The U.S. Department of Energy (DOE) Office of Nuclear Energy's Office of Storage & Transportation has developed a web-based, geospatial information and decision support application Stakeholder Tool for Assessing Radioactive Transportation (START) to assist with evaluating routing options and other aspects associated with the transportation of used nuclear fuel and high-level radioactive waste shipments. START provides DOE with a comprehensive planning tool in support of the transportation of radioactive materials. Users can navigate maps and data layers of the contiguous United States from the web-based platform as well as generate routes based on user-provided inputs. START is maintained and updated to support the user community and finds wide and varied applications among its users.

Abstract. Future climate conditions project an increase in the frequency and severity of flooding in many regions of the world. Evaluation of candidate flood adaptation strategies must consider risk assessment methods that capture scenario-based loss and damage (L&D) for cost-benefit analysis. There is a need to develop tools that improve understanding of a region’s risk exposure while recognizing data and resource limitations available for this purpose. This study aims to address this gap by employing a novel approach that utilizes historic L&D data with an eye towards the current end-tail of extreme flood events as a prognosticator of what the future might hold. A hybrid Monte Carlo simulation technique is deployed to develop flood L&D projections under future climate change scenarios and used to estimate return periods of extreme flood events. Application of this methodology is illustrated in a case study using the Northeast region of the United States. The results show decreases in expected return periods of large flooding events, thereby expanding the geographic area of increased risk. These findings suggest this approach could function as a promising screening tool to help guide local flood adaptation planning, including the possibility of adopting this approach for other extreme weather events.

As the US experiences increased losses due to extreme weather and climate change–related events, it has become important for transportation agencies, such as state Departments of Transportation (DOTs), to manage transportation networks with resiliency in mind. State DOTs have begun to leverage transportation asset management (TAM) practices, documented in transportation asset management plans (TAMPs), to address resilience. This paper provides a synthesis of existing practices, as documented within the most recent iteration of state TAMPs, used to address extreme weather, climate change, and resilience. Forty-five Bipartisan Infrastructure Law, Federal Highway Administration–compliant TAMPs were reviewed. The most common resilience activities from the review were categorised into six key categories identified in the National Cooperative Highway Research Programme (NCHRP) Research Report 1052: leadership and agency structure, capacity and competency, collaboration and communication, resource requirements, risk and resilience (RnR) assessment, and business processes. This research also identified gaps in resilience planning by comparing practices described in state TAMPs with best practices identified in NCHRP Research Report 1052. The review uncovered three key areas that would benefit from additional research and development: (1) conduct scenario planning to advance transportation system resilience, (2) develop/adopt RnR performance measures, and (3) develop/adopt definitions for RnR.

The Integrated Waste Management program under the Office of Nuclear Energy is planning for the future transportation, storage, and eventual disposal of spent nuclear fuel and high-level radioactive waste from nuclear power plant and waste custodian sites across the United States. To better enable informed decision-making regarding the back end of the nuclear fuel cycle, the IWM program has been sponsoring the development and application of system analysis tools capable of analyzing various options for managing SNF and HLW. With these tools, integrated waste management system architecture analyses are being conducted to support the future deployment of a comprehensive nuclear waste management system that considers all major back-end aspects of the nuclear fuel cycle i.e., transportation, storage, and disposal. System analyses and assessments typically use these modeling and simulation tools to investigate implications of changes in various assumptions and parameters such as acceptance rates, receipt logic, facility capacities and capabilities, use of standardized canisters, start and stop dates of facilities, etc.

START, the Stakeholder Tool for Assessing Radioactive Transportation is a web-based, decision-support tool developed by the U.S. Department of Energy (DOE) to support the Office of Integrated Waste Management (IWM). Its purpose is to provide visualization and analysis of geospatial data relevant to planning and operating large-scale spent nuclear fuel (SNF) and high-level radioactive waste transport to storage and/or disposal facilities. At present, the primary transport method for these shipments is expected to be via rail, operating predominantly on mainline track. For many shipment sites, however, access to this network will typically require initial use of a local/regional (short line) railroad or involve intermodal transport where the access leg is a movement performed by heavy-haul truck and/or barge. START has the ability to represent and analyze all of these transport options, with each transportation network segment containing site-specific physical and operational attributes. Of particular note are segment-specific accident rates and travel speeds, derived from recent data provided by the U.S. Department of Transportation, Bureau of Transportation Statistics, and other publicly available resources. DOE anticipates that START users will include federal, State, Tribal and local government officials; nuclear utilities; transportation carriers; support contractors; citizen scientists; and other stakeholders. For this reason, START is designed to enable the user to represent a wide range of operating scenarios and performance objectives, with an emphasis on providing flexibility. In doing so, the tool makes extensive use of geographic information systems (GIS) technology for performing spatial analysis and map creation.

