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    <title>Research in Progress (RIP)</title>
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    <atom:link href="https://rip.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0bG9naWMiIHZhbHVlPSJvciIgLz48cGFyYW0gbmFtZT0idGVybXNsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJsb2NhdGlvbiIgdmFsdWU9IjE2IiAvPjwvcGFyYW1zPjxmaWx0ZXJzPjxmaWx0ZXIgZmllbGQ9ImluZGV4dGVybXMiIHZhbHVlPSImcXVvdDtVbmNlcnRhaW50eSZxdW90OyIgb3JpZ2luYWxfdmFsdWU9IiZxdW90O1VuY2VydGFpbnR5JnF1b3Q7IiAvPjwvZmlsdGVycz48cmFuZ2VzIC8+PHNvcnRzPjxzb3J0IGZpZWxkPSJwdWJsaXNoZWQiIG9yZGVyPSJkZXNjIiAvPjwvc29ydHM+PHBlcnNpc3RzPjxwZXJzaXN0IG5hbWU9InJhbmdldHlwZSIgdmFsdWU9InB1Ymxpc2hlZGRhdGUiIC8+PC9wZXJzaXN0cz48L3NlYXJjaD4=" rel="self" type="application/rss+xml" />
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Bridging the Gap Between Theory and Implementation for CAV-Based Mixed Traffic
Smoothing

</title>
      <link>https://rip.trb.org/View/2625306</link>
      <description><![CDATA[In the last decade, dozens of algorithms have been published that rely on using individual automated vehicles (AVs) to smooth traffic flow, reducing congestion and emissions. However, none of these algorithms have been implemented on production vehicles, begging the question, “Why?” One possibility is that much of the AV-based traffic flow smoothing theory is based on simplistic traffic models that do not hold up to reality, while another possibility is that vehicle-level delays make these strategies impractical to implement.

This project focuses on developing and validating advanced traffic-smoothing controllers for connected and automated vehicles (CAVs) to address critical safety and mobility challenges in mixed traffic environments. The research will address uncertainties in vehicle dynamics, actuation delays, and human compliance, leveraging high-resolution traffic data, cutting-edge simulation tools, and real-world testing on a state-of-the-art CAV platform at a professional test track in Indiana.]]></description>
      <pubDate>Thu, 13 Nov 2025 14:56:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625306</guid>
    </item>
    <item>
      <title>A Reinforcement Learning Framework for Dynamic Inland Waterway Maintenance Under Stochastic Shoaling and Annual Budget Allocation</title>
      <link>https://rip.trb.org/View/2620600</link>
      <description><![CDATA[This research proposes a dynamic, data-driven framework for long-term inland waterway maintenance planning that integrates reinforcement learning (RL), and stochastic modeling. Unlike traditional models that assume deterministic sedimentation, known multi-year budgets, and static decision horizons, the research team models shoaling as a stochastic process, budgets as annually realized random variables, and infrastructure deterioration as a gradual, condition-dependent process. The core of the methodology is an infinite-horizon sequential decision model that makes year-by-year dredging and lock maintenance decisions using RL. Dredging is modeled as a continuous decision variable, and policy learning is guided by a custom-designed simulation environment that reflects realistic physical and institutional constraints. The team trains RL agents using Proximal Policy Optimization (PPO). This work addresses the curse of dimensionality that limits conventional optimization techniques by learning generalizable policies rather than enumerating all possible scenarios. By finding the solution across various uncertainty regimes, the team provides both methodological insights and practical guidance for agencies such as the U.S. Army Corps of Engineers. The resulting framework offers a robust and adaptive tool for managing long-term infrastructure investment under uncertainty]]></description>
      <pubDate>Mon, 10 Nov 2025 09:33:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2620600</guid>
    </item>
    <item>
      <title>Design and Analysis of Bridge Foundations for Redundancy

</title>
      <link>https://rip.trb.org/View/2558400</link>
      <description><![CDATA[The American Association of State Highway and Transportation Officials (AASHTO) approach for designing highway bridges and structures addresses uncertainty in load and resistance and quantifies the variability in design parameters. However, from a geotechnical perspective, foundation design has typically involved calibrating design methods to a target reliability index (β) correlated to the probability of failure, which was considered acceptable in past practice. For example, the reliability index for deep foundation design has been calibrated for probabilities of failure of 1 in 100 (β = 2.3) for driven piles and 1 in 1,000 (β = 3.0) for drilled shafts. This difference is believed to be attributed to the variation in reliability between individual foundation elements and pile groups, with the latter being considered highly redundant systems.

