The United States Department of Transportation (USDOT or DOT) is one of the executive departments of the U.S. federal government. It was established by Congress in the Department of Transportation Act on October 15, 1966.The department began operation on April 1, 1967. It is headed by the secretary of transportation, who reports directly to the President of the United States and is a member of the president's Cabinet.The department's mission is "to develop and coordinate policies that will provide an efficient and economical national transportation system, with due regard for need, the environment, and the national defense.
This study presents an integrated spatial economic and travel demand modeling framework for quantifying the monetary benefits of transportation infrastructure, an essential element of project impact assessment. Building on existing literature in spatial economic modeling (SEM), the study enhances current practices by integrating a SEM with a regional travel demand model to simulate the interactions among land use, accessibility, and economic activity. Rather than focusing solely on direct travel-related benefits, the framework captures broader and more nuanced economic effects, including productivity gains, land use shifts, and improved market access. The framework is applied to a representative infrastructure scenario in Alberta to illustrate its practical utility. The results demonstrate the value of combining spatial economic theory with empirical modeling to generate geographically detailed and policy-relevant economic impact estimates. This approach supports more comprehensive and data-informed investment evaluations aligned with regional planning and decision-making criteria.
Coherent anti-Stokes Raman spectroscopy (CARS) is an established technique for acquiring highly accurate, nonintrusive gas temperature measurements. CARS was selected for this study because other temperature measurement approaches would lose their accuracy in the extreme environment of the liquid-fueled rich-burn, quick-quench, lean-burn combustor configuration that was studied. These measurements were motivated by a goal of quantifying changes in combustor primary zone gas temperature as flames approach lean blowout (LBO). Both stable burning and near-LBO data were acquired at axial locations with (i.e., x = 24 mm) and without (i.e., x = 40 mm) fuel droplets using two liquid fuels with very different LBO boundaries. Motorized translation stages were used to move the probe volume to different spatial locations in the combustor without misaligning the CARS system. The x = 40 mm results demonstrate that the average temperature in the central recirculation zone (CRZ) decreases similar to 300 K between the stable burning and the threshold of LBO operating condition. Furthermore, the measured gas temperature distributions were very similar when both fuels burned near their respective LBO boundaries, which suggests that the temperature distribution is influenced more by the proximity to LBO than the global equivalence ratio. Measurements that were acquired further upstream at x = 24 mm showed temperature distributions that differed significantly from the x = 40 mm data. Specifically, the temperature distribution was much wider at x = 24 mm, with temperature values ranging from the air inlet temperature to the stoichiometric adiabatic flame temperature. The x = 24 mm CARS data showed a notable increase in cool gases and a notable decrease in hot gases near LBO as compared to the stable burning results. A single test case was captured where LBO happened during the data acquisition process. This confirmed that LBO is preceded by significant entrainment of cool gases. Particulate matter (PM) emissions measurements were also acquired as part of this work. The PM measurements showed a nonmonotonic dependence on the equivalence ratio, where soot production increased to some extent as the equivalence ratio was decreased. This unusual soot production observation is linked in this work to differences in fuel spray patterns that accompany a flame shape change. This flame shape change was evaluated in the authors' previous work, where it was found to result from a helical vortex structure that develops in the flow at certain conditions and disrupts the fuel spray.
Railroad-highway grade crossings (RHGCs) are critical points of conflict between roadway and rail systems, contributing to over 2000 crashes and 250 fatalities annually in the United States. This study applied machine learning methods (ML) techniques to model and predict crash frequency at RHGCs, using a comprehensive dataset from the Federal Railroad Administration (FRA) and Tennessee Department of Transportation (TDOT). The dataset included 807 validated crossings, incorporating roadway geometry, traffic volumes, rail characteristics, and control features. Five ML models—Random Forest, XGBoost, PSO-Elastic Net, Transformer-CNN, and Autoencoder-MLP—were developed and compared to a traditional Negative Binomial (NB) regression model. Results showed that ML models significantly outperformed the NB model in predictive accuracy, with the Transformer-CNN achieving the lowest Mean Squared Error (21.4) and Mean Absolute Error (3.2). Feature importance analysis using SHAP values consistently identified Annual Average Daily Traffic (AADT), Truck Traffic Percentage, and Number of Lanes as the most influential predictors, findings that were underrepresented or statistically insignificant in the NB model. Notably, the NB model failed to detect the nonlinear relationships and interaction effects that ML algorithms captured effectively. While only three variables were statistically significant in the NB model, ML models revealed a broader spectrum of critical crash determinants, offering deeper interpretability and higher sensitivity. These findings emphasize the superiority of machine learning approaches in modeling RHGC safety and highlight their potential to support data-driven interventions and policy decisions for reducing crash risks at grade crossings.
Ultra-high performance concrete (UHPC) is becoming more widely adopted as a repair material for civil infrastructure applications. As such, monitoring the health of repairs made with UHPC using nondestructive evaluation (NDE) methods will be needed, which raises the question: Which conventional NDE methods can be successfully used to evaluate UHPC repairs, given that UHPC has no coarse aggregate, higher strength and stiffness, and a high volume of steel fiber reinforcement? This paper aims to investigate the application of different NDE methods for assessing the bond behavior of UHPC bridge deck overlays to conventional concrete. Three structural slab specimens were created with artificial defects to represent deterioration that may occur in typical bridge deck overlays systems, including a reference slab without an overlay, a slab with a highly roughened interface between the overlay and substrate, and a slab with a relatively smooth interface. The NDE technologies used to interrogate the specimens included impact echo, ultrasonic surface waves, ultrasonic shear horizontal wave tomography, ground penetrating radar, and infrared thermography. After NDE assessment, direct tension bond pull-off testing was conducted to verify the NDE results. The results demonstrate that impact echo and ultrasonic surface waves remained unaffected by the UHPC interface and successfully detected embedded defects in concrete specimens. Ultrasonic tomography could also detect defects; however, the resolution of the results was not as clear when the interface between the overlay and substrate was highly roughened. Conversely, the UHPC overlay significantly affected the results obtained through ground penetrating radar scans using electromagnetic waves.
The aviation industry and regulatory authorities encounter significant challenges in deploying highly automated aircraft. A primary concern is the impracticality of flight testing mission- relevant tasks across a wide range of operating conditions to ensure the safety of automation systems. This challenge is particularly pronounced for vertical takeoff and landing (VTOL) aircraft where assuring acceptable flying qualities is complicated by intricate flight control systems with over-actuated designs, complex aerodynamic effects such as interlacing propulsor wakes, and the operational demands of urban and adverse atmospheric conditions. To address these challenges, this paper outlines a framework for flying qualities testing put forth by the Automated Flying Qualities (AFQ) Committee15 which aims to establish best practices for flight testing and certifying VTOL aircraft. Key recommendations include: (i) the development of a unified and aircraft-agnostic framework for analyzing highly automated aircraft, grounded in linear systems theory, statistical and simulation-based evaluations, and targeted flight testing; (ii) the formulation of a unified definition of control loops applicable to highly automated flight control systems; and (iii) the introduction of a rating scheme for automated flying qualities, which builds upon the Cooper-Harper Rating system to evaluate an aircraft's performance, pilot workload, and machine compensation capabilities. This paper serves as a description of the challenges facing the testers, both in industry and regulatory authorities, on ensuring these aircraft are safe to operate. The proposed framework is designed to uncover latent flight control pathologies that could lead to catastrophic outcomes in scenarios where a qualified pilot is not actively engaged in the control loop. While these recommendations are particularly tailored for complex VTOL aircraft, they hold relevance for the broader category of highly automated aircraft.