Walkability is an essential aspect of urban transportation systems.Properly designed walking paths can enhance transportation safety, encourage pedestrian activity, and improve community quality of life.This, in turn, can help achieve sustainable development goals in urban areas.This pilot study uses wearable technology data to present a new method for measuring pedestrian stress in urban environments and the results were presented as an interactive geographic information system map to support risk-informed decision-making.The approach involves analyzing data from wearable devices using heart rate variability (RMSSD and slope analysis) to identify high-stress locations.This data-driven approach can help urban planners and safety experts identify and address pedestrian stressors, ultimately creating safer, more walkable cities.The study addresses a significant challenge in pedestrian safety by providing insights into factors and locations that trigger stress in pedestrians.During the pilot study, high-stress pedestrian experiences were identified due to issues like pedestrian-scooter interaction on pedestrian paths, pedestrian behavior around high foot traffic areas, and poor visibility at pedestrian crossings due to inadequate lighting.

The study of "managed retreat" as a response to climate change impacts has emerged in recent years as an important field of inquiry that is in need of substantial further research and improved understanding—particularly in the United States (Dundon and Abkowitz, 2021; Plastrik and Cleveland, 2019; Hino, et al., 2017; Dachary-Bernard, et al., 2019). However, the critical participant stakeholders in managed retreat discussions—often city/county planning officials and community members in vulnerable areas—often do not have adequate access to meaningful planning information or other tools to support decision making. With some notable exceptions, important research in this field that could assist communities is often relegated to academic journals that are not often visited by city or county planners faced with the very decisions this research could inform. Although the realities of research careers often require a focus on publication in prestigious academic journals, more attention is now needed to get actionable research knowledge in the hands of the practitioner. The need to develop user-friendly tools that direct research information into the hands of stakeholders most likely to benefit from such work is even more urgent than ever: climate change presents current, continuing, and substantial challenges for humanity in nearly every sector of the economy. Many extreme weather events have already increased (in frequency and/or severity) because of our changing climate, including flooding, heavy precipitation, extreme heat, and other climate-induced events (U.S. EPA, n.d.).

The U.S. Department of Energy (DOE) Office of Integrated Waste Management is planning for the eventual transportation, storage, and disposal of spent nuclear fuel (SNF) and high-level radioactive waste (HLW) from nuclear power plant and DOE sites. The Stakeholder Tool for Assessing Radioactive Transportation (START) is a web-based, geospatial decision-support tool developed for evaluating routing options and other aspects of transporting SNF and HLW, covering rail, truck, barge, and intermodal infrastructure and operations in the continental United States. The verification and validation (V V) process is intended to independently assess START to provide confidence in the ability of START to accurately provide intended results. The V V process checks the START tool using a variety of methods, ranging from independent hand calculations to comparison of START performance and results to those of other codes. The V V activity was conducted independently from the START development team with opportunities to provide feedback and collaborate throughout the process. The V V analyzed attributes of transportation routes produced by START, including route distance and both population and population density captured within buffer zones around routes. Population in the buffer zone, population density in the buffer zone, and route distance were all identified as crucial outputs of the START code and were subject to V V tasks. Some of the improvements identified through the V V process were standardizing the underlying population data in START, changing the projection of the population raster data, and changes to the methodology used for population density to improve its applicability for expected users. This collaboration also led to suggested improvements to some of the underlying shape file segments within START.

Planning for community resilience through public infrastructure projects often engenders problems associated with social dilemmas, but little work has been done to understand how individuals respond when presented with opportunities to invest in such developments. Using statistical learning techniques trained on the results of a web-based common pool resource game, we analyze participants' decisions to invest in hypothetical public infrastructure projects that bolster their community's resilience to disasters. Given participants' dispositions and in-game circumstances, Bayesian additive regression tree (BART) models are able to accurately predict deviations from players' decisions that would reasonably lead to Pareto-efficient outcomes for their communities. Participants tend to overcontribute relative to these Pareto-efficient strategies, indicating general risk aversion that is analogous to individuals purchasing disaster insurance even though it exceeds expected actuarial costs. However, higher trait Openness scores reflect an individual's tendency to follow a risk-neutral strategy, and fewer available resources predict lower perceived utilities derived from the infrastructure developments. In addition, several input variables have nonlinear effects on decisions, suggesting that it may be warranted to use more sophisticated statistical learning methods to reexamine results from previous studies that assume linear relationships between individuals' dispositions and responses in applications of game theory or decision theory.

