Collections in this community

Recent Submissions

  • Convolutional Neural Network Learning Approaches for Driver Injury Severity Classification and Application in Single-Vehicle Crashes in RITI Communities

    Zhang, Guohui; Yang, Hanyi; Yuan, Runze (2024-07)
    It 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.
  • Enhancing Vehicle Sensing for Traffic Safety and Mobility Performance Improvements Using Roadside LiDAR Sensor Data

    Zhang, Guohui; Zhou, Shanglian (2024-06)
    Recent 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.
  • Pedestrian Safety Analysis using Computer Vision

    Champlin, Daniel; Hanson, Lane; Lowry, Michael (2024-06-18)
    The 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.
  • An evaluation of GPR monitoring methods on varying river ice conditions: A case study in Alaska

    Richards, Elizabeth; Stuefer, Svetlana; Rangel, Rodrigo Correa; Maio, Christopher; Belz, Nathan; Daanen, Ronald (Elsevier, 2023-03-08)
    Ice 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.
  • Promoting Positive Traffic Safety Culture in RITI Communities through Active Engagement: Implementation Guide and Outreach Activities

    Pehrson, Jacob; Prescott, Logan; Abdel-Rahim, Ahmed (2023-09)
    RITI 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.
  • Assessing the Relative Risks of School Travel in Rural Communities

    Chang, Kevin; Souvenir, Brandt (2023-09)
    This 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.

    Chang, Kevin; Williams, Jade (2023-07)
    Autonomous, or self-driving, vehicles have the capability to either fully or partially replace a human driver in the navigation to a destination. To better understand how receptive society will be to these types of vehicles, this study focused on the perceived level of trust in autonomous vehicles (AVs) by rural drivers and passengers. An online survey that examined the behavioral and value-based perspectives of drivers was developed and distributed to respondents across the United States, and a total of 1,247 valid responses were collected and analyzed. Based on the results, rural (and non-rural) respondents had similar levels of trust when comparing self-driving vehicles with human-driven vehicles, though older people and those with less education tended to have less trust in self-driving vehicles. The outcomes from this study can be used to support targeted outreach efforts for those drivers who remain skeptical about the overall safety benefits of this evolving transportation technology area.
  • Drone-based Computer Vision-Enabled Vehicle Dynamic Mobility and Safety Performance Monitoring

    Zhang, Guohui; Yuan, Runze; Prevedouros, Panos; Ma, Tianwei (2023-01-30)
    This report documents the research activities to develop a drone-based computer vision-enabled vehicle dynamic safety performance monitoring in Rural, Isolated, Tribal, or Indigenous (RITI) communities. The acquisition of traffic system information, especially the vehicle speed and trajectory information, is of great significance to the study of the characteristics and management of the traffic system in RITI communities. The traditional method of relying on video analysis to obtain vehicle number and trajectory information has its application scenarios, but the common video source is often a camera fixed on a roadside device. In the videos obtained in this way, vehicles are likely to occlude each other, which seriously affects the accuracy of vehicle detection and the estimation of speed. Although there are methods to obtain high-view road video by means of aircraft and satellites, the corresponding cost will be high. Therefore, considering that drones can obtain high-definition video at a higher viewing angle, and the cost is relatively low, we decided to use drones to obtain road videos to complete vehicle detection. In order to overcome the shortcomings of traditional object detection methods when facing a large number of targets and complex scenes of RITI communities, our proposed method uses convolutional neural network (CNN) technology. We modified the YOLO v3 network structure and used a vehicle data set captured by drones for transfer learning, and finally trained a network that can detect and classify vehicles in videos captured by drones. A self-calibrated road boundary extraction method based on image sequences was used to extract road boundaries and filter vehicles to improve the detection accuracy of cars on the road. Using the results of neural network detection as input, we use video-based object tracking to complete the extraction of vehicle trajectory information for traffic safety improvements. Finally, the number of vehicles, speed and trajectory information of vehicles were calculated, and the average speed and density of the traffic flow were estimated on this basis. By analyzing the acquiesced data, we can estimate the traffic condition of the monitored area to predict possible crashes on the highways.

