RSS 1.0Center for Safety Equity in Transportation (CSET)
Recent Submissions
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Access, Equity, and Safety in Rural Road Development: Historical Evidence from the Dalton Highway, 1974-2024This study investigates the historical example of the construction of the Dalton Highway—the most significant new road built in Alaska since 1971—to analyze how historical changes in access (both new infrastructures and access policies) and equity (who decides, who benefits, who is seen as a stakeholder) have impacted safety, broadly defined, in rural, isolated, tribal, and indigenous (RITI) communities throughout Northern Alaska.
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Safe Reinforcement Learning for Intersection Management in RITI Communities Under Rare Extreme EventsThe rapid advancement of artificial intelligence (AI) is reshaping the transportation sector, with applications spanning autonomous vehicles, driver injury prevention, and traffic management. Efficient traffic management, particularly through adaptive intersection control, holds significant potential for reducing congestion. This study explores the application of reinforcement learning (RL) to adaptive traffic signal control in rural, isolated, tribal, and indigenous (RITI) communities, which face unique challenges such as rare extreme weather events. Standard RL approaches struggle in these contexts due to limited exposure to these rare events. In our study, we first evaluate several mainstream RL algorithms and identified two most promising approaches. Then, we propose to use offline RL algorithms, which can train on existing datasets before interacting with the real environments. This provides a robust solution because (1) it is costly to deploy the algorithm and let the traffic network operate under suboptimal policies before the algorithm learns the optimal policy, and (2) it mimics the scenario where some events are not seen in the training dataset. We compare the performance of offline RL algorithms using different offline datasets, generated by policies of different levels of expertise, in realistic test cases. Results indicate that offline RL approaches perform better when trained on datasets from expert policies, stressing the importance of the quality of the offline datasets. These findings highlight the potential of RL-based adaptive traffic control for improving transportation efficiency, especially when tailored to the specific conditions of RITI communities.
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EFFECTS OF TOURISM ON RURAL ROADS & RURAL DELIVERY WITH CAVWhile some congestion is expected at rural attractions such as national parks, theme parks, special sporting events, scenic points and the like, there are locations along the rural highway network that nearby attractions cause substantial congestion and/or unusually elevated traffic safety risk. This paper presents the case of two very popular tourist attractions on the North Shore of the Island of Oahu in the State of Hawaii: Laniakea Beach and Shark’s Cove. These locations are within five miles of each other and are served solely by the 2-lane rural Kamehameha Highway. These two locations have been congestion black spots for over a decade, and local opposition to more development and tourism has been substantial. A team of students in civil engineering at the University of Hawaii at Manoa has been meeting with the local communities and has collected sample data to substantiate the extent of the problem. Several discussions were completed, where mitigation proposals were presented and discussed. This paper summarizes both the history of this problem, and the various data collected such as vehicular and pedestrian volumes, travel times and queue lengths. It also presents a list of proposed mitigations. There is a multitude of problems with most of the proposals including cost, appeal (they are not context sensitive), difficulty with agency jurisdictional bounds, community acceptance and risk from waves and long-term sea level rise.
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Center for Safety Equity in Transportation (CSET) Baseline Data and OutreachThe research presented herein summarizes the research and education efforts conducted at the University of Alaska Fairbanks as part of the Outreach, Synthesis, and Baseline Data efforts promoted by the Center for Safety Equity in its first year of operation. These key areas were to effectively engage relevant parties needed for larger research efforts, better evaluate what work has been done and how effective existing programs are and establish a baseline using existing and relevant data to inform and evaluate CSET safety efforts and identify commonalities and differences in RITI safety needs across the consortium states. Dozens of outreach events were hosted by CSET and several other events in which CSET participated to develop and foster key connections and collaborations with community members and agencies. CSET help sponsor and coordinate a regional conference focusing on transportation safety, bringing together diverse perspectives on the safety needs of rural, isolated, tribal, and indigenous (RITI) communities. CSET regularly participated in the annual Alaska Federation of Natives convention, using it as an opportunity for outreach and knowledge gathering. Three-dimensional models were designed and printed for the purpose of demonstrating the challenges of building roads in Alaska. Available data sources were compared to determine and highlight deficiencies in current analytical frameworks and support the need to incorporate other data sources in rural and tribal communities for the purposes of transportation planning and safety evaluation.