The current AASHTO Load and Resistance Factor Design (LRFD) Bridge Design Specifications (BDS) are ambiguous regarding the definition of redundancy and its application to foundations. Research is needed to investigate redundancy as it applies to geotechnical design and to enhance existing design and analysis requirements.

The objective of this research is to develop design and analysis requirements for bridge foundation elements and groups. These requirements shall account for redundancy based on a probabilistic consideration of resistance for foundations.]]></description>
      <pubDate>Wed, 28 May 2025 10:00:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2558400</guid>
    </item>
    <item>
      <title>Safety Risk Management Model for Pilot Medical Hazard Non-Disclosure</title>
      <link>https://rip.trb.org/View/2518965</link>
      <description><![CDATA[The Federal Aviation Administration's (FAA’s) Office of Aerospace Medicine requires a quantitative risk model for use in a Safety Risk Management (SRM) Panel to assess the risk of undisclosed pilot medical conditions. The AAM Safety Council identified the need for just in time research to develop a quantitative, probabilistic risk assessment model that will yield likelihood and severity estimates that can be assessed in terms of FAA risk thresholds as defined in FAA Order 8040.4C. This risk model needs to address uncertainty about hazard (medical condition) prevalence in the pilot population, rates of pilot non-disclosure and subsequent healthcare avoidance, and the mitigating effect of preventive (medical standards, preflight self-assessments, and medical treatment) and recovery (dual pilot operations and auto recovery systems) controls on the occurrence of pilot total or partial incapacitation and/or its propagation to loss of aircraft control resulting in an accident. It is anticipated that this type of model will also be needed to address the SRM recommendation made by the Mental Health and Aviation Medical Clearances Aviation Rulemaking Committee (ARC) in April 2024.]]></description>
      <pubDate>Tue, 04 Mar 2025 13:50:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2518965</guid>
    </item>
    <item>
      <title>Transportation Infrastructure Development Under Uncertainties</title>
      <link>https://rip.trb.org/View/2447061</link>
      <description><![CDATA[This project proposes to improve a framework for analyzing and optimizing interrelated transportation infrastructure projects under conditions of uncertainty. The study will enhance methods for evaluating and scheduling infrastructure investments, considering interdependencies and demand changes. By developing adaptable, long-term planning tools, the project aims to support agencies in making informed decisions that account for cost and resilience across transportation systems.
