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Bridge strikes caused by over-height vehicles frequently damage steel girders and can reduce their load-carrying capacity, requiring rapid and reliable evaluation to ensure structural safety. Current evaluation practices rely on manual measurements that often require traffic disruptions and simplified assessment approaches that do not fully capture the effects of damage. In addition, finite element simulations are time-consuming and computationally intensive to develop for practical rapid evaluation. This study developed an integrated, data-driven framework for the inspection and assessment of damaged steel bridge girders. A semiautomated procedure was developed to process laser-scan point cloud data and extract displacement profiles along the girder span. Machine learning models were trained and validated using a dataset generated from finite element simulations informed by field measurements of actual bridge strike incidents to predict the residual capacity of damaged girders. Explainable artificial intelligence techniques were incorporated to interpret model predictions and identify key parameters influencing structural performance. Results demonstrated the framework’s capabilities in effectively capturing girder displacements, and for accurately and efficiently estimating the residual capacity, to support post-strike bridge evaluation. Measured girder deformations obtained from laser scanning can be used as inputs to the machine learning models for quantifying a girder’s capacity. The predicted capacity can then be incorporated into bridge rating software to compute rating factors, enabling rapid decisions on traffic restrictions and load posting. Additionally, the interpretability results can guide engineers in identifying critical design parameters and prioritizing repair actions.

The objective of this study is to evaluate whether the current SCDOT criterion requiring the roadway subgrade to be 1 ft above the design-event ditch water surface can be relaxed under selected geometric and soil conditions, particularly where larger shoulder widths and flatter side slopes increase horizontal separation between ditch water and pavement structure.

Aggressive driving is a prevalent traffic safety concern that contributes to crash risk and undermines traffic safety culture. Building on Phase 1 work that defined aggressive driving and identified promising intervention points, this Phase 2 study examined two complementary strategies to reduce aggressive driving: supporting bystanders (i.e., spouses/partners, family members, and close friends) to intervene with drivers who engage in aggressive driving, and developing and testing media messages to encourage safer driving among drivers themselves. Key informant interviews were conducted with bystanders. Findings informed the development of a three-step bystander intervention that included expressing concern and connecting to shared values. The intervention was tested with a national sample of 102 bystanders. Results demonstrated increased bystander confidence, greater use of constructive strategies, and perceived reductions in aggressive driving frequency. Participants reported that the strategies were feasible, acceptable, and helpful. Media messages were developed in three main themes: cognitive reappraisal, adaptive response, and prosocial driving. Messages were tested with 864 drivers who reported recent engagement in aggressive driving. Findings indicated that, across message themes, messages were well received, prompted reflection on driving behavior, and supported changes in perceptions related to safety and responsibility. Together, these findings suggest that engaging bystanders and deploying supportive, nonjudgmental messaging are promising, complementary approaches to reducing aggressive driving and strengthening traffic safety culture. Resources were developed: a bystander conversation guide, a community dissemination toolkit, and a messaging development resource.

Pedestrian crosswalks, bike lanes, and medians are key safety devices in a multi-modal highway network. However, there is no statewide map of these assets' locations and types. Such a map would benefit planning, maintenance, safety assessments, and network analyses for ODOT and its partners. Currently, ODOT collects asset information through site visits and surveys, which are labor-intensive and costly across the state's vast road network. GIS databases are created ad-hoc and inconsistently, often by interns. Fortunately, ODOT Engineering Automation regularly collects 3D mobile lidar data, providing geometric and radiometric information. This research reviewed state-of-the-art approaches for detecting and classifying pedestrian crosswalks, bike lanes, and medians, and developed a methodology to extract these features from LiDAR datasets for updating ODOT’s GIS platform. The approach was tested on several datasets and showed good extraction performance, requiring minimal manual cleanup. Lastly, a plan is provided for ODOT to implement this research.

