provide actionable recommendations for policymakers, industry leaders, and infrastructure operators, focusing on scalable deployment strategies, policy adaptations, and sustainable solutions. By addressing these challenges, AVs enhance mobility, safety, and accessibility, transforming rural transportation networks into more equitable and efficient systems. This review serves as a foundational reference for future research, charting pathways for the integration of AVs in rural contexts.

In the United States, around 1/3 of the rail network is operated by short lines. These railroads play an important role in the nation's transportation system by serving as the feeder and distributor for the rail network, but often lack a digitized rail track inventory for timely and efficient rail asset management due to limited resources. Much research has been conducted to develop automatic rail extraction methods, since it is a critical step toward a comprehensive digitized rail track inventory. However, existing methods strongly rely on high-density point cloud data sets, sensor property and configuration, and assumptions on global features; therefore, their applications in short lines are limited, since rail tracks will travel through different terrains with various global features, and data sets owned by short lines are mostly low-density data sets with unknown sensor property and configuration. To address these limitations, this study proposes an automatic rail extraction method that can be applied to low-density data sets and is independent of sensor properties/configurations, and global features. The proposed method is tested on the grade-crossing data sets collected by the Federal Railroad Administration (FRA) with a low point density around the track bed area. The performance shows an average completeness of 97.1%, correctness of 99.7%, and quality of 96.8%. This approach helps short lines to establish their own digitized rail track inventory, allowing for effective operation planning and investment strategy, and builds the foundation for future geometry measurements and infrastructure management, thereby improving operational safety and efficiency without significant investment in high-end sensors and high-density data sets.

The U.S. trucking industry has the potential to be an early adopter of autonomous vehicles. The trucking industry hauls the majority of U.S. freight by weight and has a vast infrastructure network. Numerous business cases and routes may be able to utilize autonomous trucks. The trucking industry consumes more fuel and has more crashes than other modes of freight transportation. Trucking companies may be able to reduce costs through labor savings and increased utilization of trucks. All the dynamics of market size, infrastructure, fuel consumption, safety, and cost savings make the trucking industry a potential early adopter of autonomous vehicles. For autonomous technology to be applied to trucks in the United States there needs to be interest from technology developers, truck manufacturers, and trucking companies; government support to allow the testing and deployment of autonomous trucks on public roads; and acceptance from the public who will share the road with autonomous trucks. Given that autonomous truck deployments are in the beginning stages of being tested on public roads, there needs to be a comprehensive review of the market potential of autonomous trucks from the perspective of all involved stakeholders. To provide this comprehensive overview, this paper reviews the current dynamics of autonomous truck deployments in the United States, including deployment markets, business cases, adoption timelines, and logistics-, manufacturing-, operations-, technology-, and government partnerships. The paper concludes with the possible benefits of and barriers to autonomous trucks to illustrate what may drive or impede their U.S. deployment and market potential.

Road quality significantly influences vehicle behavior and safety, with mil -lions of recorded car crashes attributed to deteriorating road conditions, re-sulting in substantial loss of life and economic impact. To enhance driver safety costeffectively, this study aims to leverage smartphones for data col-lection and generate a road roughness index teiined the road impact factor to quantify ride quality for unpaved roads. However, data collected from smartphones on unpaved roads often contains significant noise, posing chal-lenges for extracting useful information for ride quality analysis. To address this, the paper introduces wavelet families including Haar, Daubechies, and Symlets, along with various thresholding techniques, to denoise the collected data. The results are evaluated using metrics mean square error, peak -to -noise ratio, and percentage residual difference. Additionally, an application is developed and designed to integrate the denoising process and generate the road impact factor using the most suitable wavelet filter. This supports driv-ers in making infolined decisions for safer driving experiences on unpaved roads.

Due to the substantial mass disparity between trains and highway vehicles, crashes at Highway-Rail Grade Crossings (HRGCs) are often severe. Therefore, it is essential to develop systematic frameworks for allocating federal and state funds to improve safety at the highest-risk grade crossings. Common techniques for hazard prioritization at HRGCs include the hazard index and the collision prediction formula. A few research projects and state departments of transportation (DOTs) have employed hybrid models that integrate crash hazard indices with prediction models to create comprehensive safety decision-making frameworks. In addition, ranking grade crossings based on their forecasted crash severity likelihood remains largely unexplored, partly due to the complexity of integrating crash severity outputs with hazard indices. This research introduces a new mixed hazard ranking model, the Analytic Hierarchy Process Hazard Index (AHP-HI), which serves as a decision-making tool for ranking grade crossings based on their potential for crash severity. The AHP-HI model combines the analytic hierarchy process (AHP) and the competing risk model (CRM), a prediction model that estimates the likelihood of crash severity for crossings. Risk analysis using the AHP-HI model categorizes public grade crossings in North Dakota into four risk levels, with 4.73% of the crossings identified as high risk.