    Ban, Xuegang (Jeff); Abramson, Daniel; Zhang, Yiran; Lukins, Sarah; Goodrich, Kevin; Mirante, Andrea; Lambert, Rachel; Yankey, Mykala (2023-02)
    Transportation and traffic safety is a primary concern within Rural, Isolated, Tribal and Indigenous (RITI) communities in Washington State. Emerging technologies such as connected and autonomous vehicles, sensors and drones have been tested and developed to improve traffic safety, but these advances have largely been limited to urban areas. This project identified opportunities and challenges of adopting drone technologies in RITI communities, and explored context-sensitive applications to traffic safety and related goals. In three phases, the team conducted community workshops, online surveys and other outreach activities with state and county agencies responsible for emergency management and crisis response in coastal Tribal and non-tribal communities; a planning studio and Comprehensive Plan Update for the City of Westport and its surrounding South Beach community straddling two rural counties and including the Shoalwater Bay Indian Tribe; and a pilot educational program with the School District that serves it. To be effective in rural contexts, adoption of drone technology depends on a broadening of local skill development and needs to target diverse community goals. In short, it needs to be broadly embedded in the community. Taking this sociotechnical approach, we focused on long-term workforce development and designed and implemented an after-school program (October 2021 – June 2022) for Ocosta Junior High School students. The course taught students how to assemble and pilot drones and apply them to a variety of practical needs including public works inspection, search and rescue, and environmental monitoring of coastal flooding.
  • Development of an Acoustic Method to Collect Studded Tire Traffic Data

    Chang, Kevin; Alhasyah, Meeloud (2023-02)
    Travel during winter months remains particularly problematic in the Pacific Northwest due to the regular occurrence of inclement weather in the form of snow and ice during freezing and sub-freezing conditions. For travelers and commuters alike, vehicle traction in the form of studded tires serves to provide an added level of driving confidence when weather conditions deteriorate. However, recurring studded tire usage causes damage to the roadway infrastructure in the form of surface wear and rutting over time. Left unattended, this damage contributes to challenging and potentially dangerous driving conditions in the form of standing water and the increased potential for hydroplaning. Currently, an efficient and automated method to collect site-specific studded tire traffic volumes is lacking. While studded tire usage can be locally estimated based on manual roadway traffic counts, parking lot counts, or household surveys, the lack of real-world traffic volumes prevents the fine-tuning of roadway deterioration models that measure performance and estimate infrastructure life. This project tested the use of off-the-shelf sound meters to determine if an acoustic method could be developed to measure studded tire volumes. Based on the results, a prediction model was developed to allow for data-driven solutions that will benefit local transportation officials, planners, and engineers responsible for managing highways and roadways.
  • Assessing the Transportation Adaptation Options to Sea Level Rise for Safety Enhancement in RITI Communities through a Structured Decision-Making Framework

    Shen, Suwan; Shim, Dayea (2023-01-18)
    Through a structured decision-making framework, this study aims to better understand the key factors influencing transportation adaptation planning in practice. Qualitative, semi-structured, in-depth interviews with various stakeholders were conducted to identify the main concerns, challenges, objectives, tradeoffs, and evaluation variables in transportation adaptation planning. Stakeholders were identified through preliminary interviews with transportation planning experts from the metropolitan planning organization using typical case and snowball sampling methods. Key aspects related to the major concerns, objectives, priorities, adaptation plan evaluations, implementation challenges, and potential conflicts and tradeoffs are identified. Major barriers to adaptation plan development and implementation include lack of resources, competing with more urgent needs, conflicts with other planning objectives, lack of holistic view, working in silos, mismatched and outdated information, uncertainty in future scenarios, and action inertia. To overcome these challenges, we propose 1) more efforts to understand community values, develop strategic goals, and identify their priorities in order to balance the tradeoffs 2) collaboration with other sectors to develop a holistic view of resilience and strategic plans that achieve multiple planning goals 3) collaborate with diverse stakeholders to reduce spatial and temporal information mismatches and to create adaptive plans that can accommodate multiple scenarios with uncertainty 4) conduct community outreach and stakeholder engagement from the beginning to build support, consolidate resources, and eliminate social inertia for plan implementation.