]]></description>
      <pubDate>Wed, 30 Oct 2024 15:09:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2447061</guid>
    </item>
    <item>
      <title>Effective Use of Traffic Speed Deflectometer for Network-based and Project-based Applications</title>
      <link>https://rip.trb.org/View/2414322</link>
      <description><![CDATA[For informed and more cost-effective maintenance and rehabilitation (M&R) needs assessments, the structural and surface conditions should be incorporated into the pavement management decision-making processes. The desire to characterize the network-level structural conditions in recent years has led to research efforts to investigate, validate, and demonstrate the effectiveness of Traffic Speed Deflectometer Devices (TSDDs). Several algorithms exist to provide the network-level and project-level information. However, none of them have considered the uncertainty of the field data in terms of the limitations of the sensors. For example, there is a certain minimum deflection velocity below which the results are unreliable. If the sensor is placed at a distance where the measured deflection velocities are less than that threshold, their magnitude is of little value in the analysis. On the other hand, if the precision of the measurement is extremely high, it would be hard to assign a representative value. Recent studies have shown that these types of uncertainty can be observed in several cases depending on the type and stiffness of the pavement. With the desire to automate the analysis, the reasonableness of the assumptions made in the analysis based on the uncertainties in the measurement should be considered to verify the veracity of the outcome. For example, one should understand when the measurement uncertainties of the deflection velocities with the farther sensors can influence the conversion of deflection velocities to deflections. As such, this study aims to identify and propose robust indices for network and project level applications and best-suited procedures for implementing them based on the type of pavement and the characteristics of the hardware of the device. The goals of this project are to provide guidelines and define processes to maximize the information and minimize the cost of network- and project-level uses of TSDDs. The first outcome is a guideline to help the National Road
Research Alliance (NRRA) partners select the best types of pavements that can be analyzed with confidence given the limitations of TSD. The second outcome of the project is a recommendation of the best data analysis procedures from those that have been proposed by several organizations. These algorithms will be selected in cooperation with TAP as part of Task 2. The outcomes of this study will be of particular value to SHAs to maximize their benefit-cost-ratio of using TSDDs by avoiding data collection on sections that are outside the useful range of operation of TSD (as discussed above) and using the best algorithm to analyze the data collected that balances the uncertainties in the measurements with the rigor of analysis. ]]></description>
      <pubDate>Fri, 09 Aug 2024 14:23:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2414322</guid>
    </item>
    <item>
      <title>Developing A Cost-Effective, Reliable, and Sustainable Precast Supply System under Price Volatility and Uncertainty of Material Supply </title>
      <link>https://rip.trb.org/View/2314005</link>
      <description><![CDATA[The supply channel of the precast process begins with the procurement of the raw materials that are processed through the PC (precast concrete) manufacturing operations and subsequently transporting the final products to the point of delivery for assembly or installation on site. The whole system forms a sequential and/or parallel or mesh network of activities to each of which there are three main qualifiers: cost, time, and reliability which dictate the production cost, product durability and reliability which contribute to the final reliability and sustainability of the PC supply system. In this research, a methodology for a cost-effective, reliable, and sustainable precast supply system is to be developed when price volatility and uncertainty of materials exist. As the price of materials and the cost of PC production fluctuate with an uncertainty of materials, an expected cost of supply system can be estimated and an optimal cost-effective supply/process plan with multiple alternatives can be prescribed with an enhanced or desired reliability and sustainability of the system. ]]></description>
      <pubDate>Sun, 24 Dec 2023 08:20:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2314005</guid>
    </item>
    <item>
      <title>Chance-Constrained Collision Avoidance Based Motion Planning in a Cooperative Perception Framework</title>
      <link>https://rip.trb.org/View/2301344</link>