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Promoting Positive Traffic Safety Culture in RITI Communities through Active Engagement: Implementation Guide and Outreach ActivitiesRural, Indigenous, Tribal, and Isolated (RITI) communities’ crash data analysis clearly highlights three major areas of concern: prevalence of excessive speed, impaired and distracted driving, and underage driving. Safety-focused educational programs and awareness campaigns have all contributed to a reduction in crashes in urban areas. However, in RITI communities, much more work is still needed. It is important that communities are provided with the proper resources and methods to deliver the appropriate training and educational tools that promote and cause a significant positive change in the traffic safety culture. Through reviewed literature and interviews with tribal community stakeholders, this research team came to understand that tribal youth are most impacted and engaged when educational material is made culturally relevant. We then developed an implementation guide to be used by tribes to create, develop, and enact a sustained educational program with the mission to positively impact traffic safety culture among youth in tribal and rural communities.
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Developing a Low-Cost Open-Source Traffic Counter for Rural Areas (CTRA)This research addresses operational disconnects and knowledge gaps related to traffic data collection in rural areas by developing a low-cost 3D-printed and open-source traffic counter (CTRA). Conventional pneumatic tube-based systems, which are still in use by transportation agencies across the United States because of their affordability, simply do not work on gravel roads and have difficulty counting non-motorized users and differentiating non-traditional vehicles from conventional motor vehicles. CTRA was developed and field tested at the University of Alaska Fairbanks and designed to provide a video-based data collection system that overcomes the limitations of other traffic counting devices. A count rate of 100% was achieved during the calibration process. Other than electronic hardware, most pieces of hardware can be printed on a 3D printer to form a simple and robust case and mounting system and only straps are needed to secure the counter to a fixed object. Because of its relatively simple and affordable design, CTRA could also be used for STEM and educational activities in schools and other related programs.
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Road Safety NuggetsThe current state of learning and engagement is much more audiovisual than it has been. Additionally, the use of cartoons can help break down certain social barriers and have the potential to promote thinking and discussion on critical and important societal issues such as climate change conservation, and transportation safety culture. A growing amount of literature supports the use of art as an effective means of science communication because of the visceral and emotional responses that are elicited when engaging the imagination. To that end, cartoons(i.e., comic panels) are used here to present timely and relevant transportation safety issues addressed at the Center for Safety Equity in Transportation (CSET), a Tier 1 University Transportation Center led by the University of Alaska Fairbanks with partners at the University of Hawaii, University of Idaho, and University of Washington. By blending art and deep knowledge of transformative engineering research, the authors hope to help the public better understand and digest complex transportation safety issues.
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INCORPORATING USE INSPIRED DESIGN IN PROVIDING SAFE TRANSPORTATION INFRASTRUCTURE FOR RITI COMMUNITIESIn this study, we focus on automating road marking extraction from the HDOT MLS point cloud database, managed by Mandli. Mandli is a company specializing in highway data collection, including LiDAR. Mandli has cooperated with various Department of Transportation throughout the United States. Here, we focus on infrastructure elements related to non-motorized travel modes, supporting the ongoing Complete Streets efforts in Hawaii. Point cloud data include different colors that represent differences in elevation and intensity values. Based on a visual inspection, road markings can be observed within these point clouds. The long-term objective of this study is to develop a framework and approach for automating the detection of these infrastructure elements, based on deep learning approaches. For this project, a YOLOv5 (You Only Look Once version 5) image object detection model was trained with the HDOT point cloud data. YOLO is a family of deep learning models designed for fast object detection; the latest published version is the 5th version. The focus here is on non-motorized objects, such as crosswalks, bike lanes and bike boxes. The same approach can be extended to other markings as well, which we plan for subsequent studies.
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DESIGNING AND PLANNING FOR SAFE PEDESTRIAN PATHS AT RAIL TRANSIT STATIONS SERVING RITI COMMUNITIESIn this study, we focus on pedestrian network construction and pedestrian route choice analysis. We developed a GIS based framework for pedestrian network construction, which takes multiple data sources, such as open source networks, satellite imagery, and pedestrian GPS traces. The pedestrian route choice study examines the impact from tradeoffs between environmental and infrastructure attributes, such as ambient noise, tree canopy shade, and surface characteristics (e.g., sidewalk, grass, etc.). We investigate these in a university campus setting, where walking trips comprise about 25% of all commute trips, with a greater percentage expected for within campus OD trips. We collect and analyze GPS data from volunteer community members of the University of Hawaii at Manoa (UHM), resulting in 298 distinct observed OD trips and their routes. From a RUM route choice standpoint, choice set generation is a difficult problem, especially for on-campus walking, which is unrestricted and can deviate from discrete roadways or sidewalks. Thus, a recursive logit route choice model is estimated to determine the tradeoffs between route link attributes, such as ambient noise, tree canopy shade, and other infrastructure attributes. The estimated recursive logit model and network construction framework were applied to four identified Skyline stations to analysis the pedestrian route choice behavior when accessing the stations.
