Raj Bridgelall

Highway–rail crossing inventories report train activity but do not characterize the freight dependence associated with each location. This study developed an auditable framework to compare modeled freight importance with reported through-train activity across the contiguous United States. The framework routed domestic interzonal freight analysis framework (FAF) rail flows (4,911 directed origin–destination pairs; 846.802 million short tons; $370.293 billion annually) along minimum-length network paths. An 11 m association limit and positive-exposure requirement retained 50,643 crossings, represented by 49,854 analytical localities. A crossing freight importance index combined percentile ranks of tonnage, freight value, and origin–destination reach. Rail-network neighborhoods compared focal importance with neighboring activity through descriptive spatial statistics and extreme-quintile classes. Direct association was modest (Spearman ρ = 0.311), while the bivariate Moran statistic was positive (0.275). Across four weighting schemes and three neighborhood sizes, 3,213 localities remained high–high, and 1,516 of 2,013 baseline disagreement localities retained their class. Among common localities, median high–high Jaccard overlap with baseline was 0.859 under independent ±10% routing-cost perturbations and 0.614 under regional ±25% perturbations; overlap was 0.806 under alternative access selection. Positive aggregate correspondence persisted, but screening locations depended on routing and access assumptions. The resulting catalog links classifications to original crossings and processing diagnostics for targeted verification. The findings describe concordance within the retained population; they do not establish operational routes, crossing-level freight accuracy, or safety risk.

As generative artificial intelligence (GAI) reshapes business education, instructors must simultaneously safeguard academic integrity and develop students’ AI literacy for an increasingly AI-driven workplace. This study developed the GAI Learning Framework (GLF), a replicable, step-by-step experiential learning approach for case-based projects in undergraduate business courses, illustrated through an operations management (OM) application. Rather than enabling passive cognitive offloading, the GLF promotes active learning by requiring students to craft context-rich prompts grounded in course materials, systematically compare outputs across multiple GAI platforms (e.g., ChatGPT, Claude, and Gemini), and critically evaluate discrepancies against independently verified sources. The framework was implemented across two sections of an undergraduate OM course and evaluated using a student survey that combined Likert-scale items with open-ended reflections (n = 50). All three constructs exceeded the scale midpoint, with pooled means ranging from 3.93 (Constructive Alignment) to 4.08 (Cognitive Load/Scaffolding). Qualitative student reflections further indicated that students perceived the framework as fostering active learning, AI platform literacy, and professional communication skills. This study offers educators a practical, classroom-tested framework for integrating GAI tools into case-based projects in business courses while preserving analytical rigor and promoting experiential learning. Supplemental Material: The online appendices are available at https://doi.org/10.1287/ited.2026.0224 .

National freight frameworks consider volume, economic value, and network connectivity, but public data do not directly identify freight dependence at individual railroad bridges. This gap limits consistent national screening for preservation and resilience planning. This study developed a reproducible national workflow to estimate bridge-level freight exposure and identify regional concentrations of important bridges. The workflow assigned Freight Analysis Framework interzonal rail flows to a connected rail network in the contiguous United States, associated eligible bridges with routed edges, and calculated tonnage, value, and distinct origin–destination (OD) reach. These measures formed a zero-preserving, percentile-based rail-bridge freight infrastructure importance index (RB-FII) that gave equal total weight to economic scale and network reach. Tie-inclusive selection and density-based clustering identified important bridge groups, while parallel-track and component-weight sensitivity analyses tested stability. The model routed all 4911 retained OD pairs, representing 846.80 million short tons and $370.29 billion, and linked 68,326 of 69,356 processed bridges (98.52%) to the routable network. The nominal top 1000 cutoff yielded 1003 bridges after retaining ties; 939 formed 32 regional clusters, and 64 remained regionally isolated. Parallel-track alternatives produced bridge-rank correlations of 0.977–0.979 and identical cluster relationships among common bridges. High-emphasis index component weight scenarios retained correlations of 0.988–0.995 with the baseline. The workflow provides an auditable national screening method for directing detailed engineering, operational, and resilience assessments toward bridges and corridors with high modeled freight dependence. The RB-FII is conditional on modeled routes and does not measure observed train routing, bridge condition, failure probability, or disruption loss.