      <description><![CDATA[Perceiving the environment in complex driving scenarios is critical for the safety of autonomous vehicles. Recent advancements in multiagent perception and vehicle-to-vehicle (V2V) technologies have enabled Autonomous Vehicles (AVs) not only to exchange basic safety messages but also to share their perception output with other vehicles. The concept of cooperative perception primarily addresses the challenge of dealing with occluded objects. Currently, a significant portion of research within the cooperative perception domain is dedicated to improving V2V communication systems and perception modules. However, there is a noticeable oversight in incorporating these technologies into like motion planning and vehicular control modules. A recent paper on motion planning for AVs in the presence of occlusions discusses an Optimal Control Problem (OCP) formulation using infrastructure sensor information, in which external data, coupled with the AV's individual perception, is used to determine perception reliability. This is subsequently used to estimate collision risk, ensuring that the estimated collision risk remains within the bounds of an acceptable maximum residual risk. However, the paper relies on numerous assumptions and heuristics to define parameters for the risk model and behavior options, respectively. Furthermore, delves into motion planning based on cooperative perception. However, it fails to consider the uncertainties linked to the perception outputs of neighboring AVs. There are two types of uncertainties in perception: aleatoric and epistemic uncertainty. Aleatoric uncertainty (data) is related to the characteristics of the sensors and effect of environmental conditions on their functionality. Epistemic uncertainty (model) is associated with the training of Deep Neural Networks and their ability to generalize to out-of-distribution (OOD) data. In a typical AV pipeline, the latter planning and control modules operate on processed environmental perception data. Thus, uncertainty in the outputs of implemented DNNs subsequently impact the decision-making process of AVs. The project proposal aims to address the above issues by constructing an OCP with probabilistic constraints [5,6]. Interaction-Aware motion prediction models capture the dependence between the predicted target vehicle trajectory and surrounding vehicles. Utilizing this in conjunction with perception uncertainties in OCP constraints while balancing risk and progress allows for non-conservative planning. The research team will explore convex approximations for these potentially non-convex constraints. The team may also consider investigating motion planning using reinforcement learning in case of inaccurate vehicle dynamics model. The team will also investigate False Data Injection in the framework, where an attacker may introduce fabricated data into the Ego's object detection algorithm, potentially causing collisions. The goal is to develop resilient strategies against these attacks. The algorithm's performance will be evaluated in two uncontrolled intersection scenarios: one involving a left turn collision scenario where a vehicle on the right side of the Ego is occluded, and the other focusing on planning around an occluded static obstacle. The team will mainly focus on V2V technology, with vehicles directly communicating the dynamic data required, but the team may also consider V2I and infrastructure aided cooperative perception if time permits. The team will evaluate the cooperative perception framework on OPV2V and V2V4Real datasets and in simulation environments.]]></description>
      <pubDate>Mon, 04 Dec 2023 17:10:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2301344</guid>
    </item>
    <item>
      <title>Multi-Horizon Urban EV Charging Infrastructure Planning: Integrating Activity Patterns, Grid Dynamics, and Uncertainty
</title>
      <link>https://rip.trb.org/View/2283485</link>
      <description><![CDATA[While huge resources have been set aside for the deployment of Electric Vehicle (EV) charging infrastructure, optimally deploying EV charging stations to encourage the decarbonization of the transportation system is a non-trivial task. Indeed, EV charging interacts with several dimensions that operate on different temporal and spatial scales: activity patterns, electric grid operation, land use—to name a few. In this project, the research team aims to develop a multi-horizon planning model that can assist policymakers in determining the timing and location of EV charging stations in an urban environment. The model incorporates several dimensions of decision-making, operation, and planning relevant to EV charging. First, at the lower level, the team incorporates activity scheduling and its interaction with charger and parking location, availability, and price. Additionally, the team accounts for the uncertainty in EV adoption, as it directly impacts the usefulness and availability of charging. Second, the model incorporates the power grid and its operational constraints, paying especially attention to its stability and dynamics. Third, at the upper level, the team considers a multi-horizon planning problem whose aim is to optimally deploy EV charging stations both in space and time. The team pays special attention to the fact that the future is uncertain and, hence, deploying stations as fast as possible might not always be optimal. The model and insights will prove valuable to several stakeholders: policymakers; federal, state, and city transportation and planning agencies; and power grid operators and regulators.]]></description>
      <pubDate>Mon, 30 Oct 2023 22:53:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2283485</guid>
    </item>
    <item>
      <title>Uncertainties in liquefaction assessment and its economic impact</title>
      <link>https://rip.trb.org/View/2096588</link>
      <description><![CDATA[Liquefaction is one of the areas in bridge design where uncertainties significantly drives up the cost. This task studies the uncertainties and recommends key areas to improve.]]></description>
      <pubDate>Fri, 13 Jan 2023 14:49:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2096588</guid>
    </item>
    <item>
      <title>Automated Lane Change and Robust Safety</title>
      <link>https://rip.trb.org/View/1942835</link>