This study investigated the use of co-simulation for evaluating cooperative driving automation (CDA) applications within a work zone environment. The examined use case focused on the merging maneuvers of a single CDA-equipped vehicle into either a CDA platoon or a traffic stream consisting of human-driven vehicles near a work zone with a one-lane blockage. The findings demonstrate that co-simulation can serve as an effective component of automated vehicle and CDA testing, particularly in complex and challenging environments such as work zones. The study further demonstrated that cooperative lane-changing behavior near work zones can be systematically assessed within a controlled simulation environment, and that the resulting performance measures can help identify limitations and guide improvements to existing automated and cooperative driving algorithms and control logic.

Providing safe and secure parking for the more than three million heavy trucks that move over 70% of the nation’s freight is both a local and national issue. This is especially true for a major freight hub such as Louisiana, with its freight-intensive corridors, including I-10 and I-12. Currently, there is only one parking spot for every 11 trucks in the state, and these shortages lead to illegal parking, creating safety, operational, and economic challenges that ripple through Louisiana's freight system. These include non-compliance with Hours-of-Service regulations, economic and freight inefficiencies, and quality-of-life issues for impacted communities. This report presents the results of research that evaluated truck parking conditions in Louisiana and proposes short- and long-term solutions to address these problems. This study employed a mixed-methods approach to data collection and analysis, combining a literature review that synthesized state and national policies and local zoning laws, stakeholder surveys and interviews, GIS and spatial analyses, qualitative thematic analysis, and economic modeling using IMPLAN to assess the impacts of developing truck parking facilities in Louisiana. The cost of not investing in truck parking in the state was also calculated. Data were collected from a wide range of stakeholders, including truck drivers, trucking companies, communities, local government officials, truck parking companies, and law enforcement personnel, providing the necessary input for analyzing issues related to truck parking in Louisiana. Conclusions and recommendations were developed from a rigorous analysis and interpretation of the data. Recommendations focused on identifying priority corridors and locations where optimal benefits from parking investments could be achieved, developing model language for local zoning ordinances to support increased truck parking development in the state, and identifying potential funding avenues and management matrices to help policymakers determine the best approach to financing parking investments. Findings from this study will enable decision-makers to comprehensively address the challenges of providing adequate truck parking in Louisiana, a key to improved freight efficiency, enhanced safety, and more livable communities.

Florida-produced fine aggregates are generally of suitable quality, and deleterious effects associated with organic matter are uncommon. However, naturally occurring organic constituents can affect cement hydration and early-age strength development depending on their concentration, composition, and interaction with cementitious phases. The sands evaluated in this study contained relatively low organic contents, making establishment of a universal organic-content threshold impractical. FDOT currently uses AASHTO T 21 as the primary screening method for potentially deleterious organic impurities and requires AASHTO T 71 testing when screening results indicate concern. This framework should be maintained. AASHTO T 21 provides effective statewide screening, while AASHTO T 71 provides performance-based confirmation. However, AASHTO T 21 is qualitative, and AASHTO T 71 requires extended testing that can delay aggregate acceptance decisions. Neither elemental analysis nor the Modified Walkley-Black method demonstrated sufficient precision, consistency, or correlation with AASHTO T 21 to support statewide acceptance criteria. However, MWB may be used to establish minespecific organic-carbon baselines, and isothermal calorimetry may be used to establish mine-specific hydration-response baselines. Deviations from these baselines may indicate the need for additional evaluation, including AASHTO T 71 testing. Organic-carbon contents below approximately 0.015% did not measurably affect hydration or compressive strength. Alkaline washing reduced measured organic content and increased pH but did not consistently improve strength. The ASTM C109 hand-tamped consolidation procedure is recommended for AASHTO T 71 mortar cube fabrication. Trace inorganic constituents, including phosphorus, did not produce measurable strength effects.