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Development and validation of a reduced one-dimensional thermos-mechanical soil stability model for predictive use with the Alaska RWISA numerical tool was developed that helps forecast when thaw occurs at depths in a road embankment during spring thaw in regions that experience seasonal freeze and thaw. The tool is a Excel spreadsheet that uses a single adjustable parameter, and is driven by time series air temperature data. The model agrees well with archived data of subsurface temperatures at five different highway locations in Alaska.
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Comprehensive Transportation Equity Analysis for RITI Community: A Data-Driven Approach with Case StudyThe report presents a comprehensive analysis addressing safety equity concerns for rural communities and American Indian and Alaskan Native (AI/AN) populations, who experience disproportionate rates of serious injuries, fatalities, and general collisions. Despite these disparities, significant gaps exist in understanding the demographics of collisions, particularly within tribal communities where law enforcement jurisdictions are complex, and individuals may misreport their tribal status to gain benefits, leading to biases in collision data. This study aims to fill these gaps by developing a statistical model to predict the true demographics of collisions and enhance safety equity. An ecological regression model, accounting for individual-level characteristics influencing collision rates, is employed. Focusing on Yakima County, Washington—a rural area with the large Yakama Nation reservation—the study examines the impact of household income and AI/AN status on collision rates across three categories: all collisions, injury collisions, and fatal collisions. The results reveal that lower-income individuals are slightly overrepresented in collisions, while higher-income individuals are underrepresented. However, AI/ANs are significantly overrepresented in all collision types, being 3.8 times more likely to be involved in fatal collisions compared to the general population. These findings highlight the utility of ecological regression in revealing the true demographics of collisions and underscore critical safety equity issues in rural and AI/AN communities.
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Cost-Effective System for Rural Roadway Traffic, Surface Conditions and Weather Conditions MonitoringThis report presents the Mobile Unit for Sensing Traffic (MUST) project, which addresses the critical need for comprehensive traffic data in tribal and rural areas that often suffer from disproportionately high rates of traffic incidents due to limited infrastructure and resources. By implementing a low-cost, user-friendly data collection system, the MUST project aimed to provide real-time traffic information to significantly enhance traffic safety. The pilot installation in Yakama Nation, Washington, at a high-traffic intersection known for frequent accidents, marks a crucial step in this initiative. Collaboration with the Yakama Nation's Tribal Traffic Safety Coordinator and Yakama Power ensured the successful installation and operation of the sensor on a strategically selected telephone pole. Equipped with advanced machine learning technology, the MUST system collects detailed data on traffic flow, road surface conditions, and environmental factors such as temperature and humidity, visualized through a sophisticated dashboard for real-time monitoring and data-driven decision-making. This system allows for the identification of high-risk areas and the implementation of targeted safety measures, such as improved signage and road maintenance, while addressing specific concerns like pedestrian safety, visibility issues due to heavy fog, and speeding. By providing a robust dataset previously unavailable, the MUST project supports the Yakama Nation’s efforts to understand and mitigate traffic safety issues, ultimately enhancing the overall safety and quality of life for the community. This pilot project serves as a model for other tribal and rural areas looking to leverage advanced technology to improve their transportation safety infrastructure.
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Comprehensive Roadway Safety Data Visualization and Evaluation Platform for Yakama NationThe Yakama Nation Department of Natural Resources (DNR) Engineering collaborated with the Smart Transportation Application & Research Laboratory (STAR Lab) at the University of Washington to develop a comprehensive roadway safety data visualization and evaluation platform. With the U.S. Department of Transportation’s Safety Data Initiative (SDI) fund, this tool will support information for the Yakama Nation government for their decision-making. The safety datasets provided in this tool consist of collision records (collision, vehicle, occupant, pedestrian) and roadway characteristics (roadlog, curve and grade, ramp, traffic information, special-use lane, etc.). The multi-source database supported data collection, quality control, integration, database management, visualization, and analytical results. The safety tools can be utilized for analytical and visualization functions such as crash data visualization, hotspot identification, and network screening. Examples of available safety data include crash type, frequency, severity, and risk estimate, and safety data download.