    Zhang, Guohui; Yang, Hanyi; Yu, Hao; Li, Zhenning; Zou, Rong; Yuan, Runze; Ma, Tianwei (2022-09)
    This report documents the research activities to investigate the traffic crashes in Rural, Isolated, Tribal, or Indigenous (RITI) communities involving considerable incapacitating injuries and fatalities. The traffic crashes occurring in RITI communities, are different from urban traffic crashes, and are related more to the features like speeding, low application of safety devices (for instance, seatbelt), adverse weather conditions and lacking maintenance and repairs for road conditions, and inferior lighting conditions. Thus, it is necessary to study the properties and attributes of traffic crashes at the RITI area using data analysis methods, such as statistical methods, and data-driven methods. This project is trying to analyze the rural crash injury and fatality patterns caused by changing climates in RITI communities based on enhanced data analysis using latest mathematical method. The mixed logit model to examine the risk factors in determining driver injury severity in four crash configurations in two-vehicle rear-end crashes on state roads based on seven-years of data from the Washington State Department of Transportation. The differences between the MLM and the LCM are investigated for exploring the relationships between driver injury severity in the rain-related rural single-vehicle crash and its corresponding risk factors. Moreover, this project develops a latent class mixed logit model with temporal indicators to investigate highway single-vehicle crashes and the effects of significant contributing factors to driver injury severity. The results of this research will be beneficial to transportation agencies to propose effective methods to improve rural crash severities under special climate and weather conditions and minimize the rural crash risks and severities.

    Vasudevan, Vinod; Kapourchali, Mohammad Heidari (2022-03-30)
    Rural intersections are high-risk locations for road users. Particularly, during the nighttime, lower traffic volumes make it difficult for drivers to discern an intersection despite traffic signs. The lack of alertness may lead to severe crashes. An effective way to reduce the likelihood of crashes at isolated intersections is to warn road users of the intersection in advance. A smart-lighting system can detect approaching vehicles using sensors and transmit this information to a receiver to illuminate the intersection. By deploying a demand-responsive light, it is expected that the system will provide adequate warning to road users, both motorized and non-motorized. This report documents the development and deployment of a smart-lighting system at the University of Alaska Anchorage (UAA).
  • Improving Safety for RITI Communities in Idaho: Documenting Crash Rates and Possible Intervention Measures

    Lowry, Michael; Swoboda-Colberg, Skye; Prescott, Logan; Abdel-Rahim, Ahmed (2022-03-23)
    This report describes a new set of Geographic Information System (GIS) tools that we created to conduct safety analyses. These new GIS tools can be used by state DOTs and transportation agencies to document crash rates and prioritize safety improvement projects. The tools perform Network Segment Screening, the first step in the Roadway Safety Management Process (RSMP) outlined in the Highway Safety Manual (HSM). After developing these new tools, we conducted two case studies to demonstrate how they can be used. The first case study was for screening intersections. Our analysis included all intersections on the Idaho State Highway System. In practice, the analysis would likely be done only for a subset of intersections, such as only for signalized intersections on urban arterials. We chose all intersections for illustration purposes. The result was a ranking of intersections that would most likely benefit from safety improvement efforts. We applied three performance measures to rank the intersections: Crash Frequency, Crash Rate, and Equivalent Cost. The second case study was for screening roadway segments. Again, the entire Idaho State Highway System was included for illustration. The HSM describes two key methods for screening roadway segments: Simple Ranking and Sliding Window. Both methods are available in the new tools. This case study demonstrates the advantage of the Sliding Window, which would be impractical to accomplish on a large scale without the assistance of our new GIS tools. The final part of the work presented in this report is a synthesis to identify and document possible measures to reduce crashes for RITI communities in Idaho and throughout the northwest region.
  • Evaluation of Delivery Service in Rural Areas with CAV