National freight frameworks consider volume, economic value, and network connectivity, but public data do not directly identify freight dependence at individual railroad bridges. This gap limits consistent national screening for preservation and resilience planning. This study developed a reproducible national workflow to estimate bridge-level freight exposure and identify regional concentrations of important bridges. The workflow assigned Freight Analysis Framework interzonal rail flows to a connected rail network in the contiguous United States, associated eligible bridges with routed edges, and calculated tonnage, value, and distinct origin–destination (OD) reach. These measures formed a zero-preserving, percentile-based rail-bridge freight infrastructure importance index (RB-FII) that gave equal total weight to economic scale and network reach. Tie-inclusive selection and density-based clustering identified important bridge groups, while parallel-track and component-weight sensitivity analyses tested stability. The model routed all 4,911 retained OD pairs, representing 846.80 million short tons and $370.29 billion, and linked 68,326 of 69,356 processed bridges (98.52%) to the routable network. The nominal top 1,000 cutoff yielded 1,003 bridges after retaining ties; 939 formed 32 regional clusters, and 64 remained regionally isolated. Parallel-track alternatives produced bridge-rank correlations of 0.977–0.979 and identical cluster relationships among common bridges. High-emphasis index component weight scenarios retained correlations of 0.988–0.995 with the baseline. The workflow provides an auditable national screening method for directing detailed engineering, operational, and resilience assessments toward bridges and corridors with high modeled freight dependence. The RB-FII is conditional on modeled routes and does not measure observed train routing, bridge condition, failure probability, or disruption loss.

Aggregate livability indices consistently rank Fargo, North Dakota, among the most livable small-to-mid-sized U.S. cities, yet city-level scores obscure whether favorable average performance reflects equitable neighborhood-level access to essential amenities. However, evidence on whether favorable citywide livability rankings correspond to equitable neighborhood-level access in small- and mid-sized U.S. cities remains limited. To address this gap, this study conducts a tract-level spatial accessibility analysis in Fargo, examining access to grocery stores, healthcare facilities, and parks across 38 census tracts using road network-based nearest-facility assignment, a Composite Accessibility Index (CAI), and Gini coefficients. Results reveal that despite strong aggregate rankings, approximately 9700 residents (8.0% of the study-area population of 121,219) reside in the bottom composite accessibility quartile. Standard Gini coefficients ranged from 0.081 to 0.093, while population-weighted Gini coefficients were lower (grocery = 0.040; healthcare = 0.042; parks = 0.029; CAI = 0.029), indicating modest citywide inequality. Nevertheless, the worst-served peripheral tract recorded a CAI of 0, with network distances ranging from 6.9 to 7.8 miles across the three amenity types. A sensitivity analysis confirms robustness across threshold choices. These findings indicate that modest citywide inequality can coexist with localized accessibility disadvantages in peripheral tracts, supporting spatially disaggregated, equity-oriented livability assessment.

Advanced Air Mobility (AAM) is increasingly recognized as a promising approach for improving healthcare logistics, yet evidence remains fragmented across aviation, transportation, healthcare, and digital infrastructure. This study examined the operational, clinical, and institutional evidence to characterize the evolution of healthcare-focused AAM, identify dominant research themes, and determine critical knowledge gaps. The review followed PRISMA-ScR guidelines and combined bibliometric analysis, thematic synthesis, and semantic network analysis of 168 peer-reviewed studies published between 2015 and 2025 and retrieved from IEEE Xplore, ScienceDirect, Scopus, and Web of Science. The analysis identified six thematic clusters encompassing system design, healthcare logistics, biological specimen transport, emergency response, health equity, and digital infrastructure. Publication activity increased rapidly after 2020. Emergency response represented the most mature research domain, and pharmaceutical logistics, longitudinal operational validation, and health equity remained comparatively underdeveloped. These findings demonstrate that healthcare-focused AAM functions as a sociotechnical system requiring coordinated advances in technology, governance, institutional integration, and equitable access to support clinically reliable and operationally sustainable healthcare delivery.