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Assessing Pedestrian Safety on Roads Through Machine Learning Approaches for State Highways in Washington StateThe report presents a unique contrast for several Machine Learning approaches aiming at understanding pedestrian fatal collisions. Four classification techniques are applied to assess how roadway features mainly correlate to pedestrian fatal crashes: Logistic Regression, Nearest Neighbor Classification, Decision Tree, and Random Forest Classifier. The data used in this project was collected from the Highway Safety Information System (HSIS) database, which provides both collision data for the entire state of Washington and roadway characteristics for all state highways. Each of the four modeling approaches was implemented using K-fold cross-validation, a process that allows choosing the best parameters for the model. Their results were evaluated and then compared in terms of accuracy score and confusion matrices for the testing data set. It was found that the Decision tree had consistent results and the best performance among all models, showing how the distinct predictors relate to each other to predict fatal pedestrian collisions.
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Convolutional Neural Network Learning Approaches for Driver Injury Severity Classification and Application in Single-Vehicle Crashes in RITI CommunitiesIt is crucial to examine the characteristics and attributes of traffic crashes in Rural, Isolated, Tribal, or Indigenous (RITI) communities using statistical and data-driven methods. However, traditional crash data analysis faces challenges due to unobserved heterogeneities and temporal instability. To address these issues, a fusion convolutional neural network with random term (FCNN-R) model is developed for driver injury severity analysis. The proposed model consists of a set of sub-neural networks (sub-NNs) and a multi-layer convolutional neural network (CNN). Seven-year (2010-2016) single-vehicle crash data is applied. The proposed model outperformed other five typical approaches in the predictability comparison. In addition, unobserved heterogeneity, which has been recognized as a critical issue in crash frequency modelling, generates from multiple sources, including observable and unobservable factors, space and time instability, crash severities, etc. In this project, hierarchical Bayesian random parameters models with various spatiotemporal interactions are further developed to address as well. Selected for analysis are the yearly county-level alcohol/drug impaired-driving related crash counts data of three different injury severities including minor injury, major injury, and fatal injury in Idaho from 2010 to 2015. Significant temporal and spatial heterogenous effects are detected in all three crash severities. These empirical results support the incorporation of temporal and spatial heterogeneity in random parameters models.
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Enhancing Vehicle Sensing for Traffic Safety and Mobility Performance Improvements Using Roadside LiDAR Sensor DataRecent technological advancements in computer vision algorithms and data acquisition devices have greatly facilitated research activities towards enhancing traffic sensing for traffic safety performance improvements. Significant research efforts have been devoted to developing and deploying more effective technologies to detect, sense, and monitor traffic dynamics and rapidly identify crashes in in Rural, Isolated, Tribal, or Indigenous (RITI) communities. As a new modality for 3D scene perception, Light Detection and Ranging (LiDAR) data have gained increasing popularity for traffic perception, due to its advantages over conventional RGB data, such as being insensitive to varying lighting conditions. In the past decade, researchers and professionals have extensively adopted LiDAR data to promote traffic perception for transportation research and applications. Nevertheless, a series of challenges and research gaps are yet to be fully addressed in LiDAR-based transportation research, such as the disturbance of adverse weather conditions, lack of roadside LiDAR data for deep learning analysis, and roadside LiDAR-based vehicle trajectory prediction. In this technical report, we focus on addressing these research gaps and proposing a series of methodologies to optimize deep learning-based feature recognition for roadside LiDAR-based traffic object recognition tasks. The proposed methodologies will help transportation agencies monitor traffic flow, identify crashes, and develop timely countermeasures with improved accuracy, efficiency, and robustness, and thus enhance traffic safety in RITI communities in the States of Alaska, Washington, Idaho, and Hawaii.