      <description><![CDATA[Building on the researchers' prior work on lane keeping and lane changing, this collaborative research project aims to take a significant step forward towards developing innovative solutions for autonomous lane change maneuvers. This project aims to achieve three major objectives: (1) integrating reinforcement learning and (control) barrier functions to address safety-oriented constraints; (2) developing robustness analysis and robust redesign for connected and autonomous vehicles in the presence of uncertainties and time delay; (3) validating the proposed lane changing control algorithms with real-world trajectory data and SUMO testing under different environments in the presence of different vehicle mixes and driver uncertainties.]]></description>
      <pubDate>Fri, 22 Apr 2022 16:13:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/1942835</guid>
    </item>
    <item>
      <title>Quantifying the Impact of Data Unavailability, Inaccuracies and Uncertainty on Deterioration Modeling and Infrastructure Asset Management Policies</title>
      <link>https://rip.trb.org/View/1903010</link>
      <description><![CDATA[Accurate prediction of infrastructure component condition and performance is essential to support optimal planning of life-cycle maintenance and inspection actions. Although relevant available data are often abundant and describe several features and characteristics, they are not always accurate and/or complete. Data can often be completely missing from databases, for example due to a regular inspection not being properly recorded or, more importantly, maintenance activities not being documented, and various monitoring devices’ sensitivities and ratings given by different inspectors can lead to significant data variability and uncertainty.  To this end, three important goals of this project are (i) to offer solutions for data-based detection of missing maintenance and repair database entries, (ii) quantification of the impacts of uncertain and incomplete data on deterioration modeling, and  (iii)  assessment of all of the above in the context of life-cycle decision-making. Advanced statistical analysis and machine learning methodologies will be utilized to identify trends in the data and detect potential outliers, such as condition ratings that last statistically longer than expected or statistically prolonged intervals between inspections. Frameworks already developed by the PIs can be leveraged and extended to address these concerns. As examples: Markov models (with or without latent states) can support structural deterioration predictions based on noisy and incomplete data, under further development; survival analysis models can parametrize and map noisy condition data to important metrics of interest (e.g.  remaining service life); and Partially Observable Markov Decision Processes (POMDPs) powered by AI concepts of Deep Reinforcement Learning (DRL) can be extended to integrate the above with decision-making optimization powered by AI concepts of Deep Reinforcement Learning (DRL) can be extended to integrate the above with decision-making optimization]]></description>
      <pubDate>Tue, 11 Jan 2022 16:46:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/1903010</guid>
    </item>
    <item>
      <title>Guidance for Agencies to Incorporate Uncertainty into Long-Range Transportation Planning



</title>
      <link>https://rip.trb.org/View/1854173</link>
      <description><![CDATA[For decades, state departments of transportation (DOTs), metropolitan planning organizations (MPOs), and other transportation agencies have developed multimodal long-range transportation and capital investment plans as required by federal law to ensure the plans meet future and forecasted needs. These plans have anticipated trends and considered uncertainty, however, state DOTs and MPOs are facing new and compounding uncertainties that are difficult to consider, forecast, or fully understand how they may impact transportation networks and mobility. These challenges are coupled with additional regulatory requirements, resulting in transportation planning and programming, becoming more complex inside more constrained processes.
 
Long-range transportation planning is fraught with uncertainty. A confluence of trends, including, but not limited to, shifts in demographics, the economy, and workforce; freight and supply chains disruptions; pressure from land use decisions; changes in society, culture, and politics; the impact of, and security concerns with, rapidly changing technologies and emerging modes of mobility; a growing number of wide-ranging risks to transportation networks; and continued uncertainty about future funding for transportation. There is also significant uncertainty regarding the short-term duration and recovery from the pandemic and the long-term impacts of COVID-19 on travel behavior. Additional regulatory requirements of transportation planning include, but are not limited to, performance based-planning and programming, and asset management.
 
State DOTs and MPOs have some research and guidance at their disposal to help understand uncertainty, including how to utilize methods such as scenario planning, robust decision-making, and risk analysis tools to assess the potential impacts of uncertainty. Research is needed to identify points in processes where flexibility exists and how state DOTs and MPOs can better consider uncertainty. Specifically, state DOTs and MPOs need research to focus on statewide and metropolitan long-range transportation plans (LRTP), statewide and metropolitan transportation improvement programs (STIP/TIP), and other related planning documents (e.g., modal or implementation plans), to inform investment decisions that achieve agency goals.