Bridge management systems primarily prioritize structural condition, while pavement management systems focus on roadway performance. Neither provides a unified measure of the potential for bridges to impede traffic movement by jointly considering infrastructure condition and traffic demand. This separation limits the ability of stakeholders to identify bridges where preservation investments may yield the greatest mobility benefits across a national transportation network. This study develops a mobility impedance index (MII) by integrating pavement roughness, bridge deck condition, current traffic demand, and projected traffic growth into a single composite measure. A transparent and transferrable data-integration and prioritization methodology was developed to spatially integrate the Highway Performance Monitoring System (HPMS) and National Bridge Inventory (NBI), producing a national analytical dataset of 79,595 open, unrestricted mainline NHS bridges in the contiguous United States. The index was evaluated using distributional analysis, local Getis-Ord hotspot analysis, sensitivity testing, and temporal comparability assessment. The MII exhibited a well-behaved generalized normal distribution and identified 20,621 bridges as statistically significant local hotspots, concentrated primarily within major metropolitan regions and high-demand transportation corridors. Sensitivity analyses demonstrated that the proposed MII is robust, with bridge-rank correlations ranging from 0.807 to 0.965 across the alternative specification scenarios and hotspot-set similarities ranging from 0.446 to 0.801. Restricting the analysis to bridges whose component observation years were within three years produced virtually identical prioritization results (Spearman ρ = 0.999), confirming that temporal differences among the component datasets have negligible influence on bridge rankings. The proposed MII provides transportation agencies with a nationally scalable data-driven decision-support method for network-level bridge prioritization that complements existing bridge and pavement management practices by identifying bridges and regions where preservation investments are likely to provide the greatest mobility benefits and support more sustainable allocation of limited infrastructure preservation resources.

Highway–rail grade crossing (HRGC) safety has improved substantially over recent decades through engineering upgrades, active warning systems, crossing closures, enforcement, and public education. Recent national trends, however, suggest that these gains may have slowed, raising the question of whether HRGC safety has entered a persistent plateau. This study investigates whether the historical decline in U.S. HRGC incidents has transitioned into a statistically distinct safety regime and whether the factors associated with casualty occurrence have changed following that transition. An analytical framework integrating regime-transition analysis, cross-regime casualty comparison, and explainable machine learning was applied to nationwide Federal Railroad Administration incident records from 1976 to 2025. Trend analysis, complementary stationarity diagnostics, residual diagnostics, information criteria, and sensitivity analysis consistently identified 2012 as the onset of a statistically stationary safety plateau. Comparison of casualty outcomes showed no meaningful change in either the probability of casualty occurrence or the distribution of injury and fatality outcomes following the transition. Explainable random forest models further demonstrated substantial temporal stability in the factors associated with casualty occurrence. Train speed, vehicle occupancy, driver presence, and highway-user actions remained the dominant predictors across both safety regimes, with driver presence ranking among the most influential characteristics during the plateau period. These findings indicate that the current safety challenge is not the emergence of new collision mechanisms but the persistence of well-established operational and behavioral risk factors. Future reductions in HRGC casualties will likely require targeted engineering improvements, advanced warning technologies, connected-vehicle and vehicle-to-infrastructure systems, artificial intelligence-enabled monitoring, and focused public education to address the persistent residual risks sustaining the national safety plateau.

Highway–rail grade crossing (HRGC) incidents in the United States declined substantially for several decades before stabilizing in recent years. Understanding this persistence is important because future safety improvements may depend on identifying locations where incident occurrence remains resistant to further reduction. This study developed an integrated framework to characterize persistent HRGC incident environments using 50 years (1976–2025) of Federal Railroad Administration incident records. Trend, structural-break, variance, and stationarity tests were first applied to determine whether the historical decline transitioned into a distinct persistence regime. A county-level persistence index (PI) was then developed to quantify the combined effects of incident burden and resistance to decline during the plateau period. Distributional analysis characterized the statistical behavior of the PI, while global and local Moran’s I statistics evaluated its spatial organization. Explainable machine learning methods were subsequently used to identify incident characteristics associated with elevated persistence. The results identified a statistically significant regime change around 2010. Prior to 2010, incidents exhibited a strong declining trend, whereas the subsequent period displayed a statistically significant but substantially weaker decline, lower variance, and behavior consistent with a persistence regime characterized by a markedly attenuated rate of improvement. The PI followed a strongly right-skewed distribution that was best represented by a bounded heavy-tailed unit log-logistic model, indicating that persistence is concentrated within a relatively small subset of counties. Spatial analysis revealed significant positive spatial autocorrelation (Moran’s I = 0.180, p = 0.001) and geographically coherent clusters concentrated primarily in the southeastern United States and several major freight-oriented regions. Explainable machine learning models identified train-operating characteristics, warning device contexts, movement patterns, and temporal conditions as key attributes associated with high-persistence counties. The findings demonstrate that the post-2010 incident plateau is sustained disproportionately by a limited number of geographically concentrated environments and provide a framework for supporting more targeted safety interventions.