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Pedestrian Safety Analysis using Computer VisionThe goal of this research project was to explore the capabilities of computer vision for pedestrian safety analysis. Computer vision, an AI application in image processing, tracks the movement of cars, bikes, and pedestrians, offering superior information about speed, trajectory, and count data for various transportation modes. The University of Idaho acquired two computer vision sensors from the startup Numina. This project funded the installation and one year of data access. In collaboration with the City of Moscow, we identified a test location, but the sensors failed to provide the necessary data. Consequently, we pivoted to the open-source computer vision package YOLOv8. Our research then focused on YOLOv8’s capabilities for pedestrian safety analysis. The first task tested detection accuracy, and the second compared different model sizes, or “brain sizes,” of YOLOv8, which range from smaller, faster models to larger, more accurate ones. Accuracy tests compared average detection confidence across various zones and times of day, revealing that cars in high daylight had the highest confidence levels, while objects closer to the camera and oriented perpendicularly were detected more accurately. In contrast, objects at skewed angles and farther distances had lower confidence levels. The model size comparison showed that larger models, despite requiring more time and storage, produced significantly higher-quality detections.
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An evaluation of GPR monitoring methods on varying river ice conditions: A case study in AlaskaIce roads and bridges across rivers, estuaries, and lakes are common transportation routes during winter in regions of the circumpolar north. Ice thickness, hydraulic hazards, climate variability and associated warmer air temperatures have always raised safety concerns and uncertainty among those who travel floating ice road routes. One way to address safety concerns is to monitor ice conditions throughout the season. We tested ground penetrating radar (GPR) for its ability and accuracy in measuring floating ice thickness under three specific conditions: 1) presence of snow cover and overflow, 2) presence of snow cover, and 3) bare ice, all common to Interior Alaska rivers. In addition, frazil ice was evaluated for its ability to interfere with the GPR measurement of ice thickness. We collected manual ice measurements and GPR cross-sectional transects over 2 years on the Tanana River near Fairbanks, Alaska, and for 1 year on the Yukon River near Tanana, Alaska. Ground truth measurements were compared with ice thickness calculated from an average velocity model created using GPR data. The error was as low as 2.3–6.4% on the Yukon River (Condition 3) and 4.6–9.5% on the Tanana River (Conditions 1 and 2), with the highest errors caused by overflow conditions. We determined that certain environmental conditions such as snow cover and overflow change the validity of an average velocity model for ice thickness identification using GPR, while frazil ice accumulation does not have a detectable effect on the strength of radar reflection at the ice-water interface with the frequencies tested. Ground penetrating radar is a powerful tool for measuring river ice thickness, yet further research is needed to advance the ability of rural communities to monitor ice thickness using fewer time-intensive manual measurements to determine the safety of ice cover on transportation routes.
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Promoting Positive Traffic Safety Culture in RITI Communities through Active Engagement: Implementation Guide and Outreach ActivitiesRITI crash data analysis clearly highlights three major areas of concern: prevalence of excessive speed, impaired and distracted driving, and underage driving. Safety-focused educational programs and awareness campaigns have all contributed to a reduction in crashes in urban areas. However, in RITI communities, much more work is still needed. It is important that communities are provided with the proper resources and methods to deliver the appropriate training and educational tools that promote and cause a significant positive change in the traffic safety culture. Through reviewed literature and interviews with tribal community stakeholders, this research team came to understand that tribal youth are most impacted and engaged when educational material is made culturally relevant. We then developed an implementation guide to be used by tribes to create, develop, and enact a sustained educational program with the mission to positively impact traffic safety culture among youth in tribal and rural communities.
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Assessing the Relative Risks of School Travel in Rural CommunitiesThis study examined school travel safety and risk and explored the potential differences between conditions that are present today with those that existed nearly two decades ago, when the Transportation Research Board published its landmark study on school travel safety. For this study, thirty transportation professionals were interviewed and a twenty-year crash data set from the Fatality Analysis Reporting System (FARS) was analyzed. The responses from the interviews were separated into ten common themes. The three most mentioned themes were education programs, concerns of roadway environments, and school bus safety. Based on the responses, concerns about the roadway environment, poor driver behavior, and the role of parents on mode choice have not changed in the last twenty years; however, safety education programs, vehicle centric travel, community planning, and pick up/drop off safety have evolved over time. With regard to the FARS data set, which was used as a benchmark to assess school transportation safety, the overall trends indicate that the trip to and from school remains a relatively safe activity, particularly along rural facilities where positive results were identified across four key metrics. Along urban facilities, slightly increasing trends were observed in the annual number of fatalities and in the number of non-motorists involved in a fatal crash, suggesting that opportunities remain to enhance and to improve the travel environment for school children.
