Design and Evaluate Coordinated Ramp Metering Strategies for Utah Freeways

MPC-641During the past few decades, ramp metering control has been widely implemented in many U.S. states, including Utah. Numerous studies and applications have demonstrated that ramp metering control is an effective strategy to reduce overall freeway congestion by managing the amount of traffic entering the freeway. Ramp metering controllers can be implemented as coordinated or uncoordinated systems. Currently, Utah freeway on-ramps are operated in an uncoordinated way. Despite improvements to the operational efficiency of mainline flows, uncoordinated ramp metering will inevitably create additional delays to the ramp flows. Therefore, this project aims to assist the Utah Department of Transportation (UDOT) in deploying coordinated ramp metering systems and evaluating the performance of deployed systems. First, we leverage a method to identify existing freeway bottlenecks using current UDOT datasets, including PeMs and ClearGuide. Based on this, we select the site that may benefit from coordinated ramp metering from those determined locations. A VISSIM model is then developed for this selected corridor and the VISSIM model is calibrated based on collected traffic flow data. We apply the calibrated VISSIM model to conduct simulations to evaluate system performance under different freeway mainline congestion levels. Finally, the calibrated VISSIM model is leveraged to evaluate the coordinated ramp metering strategy of the bottleneck algorithm from both operational and safety aspects

Analysis of ABC Bridge Column-to-Footing Joints With Recessed Splice Sleeve Connectors

The use of precast concrete in bridge construction has been abundant in recent years because of its efficiency and superior quality control. Precast components are connected at the bridge site to reduce construction time and traffic disturbance. The Grouted Splice Sleeve (GSS) connection provides good bending moment resistance between precast reinforced concrete (RC) components. This connection type has been widely employed in non-seismic areas. The use of this connection in moderate or high seismic zones has been explored and proposed for medium-size highway bridges. It is essential to propose a numerical modeling technique at the local and global level with the proposed connection type. The primary goal of this project is to compare the computational model to experimental results under cyclic quasi-static loading; the computational model is then used to generate the structural response of a bridge bent under seismic loading. Using existing material models and a forced-based beam-column fiber element that accounts for fatigue, bar-slip, purposeful debonding, and plastic hinge length, a computer model capable of predicting structural response under cyclic loading is constructed. The computational model is subsequently utilized to calculate the seismic response of a three-column bridge bent to near-field and far-field earthquakes in terms of overall maximum drift ratio and drift ratio at the maximum level of seismic demand

Developing a Culvert Inspection Manual and Estimating Culverts\u2019 Deterioration Curve, Inspection Frequency and Service Life for UDOT

22-8098Culverts are among the most important assets of state transportation departments. As culvert inspection and maintenance are critical to the safe operation of transportation infrastructure systems and preventing injuries, human life losses, and heavy financial losses, they should be inspected and maintained regularly. Several state DOTs and the American Association of State Highway and Transportation Officials (AASHTO) have published culvert inspection and asset management manuals, which vary widely between states. Despite the effectiveness of these guidelines and manuals, different states consider different qualitative and quantitative parameters, which means that they are specific to each state and may not apply to Utah's culverts. To this end, the purpose of this research is to help UDOT develop a comprehensive system of culvert management by producing a Utah culvert management manual. The research's objectives are threefold: (1) develop a comprehensive inspection and asset management manual for culverts in Utah based on specific characteristics, (2) estimate the deterioration curves for UDOT culverts, and (3) predict the frequency and service life of UDOT culverts. Based on culvert inventories from Colorado, Utah, and Vermont, the final curves were generated using Support Vector Regression (SVR) and Random Forest Regression (RFR) algorithms. Estimating the final deterioration curve for culverts in Utah can be done using the combination of the inventories. After determining the likelihood of failure based on Utah's final culvert deterioration curve, a risk-based prioritization approach was used to determine the frequency of culvert inspections. The final stages of research included generating the final deterioration curve based on the three culvert inventories for Utah's culverts, as well as developing a culvert management manual. In developing the Culvert/Storm Drain Management Manual for Utah, the contents of other states' manuals and federal guidelines for culvert inspection and maintenance were combined and modified for Utah