The Federal Railroad Administration contracted ENSCO to investigate the application of model-assisted probability of detection (MAPOD) to handheld ultrasonic rail flaw inspection. This research was conducted as part of a project that began in 2019 to investigate methods for evaluating the performance of track inspection technologies. The demonstration of MAPOD presented in this study points to several opportunities for further research. For example, hit-miss POD analysis could be extended to accurately show the probability of sizing flaws, and MAPOD simulations could include additional physical and human factors. Most importantly, the MAPOD approach developed in this study could also be adapted and applied to other track inspection technologies, such as track geometry measurement

Truck platooning, a technology where connected and automated trucks travel in closely linked convoys, promises significant reductions in fuel consumption and emissions. However, these long convoys present a major safety hazard on existing roadways, particularly on rural two-lane undivided highways. On these roads, human drivers must use the oncoming traffic lane to overtake, and a long truck platoon acts as a dangerous and extended barrier. To address this problem, we established a comprehensive framework to safely integrate platoons into mixed traffic. The research progressed through four steps. First, we modeled the precise time and distance required for a human driver to safely pass a platoon. Second, we developed a dynamic algorithm that determines safe platooning limits, such as the maximum number of trucks allowed, based on real-time road geometry and oncoming traffic. Third, we tested these rules using advanced traffic simulations. Finally, we designed an active gap opening control system, allowing automated trucks to proactively split apart and provide a safe harbor for passing vehicles. Our findings demonstrate that while long platoons operate safely on multilane highways, they require strict, dynamic limitations on two-lane roads. This research provides transportation policymakers with a clear, data-driven blueprint. By shifting from rigid rules to context-aware operational rules and utilizing cooperative gap opening technologies, agencies can preserve the economic benefits of truck platooning while rigorously protecting the safety of everyday drivers.

The Federal Railroad Administration sponsored a research team from Engineering Services, Inc. to perform an analysis of the effectiveness of the thermal protection system on a DOT-117 rail car as a fire protection measure. This research was carried out between February 2022 and March 2023 at multiple ESi locations; the experimental tests were performed at ESi’s Houston, Texas office. The objective of this work was to evaluate the performance of the thermal protection system in rail cars as a risk mitigation control to prevent a catastrophic thermal event. The research group also analyzed the heat transfer from a fire to rail car contents to understand the effectiveness of a potential fire response from a local fire department.

The Federal Railroad Administration (FRA) contracted a research team from TTCI (now MxV Rail) to assess the potential use of multi-band and directional antennas to meet the current and future wireless communication needs of the U.S. railroad industry. FRA supported this project due to the high level of complexity involved in developing requirements for and quantifying the benefits of multi-band and directional antennas. The research team found that significant communications capacity gains could be achieved in dense urban areas using more directional antennas, particularly on base stations, with additional gains possible through increased use of directional antennas at wayside locations and/or use of smart antenna systems on locomotives.

The Highway Safety Manual (HSM; AASHTO, 2010) is described by the American Association of State Highway and Transportation Officials (AASHTO) as “the premier guidance document for incorporating quantitative safety analysis in the highway transportation project development process.” The current HSM (1st edition) was published in 2010, with an update expected to be released. Driver behaviors, including speeding, distraction, and drowsiness, are widely recognized as major contributors to roadway crashes, and the HSM devotes a full chapter to providing detailed background and the core elements on human factors that affect the interaction of drivers and roadways. However, the quantitative components of the HSM seldom make the contribution of driver behavior explicit.

NCHRP Web-Only Document 474: Incorporating Driver Behavior and Characteristics into Safety Prediction Methods, produced by TRB’s National Cooperative Highway Research Program, explores whether speed data and driver behavior-related elements documented in crash reports could be used as direct measures to improve crash prediction beyond existing HSM methods. The report suggests that current HSM Safety Performance Functions (SPFs) already capture much of the influence of driver behavior indirectly through roadway geometry, traffic exposure, and other infrastructure characteristics. Although the project did not find sufficient evidence to support the development of new behavior-based SPFs for the HSM, it advanced safety research and practice by developing the Probabilistic Induced Exposure (PIE) methodology and strengthening confidence in the current HSM methodology.