Global climate change presents both acute and long-term risks to humanity. Managed retreat has emerged in the literature as one method by which to manage some acute and slow-onset events caused by climate change, but it requires substantial additional research and examination. It is now clear that humanity must scrutinize how and where we live and the wisdom of policies that support continued rebuilding and reinvestment after climate-related disasters. Despite its emergence as a potential policy response to risk, the phrase “managed retreat” is documented as a barrier in itself to successful adaptation actions, largely because the term is currently almost exclusively considered to mean physical movement of infrastructure or people out of harm’s way—that is, retreat. There is a need to document and consider case studies where managed retreat is being utilized more broadly and to consider these case studies as a climate governance approach to managing risk. The case studies presented of local policy responses to climate-induced disaster events demonstrate examples of the permanent changes that are already occurring to the existing and historical governance of climate-related risks. These case studies can serve to broaden the climate adaptation discussion and framework beyond “managed retreat” and may lead to more successful implementation of adaptation measures that reduce climate risks. We adopt the term “transformative adaptation measures,” rather than “managed retreat,” and provide case study illustrations of climate governance strategies that communities faced with a changing climate risk profile may consider, rather than focusing on “retreat.”

The U.S. inland waterways play a vital role in the domestic economy, but extreme weather events, especially floods, perennially threaten to disrupt their operations. Here, we develop a data-driven approach to analyzing economic risks due to flood closures along the inland waterways that combines agent-based, economic interdependence, and Bayesian modeling. We demonstrate our framework by evaluating economic impacts of various flood disruptions along the Upper Mississippi River and determining cases where a publicly operated, flood-resilient port located near the mouth of the river can reroute shipments and mitigate production losses for the region. We find that Illinois, Louisiana, Minnesota, and Missouri are the states that suffer the most production losses from flood disruptions and that agriculture and chemical manufacturing are the most impacted industries. However, during floods whose return periods exceed 30-years, the flood resilient port becomes cost-effective in mitigating losses for the region. Our methodology can be easily extended to other hazards and sections of the inland waterways.

INTRODUCTION:Bicycling plays an important role as a major non-motorized travel mode in many urban areas. While increasingly serving as a key part of an integrated transportation demand management system and a sustainable mobility option, interest in biking as an active transportation mode has been unfortunately accompanied by an increase in the number of bike crashes, many with incapacitating injuries or fatal outcomes. Thus, to improve bicycling safety it is crucial to understand the critical factors that influence severe bicyclist crash outcomes, and to identify and prioritize policies and actions to mitigate these risks.METHOD:The study reported herein was conducted with this objective in mind. Our approach involves the use of classification models (logistic regression, decision tree and random forest), as well as techniques for treating unbalanced data by under sampling, oversampling, and weighted cost sensitivity (CS) learning, applied to bike crash data from the State of Tennessee's two largest urban areas, Nashville and Memphis.RESULTS:The results indicate that random forest with weighted CS offers the potential for greater explanatory accuracy, an important observation given the paucity of efforts to date in applying random forest to bike safety studies. Inadequate lighting conditions, crashes on roadways, speed limits, average annual daily traffic, number of lanes, and weekends are the critical features identified.CONCLUSION:Based on these results, a series of specific, suggested policy changes are presented for implementation consideration.PRACTICAL APPLICATIONS:There is existing guidance in FHWA Lighting Handbook and TDOT's Roadway Design Guidelines that spell out some engineering design solutions like lighting provisions, bicycle facility design, and traffic calming measures. These measures may alleviate the identified key features impacting fatal and incapacitating bicycle injuries. Further research should be conducted to gauge the efficacy of the solutions suggested.

History has shown that occurrences of extreme weather are becoming more frequent and with greater impact, regardless of one’s geographical location. In a risk analysis setting, what will happen, how likely it is to happen, and what are the consequences, are motivating questions searching for answers. To help address these considerations, this study introduced and applied a hybrid simulation model developed for the purpose of improving understanding of the costs of extreme weather events in the form of loss and damage, based on empirical data in the contiguous United States. Model results are encouraging, showing on average a mean cost estimate within 5% of the historical cost. This creates opportunities to improve the accuracy in estimating the expected costs of such events for a specific event type and geographic location. In turn, by having a more credible price point in determining the cost-effectiveness of various infrastructure adaptation strategies, it can help in making the business case for resilience investment.