    Prevedouros, Panos; Alghamdi, Abdulrahman (2022-03-15)
    Urban areas have been experiencing automated delivery technology for several servings of food or a few bags of groceries, with automated (robotic) mini vehicles. The benefits of such automated delivery may be much more significant for rural areas with long distances due to the large potential savings in travel time, travel cost, and crash risk. Compared to urban areas, rural areas have older and more disabled residents, longer distances, higher traffic fatality rates, and high ownership of less fuel-efficient vehicles such as pickup trucks. An evaluation of connected autonomous vehicle (CAV) delivery service in rural areas was conducted. A detailed methodology was developed and applied to two case studies: One for deliveries between Hilo and Volcano Village in Hawaii as a case of deliveries over a moderate distance (~50-mile roundtrip) in a high-energy-cost environment, and another for deliveries between Spokane and Sprague in Washington State as a case of deliveries over a longer distance (~80-mile roundtrip) in a low-energy-cost environment. The delivery vehicles were based on the same compact van: A person-driven gasoline-powered van, a person-driven electric-powered van, and a CAV electric-powered van. The case study results suggest that the CAV van can be a viable option for implementing a delivery business for rural areas based on the evaluation results that accounted for a large number of location-specific costs and benefits and the number of orders served per trip.
  • Barriers and Opportunities for Using Rail-Trails for Safe Travel in Rural, Isolated, and Tribal Communities

    Lowry, Michael; Chang, Kevin (2021-11)
    This project explored barriers and opportunities for more effectively using rail-trails for safe travel in rural, isolated, tribal, and indigenous communities. We investigated using crowdsourced data from a fitness app to estimate bicycle volumes on trails. For 10 locations this new method produced suitable results, but for 19 locations the method was not satisfactory. Future research could identify situations in which this new method is feasible. We also created a new mapping tool to get demographic data surrounding locations where new rail-trails could be built. We identified 8,616 miles of potential rail-trail in the Pacific Northwest and explored the surrounding demographics for 12 locations in rural communities in Idaho, Oregon, and Washington. We conducted two separate surveys to solicit community member opinions and usage habits of the Trail of the Coeur d’Alenes.
  • Developing a Data-Driven Safety Assessment Framework for RITI Communities in Washington State

    Wang, Yinhai; Sun, Wei; Ricord, Sam; de Souza, Cesar Maia; Yin, Shuyi; Tsai, Meng-Ju (2021-09-10)
    The roadway safety of the Rural, Isolated, Tribal, or Indigenous (RITI) communities has become an important social issue in the United States. Official data from the Federal Highway Administration (FHWA) shows that, in 2012, 54 percent of all fatalities occurred on rural roads while only 19 percent of the US population lived in rural communities. Under the serious circumstances, this research aims to help the RITI communities to improve their roadway safety through the development of a roadway safety management system. Generally, a roadway safety management system includes two critical components, the baseline data platform and safety assessment framework. In our Year 1 and Year 2 CSET projects, a baseline data platform was developed by integrating the safety related data collected from the RITI communities in Washington State. This platform is capable of visualizing the accident records on the map. The Year 3 project further developed the safety data platform by developing crash data analysis and visualization functions. In addition, various roadway safety assessment methods had been developed to provide safety performance estimation, including historical accident data averages, predictions based on statistical and machine learning (ML) models, etc. Beside roadway safety assessment methods, this project investigated the safety countermeasures selection and recommendation methods for RITI communities. Specifically, the research team has reached out to RITI communities and established a formal research partnership with the Yakama Nation. The research team has conducted research on safety countermeasures analysis and recommendation for RITI communities.