Image-Based 3D Reconstruction of Utah Roadway Assets

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 and cost issues. Some pioneer departments of transportation in the United States use mobile Light Detection and Ranging (LiDAR) to monitor highway assets and pavement condition data. However, LiDAR is expensive and not affordable for every maintenance agency. Additionally, special technical knowledge is required to perform this method, which may not be accessible to the maintenance agency staff. Recently, image-based 3D reconstruction has been shown to be a cheaper and simpler technology than LiDAR. In this report, we assess the alternative method (image-based) for reconstructing 3D models (virtual 3D point clouds) of transportation agencies. The analysis of the data quality and associated costs holds the promise for conducting a feasible roadway asset inventory

Mobile Phone-Based Artificial Intelligence Development for Maintenance Asset Management

22-8099Transportation asset management needs timely information collection to inform relevant maintenance practices (e.g., resource planning). Traditional data collection methods in transportation asset management require either manual operation or support of unique equipment (e.g., Light Detection and Ranging (LiDAR)), which could be labor-intensive or costly to implement. With the advancement of computing techniques, artificial intelligence (AI) has been developed to be capable of automatically detecting objects in images and videos. In this project, we developed accurate and efficient AI algorithms to automatically collect and analyze transportation asset status, including identification of pavement marking issues, traffic signs, litter & trash, and steel guardrails & concrete barriers. The AI algorithms were developed based on the You Only Look Once (YOLO) framework built on Convolution Neural Network as the deep learning algorithms. Specifically, a smartphone was mounted on the vehicle\u2019s front windshield to collect videos of transportation assets on both highways and local roads. These videos were then converted and processed into labeled images to be training and test datasets for AI algorithm training. Then, AI models were developed for automatic object detection of the listed transportation assets above. The results demonstrate that the developed AI models achieve good performance in identifying targeted objects with over 85% accuracy. The developed AI package is expected to enable timely and efficient information collection of transportation assets, hence, improving road safety

Assessing and Improving Efficiency of Snowplowing Operations via Data and Analytics

69A3551747108This project presents a comprehensive study on enhancing snowplowing routes in 12 regions in northern Utah. The research employs both exact and approximate methods to identify snowplowing routes that lead to reductions in total travel time, turnaround time, and deadhead miles by an average of 4.87%, 15.38%, and 13.85%, respectively, across all the regions. These improvements can significantly enhance the efficiency of snow removal operations and contribute to the overall social welfare. The study's models also examine the tradeoffs between various operational policies, such as echelon vs. non-echelon routing and fleet extension. These insightful analyses empower local management teams to determine the most suitable strategies for their respective regions. Apart from optimization modeling, a pivotal aspect of this work involves data visualization. The team utilizes data visualization techniques to effectively demonstrate the efficacy of the new snowplowing routes, comparing them to current practices, and presenting the findings to the Utah Department of Transportation. This visualization aids in conveying the significance and impact of the proposed improvements, further supporting decision-making processes

Dashcam-Enabled Deep Learning Applications for Airport Runway Pavement Distress Detection

23-8193Pavement distress detection plays a vital role in ensuring the safety and longevity of runway infrastructure. This project presents a comprehensive approach to automate distress detection and geolocation on runway pavement using state-of-the-art deep learning techniques. A Faster R-CNN model is trained to accurately identify and classify various distress types, including longitudinal and transverse cracking, weathering, rutting, and depression. The developed model is deployed on a dataset of high-resolution dashcam images captured along the runway, allowing for real-time detection of distresses. Geolocation techniques are employed to accurately map the distresses onto the runway pavement in real-world coordinates. The system implementation and deployment are discussed, emphasizing the importance of a seamless integration into existing infrastructure. The developed distress detection system offers significant benefits to the Utah Department of Transportation (UDOT) by enabling proactive maintenance planning, optimizing resource allocation, and enhancing runway management capabilities. Future potential for advanced distress analysis, integration with other data sources, and continuous model improvement are also explored. The project showcases the potential of low-cost dashcam solutions combined with deep learning for efficient and cost-effective runway distress detection and management