Human responses to climate change are continuing to evolve. At one time, mitigation (reduction) of human emissions of greenhouse gases appeared to offer the best response to prevent the worst impacts of a changing climate. It soon became clear, however, that the world would not be able to reduce emissions quickly enough or to a level sufficient to prevent, in the words of the United Nations Framework Convention on Climate Change, “dangerous anthropogenic interference with the climate system”. Climate change is already altering the frequency and severity of extreme weather events worldwide, and these trends are expected to increase in the foreseeable future. Accordingly, it is well recognized that adapting in place to the changing climate is necessary. Yet, that may not be enough. An additional step in responding to climate risks is emerging, one that requires fundamentally and permanently changing the human interactions with nature in parts of the world. This strategy is often referred to as “managed retreat,” but that term has become controversial, and other terms are needed that express inclusion of the positive societal benefits that can emerge from proactive action. This paper provides a review of the emerging themes within the literature of managed retreat as a climate risk management approach, uses examples from the transportation and infrastructure sector, collects and identifies important nomenclature and definitions, key decision-making considerations, and research gaps that warrant immediate attention. The results of this review are intended to be useful to academic climate change adaptation researchers and infrastructure practitioners alike.

Social distancing has become a pressing and challenging issue during the Covid-19 pandemic. In a smart cities context, it becomes possible to measure inter-personal distance using networked cameras and computer vision analysis. We deploy a computer vision pipeline based on Retinanet that identifies pedestrians in streaming video frames, then converts their positions to GPS coordinates for distance calculation and further analysis. This processing is applied to nine camera streams at three locations from around Vanderbilt University. We collect 70 hours of baseline distancing data over the course of two weeks, after which time we deploy small behavioral interventions at the three locations aimed at increasing distancing compliance. Another 70 hours of data with the interventions in place will be analyzed against the baseline data to determine if they had an effect on distancing compliance.

Numerous hazardous materials (hazmat) shipments travel daily by rail in the U.S. While the industry has an impressive safety and security track record, incidents continue to occur, posing risks to hazmat responders, inspectors, carriers, shippers and the community at large. One promising area to minimize the risks of rail hazmat transport is through deployment of smart systems to improve the accuracy and timeliness of communication among stakeholders. Providing the rail shipper with these capabilities is key to influencing the entire supply chain. The shipper loads the product, knows its material properties, often owns the fleet equipment, and never completely relinquishes its custodial role until the product is successfully delivered to the customer. This paper describes the results of a study to conceptualize and demonstrate how the integration of a number of technologies can be leveraged by rail hazmat shippers to achieve enhanced safety and security. It includes a discussion of system components, how information is transmitted to the rail hazmat shipper to form an integrated database, and messaging of alerts to appropriate hazmat transportation stakeholders. A case study is included to illustrate how system output is being used by a rail hazmat shipper in making improved risk-informed decisions.

Communities everywhere are being subjected to a variety of natural hazard events that can result in significant disruption to critical functions. As a result, community resilience assessment in these locations is gaining popularity as a means to help better prepare for, respond to, and recover from potentially disruptive events. The objective of this study was to identify key vulnerabilities relevant to addressing rural community resilience through conducting an initial flood impact analysis, with a specific focus on emergency response and transportation network accessibility. It included a use case involving the flooding of a rural community along the US inland waterway system. Special consideration was given to impacts experienced by at-risk populations (e.g., low economic status, youth, and elderly), given their unique vulnerabilities. An important backdrop to this work is recognition that Federal Emergency Management Agency’s Hazus, a free, publicly available tool, is commonly recommended by the agency for counties, particularly those with limited resources (i.e., rural areas), to use in developing their hazard mitigation plans. The case study results, however, demonstrate that Hazus, as currently utilized, has some serious deficiencies in that it: (1) likely underestimates the flood extent boundaries for study regions in a Level 1 analysis (which solely relies upon filling digital elevation models with precipitation), (2) may be incorrectly predicting the number and location of damaged buildings due to its reliance on out-of-date census data and the assumption that buildings are evenly distributed within a census block, and (3) is incomplete in its reporting of the accessibility of socially vulnerable populations and response capabilities of essential facilities. Therefore, if counties base their flood emergency response plans solely on Hazus results, they are likely to be underprepared for future flood events of significant magnitude. An approach in which Hazus results can be augmented with additional data and analyses is proposed to provide a more risk-informed assessment of community-level flood resilience.