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Existing bridge deterioration models rely on subjective national bridge inventory (NBI) condition ratings from visual inspections, which lack the objective assessments needed for informed repair and maintenance decisions. Non-destructive evaluation (NDE) tests, such as impact echo (IE), provide quantitative...
In recent years, transportation agencies have increasingly turned to machine learning (ML) to enhance the effectiveness of infrastructure asset management. However, limited local inventory data often hinders building accurate and reliable ML models. Additionally, data privacy and ownership concerns discourage...
Despite the ADA’s passage in 1990, non-compliant pedestrian infrastructure remains widespread, often due to the lack of comprehensive pedestrian infrastructure data. Traditional methods of measuring compliance are time consuming, prompting the need for more efficient approaches. This research explores...
This report investigates the application of low-cost sensing technologies, including GPS, accelerometers, and smartphones, to monitor roadway pavement conditions in real time. By leveraging widely available sensors embedded in vehicles, this research demonstrates how machine learning models can detect...
The Americans with Disabilities Act (1990) mandates that paratransit services should be comparable to fixed-route systems. However, with only 5% of the population utilizing public transit, this comparison does not adequately highlight the disparities between persons with disabilities and those without....
Potholes are a significant pavement distress compromising safety and causing costly damage. They result from pavement deterioration due to aging, weather, and traffic overloads, with the Mountain Plains region particularly affected due to freeze/thaw cycles. Timely identification and repair of potholes...
The rapid increase in traffic volume and heavy vehicle loads is causing accelerated deterioration of pavement infrastructure worldwide. This research investigates the impact of overweight and dynamic axle loads on pavement performance, employing advanced weigh-in-motion (WIM) systems and machine learning...
Transportation asset management requires timely information collection to inform relevant maintenance practices. Traditional data collection methods often necessitate manual operation or the use of specialized equipment, e.g., light detection and ranging (LiDAR), which can be labor-intensive and costly...
Understanding ramp queue length and queuing time is important for transportation agencies to manage and operate the ramps with optimum performance. Since these data are collected with conventional sensor systems such as coils, they are prone to error, especially during traffic congestion. The increased...
Understanding the condition of roadway assets is important for transportation agencies to plan for future improvements and asset management purposes quantitatively. Since these assets are distributed across the country, a manual data collection system falls short of the automated methods due to time...