Automated Safety Assessment of Rural Roadways Using Computer Vision

228094Roadside elements play an important role in the number and severity of crashes. Rigid obstacles (trees, rocks, embankments, etc.), guardrails, clear zones, and side slopes are among the factors that might affect roadside safety. The Federal Highway Administration (FHWA) presented a rating system to help DOTs and transportation agencies make better decisions about improving road segments. However, the manual process of rating road segments is time consuming, inconsistent, and labor intensive. To this end, this project proposed an automated rating system based on images taken from Utah roadways. Utilizing machine-learning algorithms and Mandli images, the developed approach employs the FHWA rating system as the primary standard for assessing roadside safety. To provide more detailed information about safety conditions on the roadside, various computer vision algorithms have been developed to detect each roadside feature. The pre-trained models for available clear zone detection and side slope classification have also been established. A shape-file has been generated by assigning a safety ranking to road segments on five state roads. This product can assist traffic engineers in decision-making to improve road safety by prioritizing projects that address problematic locations. The results show a promising approach to enhancing road safety and preventing crashes

Development of Next Generation Liquefaction (NGL) Database for Liquefaction-Induced Lateral Spread

TPF-5(350)This report presents several advancements in the empirical modeling of liquefaction-induced lateral spread. It starts with a newly collected dataset of 5,560 historical lateral spread displacement vectors, a sample size over 10 times larger than the existing databases and subsurface data comprising over 633 standard penetration test boreholes. This work presents a comprehensive comparison of state-of-the-art empirical models for lateral spreads through Monte Carlo simulations and sensitivity analyses and proposes new evaluation metrics to measure performance. It also quantifies the uncertainty of model weights of the Multiple Linear Regression (MLR) model using Bayesian Statistics. A new functional form is proposed for the MLR model using the least absolute shrinkage and selection operator method. Importantly, the conventional probabilistic framework for predicting lateral spread is expanded to account for the probability of lateral spread triggering given the triggering of liquefaction. This expansion allows us to model zero-displacement lateral spreads despite having liquefaction susceptibility. A convolutional neural network classifier is developed to model the probability of lateral spread triggering with an out-of-fold model accuracy of 90.5%. A new mathematical representation of soil types is presented and trained in the context of liquefaction and lateral spread and boosted model performance

Development of Educational Materials for the Public and First Responders on the Limitations of Advanced Driving Assistance Systems

21-8221With the advancement of automated vehicle technologies, it is critical to understand the knowledge gap among drivers on the limitations and safety restrictions of existing advanced driving assistance systems (ADAS), which contributes to dangerous driving habits and misjudgments. For example, some ADAS include adaptive cruise control, but many drivers do not know that this feature may not function as expected in response to stationary objects. Lane departure warning systems do not always register lane markings or pavement edges with damage or covered with snow. This kind of misunderstanding has led to some highly publicized crashes. Currently, limited information is available related to crashes involving ADAS since there is no proper distinction for this kind of vehicle in current crash reporting. There is also a major concern to understand the cause and fault behind a crash involving this kind of vehicle. Without sufficient knowledge about the functionality of the technology, it is difficult for traffic incident management (TIM) personnel to determine whether the ADAS feature of a vehicle impacted a traffic incident. This study will help TIM personnel better understand ADAS technology by providing a database of commercially available vehicles incorporating this technology and training on terminology and limitations of ADAS. These findings can be used by the Utah Department of Transportation to educate both drivers and first responders