 
The objectives of this research are to: (1) identify how, when, where, and why uncertainty should be considered in state DOT and MPO planning and programming processes; (2) 
develop frameworks, guidance, and/or toolkit(s) to factor uncertainty into LRTPs, STIP/TIPs, and other related planning documents, and that allow state DOTs and MPOs to monitor and implement their plans; (3) identify strategies and techniques to proactively adapt plans when impacts from uncertainty require agencies to pivot to ultimately achieve their goals; and (4) identify strategies to communicate with stakeholders, including partners, decision-makers, elected officials, and the public-at-large about uncertainty in transportation planning and programming.]]></description>
      <pubDate>Tue, 25 May 2021 15:49:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/1854173</guid>
    </item>
    <item>
      <title>Residual life and reliability assessment of underground RC pipelines under uncertainty</title>
      <link>https://rip.trb.org/View/1751155</link>
      <description><![CDATA[Because of scare financial resources and the abundance of urgently needed pipeline maintenance and repair projects, the prioritization of funding to these projects is a major issue that municipalities encounter everywhere, especially in Region 6. One way to optimize the limited resources allocated to operation and management of sanitary sewers is to consider probabilistic performance assessment, which provides a complete characterization of performance of structural elements and systems. The most widely employed probabilistic performance indicator is reliability, a measure of probability of failure relative to a particular limit state (e.g., ultimate strength or serviceability). Reliability methods can be used to identify which pipeline sections within a particular system require the most urgent inspection or repair. In order to apply the proposed approach to the RCPs in the city of Houston, the research team will work intensively with the Center for Structural Engineering Research/Simulation and Pipeline Inspection at UTA to obtain filtered, LIDAR data. From this filtered LIDAR data, a probability distribution representative of wall thickness loss at the time of inspection will be calculated. Next, this derived probability distribution will be integrated within a serviceability limit state that defines failure as the complete loss of concrete cover. Considering this limit state and a prescribed probability of exceedance threshold, a reliability-based prediction of the remaining service life will be determined. Advanced statistical techniques will also be used to convey the confidence of these predictions. Finally, an asset management report that outlines the location of the most vulnerable pipeline sections, will be created. The asset management report will provide decision makers crucial information regarding the current state of their city’s pipeline network. Although the approach developed can be applied to any municipality’s pipeline network, the capabilities of developed methodology will be elucidated through its application to Houston-area sanitary sewers.]]></description>
      <pubDate>Tue, 10 Nov 2020 20:20:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/1751155</guid>
    </item>
    <item>
      <title>Multi-Level Resilience-Based Transportation Asset Management (TAM) Framework using Bayesian Network</title>
      <link>https://rip.trb.org/View/1743220</link>
      <description><![CDATA[This project is a multi-level resilience-based transportation asset management framework using Bayesian network. The framework is aimed at (a) measuring transportation network resilience at multiple management levels (e.g., project, network and enterprise levels), (b) tracking and quantifying uncertainties existing at every level so as to effectively manage uncertainties in assessing the overall network resilience, (c) determining the optimal combination of inspection/monitoring techniques based on Value of Information, and (d) providing the optimal allocation of budgets to multiple pre- and post-disaster resilience-enhancing strategies.

The project will provide decision-makers (e.g., state DOT risk managers, executives, and program and project managers) with several analytical models, including (a) component-level time-dependent reliability analysis and its updating procedure based on different types of inspection and monitoring techniques; (b) network analysis which can incorporate both the robustness of components and the adaptive capacity of a network; (c) Bayesian-network-based resilience assessment model that evaluates each resilience capacity at multiple transportation management system levels and quantifies uncertainty at every assessment stage; and (d) asset management strategies by disaggregating resilience into the three resilience capacities using backward simulation. The framework itself and such analytical models can be implemented in risk-/resilience-based transportation asset management. Moreover, this project can be extended to develop a Python interactive tool, which is designed to enable transportation agencies to understand how the research findings can be easily and successfully implemented in improving the resilience of their transportation system.]]></description>
      <pubDate>Tue, 06 Oct 2020 13:56:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/1743220</guid>
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