    Pereira, Luana Carneiro; Prevedouros, Panos (2021-09-30)
    Dashboard cameras and sensors were installed in 233 taxi vans on Oahu, Hawaii which produced several hours of events classified as naturalistic driving data (NDD) in a period of seven months between fall 2019 and spring 2020. The study achieved its objectives to: (1) collect data from NDD events where driving maneuvers caused an acceleration of 0.5g or higher; (2) develop a database suitable for statistical analysis; (3) derive basic statistics for all variables; (4) investigate correlations between variables; and (5) further investigate correlations (which may represent causality effects) for the most frequent types of events, using stepwise linear regression models. The database included a total of 402 harsh events, of which were 398 near-crashes and four were crashes. Several variables such as road, environmental, driver and vehicle characteristics were coded for each event. The installation of Samsara by the CTL company proved to be a successful tool for coaching drivers, and for providing useful insights into traffic safety factors relating to near-miss events.
  • Extracting Rural Crash Injury and Fatality Patterns Due to Changing Climates in RITI Communities Based on Enhanced Data Analysis and Visualization Tools (Phase I)

    Zhang, Guohui; Prevedouros, Panos; Ma, David; Yu, Hao; Li, Zhenning; Yuan, Runze (2021-09)
    Traffic crashes cause considerable incapacitating injuries and losses in Rural, Isolated, Tribal, or Indigenous (RITI) communities. Compared to urban traffic crashes, those rural crashes, especially for those occurred in RITI communities, are heavily associated with factors such as speeding, low safety devices application (for instance, seatbelt), adverse weather conditions and lacking maintenance and repairers for road conditions, inferior lighting conditions, and so on. Therefore, there exists an urgent need to investigate the unique attributes associated with the RITI traffic crashes based on numerous approaches, such as statistical methods, and data-driven approaches. This project focused on extracting rural crash injury and fatality patterns due to changing climates in RITI communities based on enhanced data analysis and visualization tools. Three new interactive graphic tools were added to the Rural Crash Visualization Tool System (RCVTS), to enhance the visualization approach. A Bayesian vector auto-regression based data analysis approach was proposed to enable irregularly-spaced mixture-frequency traffic collision data interpretation with missing values. Moreover, a finite mixture random parameters model was formulated to explore driver injury severity patterns and causes in low visibility related single-vehicle crashes. The research findings are helpful for transportation agencies to develop cost-effective countermeasures to mitigate rural crash severities under extreme climate and weather conditions and minimize the rural crash risks and severities in the States of Alaska, Washington, Idaho, and Hawaii.
  • BUILDING CAPACITY FOR CLIMATE ADAPTATION Assessing the Vulnerability of Transportation Infrastructure to Sea Level Rise for Safety Enhancement in RITI Communities

    Shen, Suwan; Shim, Dayea (2021-09-01)
    Sea level rise (SLR) and more frequent extreme weather events are an emerging concern for transportation infrastructures in coastal areas. In particular, the livelihoods and transportation safety of vulnerable populations such as indigenous rural communities may be at higher risk to sea-level rise and exacerbated coastal flooding due to their heavy dependence on natural resources, settlements in relatively isolated fringe land, limited accessibility to services, and alternative economic activities, as well as lack of resources and tools for adaptation. Despite existing studies on sea-level rise’s impacts, there is a lack of understanding of how the impacts of tidal flooding and sea-level rise may be unevenly distributed both spatially and socially, and how vulnerable (e.g. rural, relatively isolated) communities have experienced such impacts and perceive future risks. Using survey data, this project helps to better understand the current experience and risk perception of different communities when facing sea-level rise and more frequent coastal flooding. It helps to understand different communities’ perceived travel challenges with coastal flooding, the social sensitivity to different types of challenges, and the priorities and concerns to access various types of resources with the projected sea-level rise. The findings could be used to develop adaptation strategies that improve communities’ safe access to highly valued resources and activities.

View more