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    <title>Research in Progress (RIP)</title>
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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>Reliability-Aware Accessibility Measurement and Planning for Rural Transportation Systems</title>
      <link>https://rip.trb.org/View/2703797</link>
      <description><![CDATA[Reliable access to essential destinations is a persistent challenge in rural transportation systems, where long travel distances, limited infrastructure, and exposure to environmental disruptions can significantly affect mobility. Transportation accessibility is widely used in planning to evaluate how well transportation networks connect people to services and opportunities, yet most accessibility measures assume deterministic travel conditions and do not account for travel-time variability, weather disruptions, or infrastructure reliability. As a result, existing accessibility metrics may overestimate the practical ability of rural residents to reach essential destinations and provide limited guidance for transportation planning under uncertain conditions.
This project develops a reliability-aware accessibility measurement and planning framework for rural transportation systems. The research will extend traditional accessibility measures by incorporating transportation network uncertainty through scenario-based modeling of travel-time variability and disruption conditions. Reliability-aware accessibility metrics will be benchmarked against conventional accessibility measures and embedded within an optimization-based planning model that helps identify transportation interventions that improve reliable access under resource constraints. The framework will be demonstrated through a rural transportation case study using publicly available data and implemented as a prototype decision-support workflow for transportation planners.]]></description>
      <pubDate>Sat, 16 May 2026 11:55:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703797</guid>
    </item>
    <item>
      <title>Strategic Investment Choice to Reduce Disruptions and Increase Resiliency of Roadway
Freight Network</title>
      <link>https://rip.trb.org/View/2684218</link>
      <description><![CDATA[The proposed research will develop models and algorithms to identify systematic investment strategies by reducing link disruption failure probabilities and enhancing overall roadway resilience for freight flows. A new stochastic programming modeling framework will be developed in which disruption probabilities depend on resource allocation decision variables and new algorithms will be developed to deal with the computational challenges caused by both the large number of scenarios and the nonlinearity in both first-stage and second-stage sub-problems. The framework, including data integration, models, and solution methods, will be programmed and tested with a case based on the freight network in the State of Tennessee.]]></description>
      <pubDate>Wed, 25 Mar 2026 16:46:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684218</guid>
    </item>
    <item>
      <title>Generating reliable freight disruption measures with freight telematics data</title>
      <link>https://rip.trb.org/View/2684220</link>
      <description><![CDATA[Freight network resilience is critical for economic stability, especially during disasters and infrastructure failures. This study refines disruption measures using Robinsight, COMPASS IOT, and Robinsight telematics data, alongside WAZE crowdsourced data and infrastructure-based instrumentation (TN RDS). Building on prior research, we analyzed freight mobility impacts from events like the Oregon Durkee Fire (2024), Hurricane Helene, and major bridge closures (I-40, I-55, I-84).

Year 3 focuses on validating key disruption indicators, enhancing predictive models, and integrating emerging data sources to assess infrastructure failures and safety risks from freight detours. Aligned with US Department of Transportation priorities, this research provides transportation agencies with actionable insights to improve freight mobility, inform infrastructure investments, and strengthen supply chain resilience. The findings will support data-driven decision-making, ensuring a more adaptive and robust freight transportation system.]]></description>
      <pubDate>Wed, 25 Mar 2026 16:27:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684220</guid>
    </item>
    <item>
      <title>Evaluating isolated areas, alternative routing, and economic impact for resilient transportation in North Carolina</title>
      <link>https://rip.trb.org/View/2604572</link>
      <description><![CDATA[Natural disasters, such as flooding, landslides, storm surge, and wildfire can cause severe impacts to the social, environmental, economic, and transportation systems of North Carolina. At the same time, transportation infrastructure plays a critical role in natural disaster response and recovery efforts during these natural disaster events. Unfortunately, extreme hazard events such as these are occurring with greater frequency and intensity. These events can negatively impact road functionality and lead to the loss of essential services. According to the National Oceanic and Atmospheric Administration (NOAA), weather-related disasters have cost over $1.875 trillion since 1980. The built environment isn’t designed to handle many of the impacts that are happening due to extreme hazard events. For example, stormwater systems, culverts, and tidal pumps were all designed for past events— not current and future conditions. The failure of these systems will impact  communities to a level where they may not be able to return to normal for months or years.

Transportation planners and engineers from North Carolina Department of Transportation (NCDOT), as well as other federal, state, and local agencies across the state, and in close collaboration with emergency managers, are increasingly looking for better ways to address these issues and become more resilient, while simultaneously planning for a more reliable transportation network. Planning for extreme events is about finding ways for systems to bounce back to normal as quickly as possible after the negative impacts of an event. One particular issue that NCDOT faces is the rerouting of traffic during and immediately after natural disaster events. Typical considerations include traffic volumes, current conditions, roadway capacities, and overall safety. However, there are other considerations such as the overall economic impact, including issues like commerce, commute times for individuals traveling between work and home, access to essential services, and disruption to local businesses, that should also be taken into account. These impacts can be further compounded in areas where entire networks of roads, such as a neighborhood or community, become cut-off due and thus isolated. This isolation can be due to such factors as a damaged bridge or road washout. Worse yet, these impacts can often last for days or even months. By identifying these areas ahead of time, and better understanding the potential economic impacts, NCDOT and other agencies can be better equipped when planning for a more resilient and sustainable transportation infrastructure system.

The joint proposal team, consisting of researchers from the University of North Carolina at Asheville’s National Environmental Mapping and Applications Center (NEMAC) and the University of North Carolina at Charlotte, proposes a comprehensive and innovative approach to helping NCDOT better understand the forces behind transportation route and commute pattern disruptions, and their effects on local economies, in the face of an increase in extreme hazard events. Through comprehensive user research and discovery, data analysis, and the development of decision-making workflows, the project team seeks to provide NCDOT with actionable insights to better plan and respond to disruptions related to extreme hazard events, ultimately improving infrastructure reliability and community access.]]></description>
      <pubDate>Tue, 30 Sep 2025 11:13:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2604572</guid>
    </item>
    <item>
      <title>Increasing the Resilience of Transportation Systems under a Combination of
Cybersecurity Attacks and Extreme Events</title>
      <link>https://rip.trb.org/View/2548628</link>
      <description><![CDATA[This project is focused on measuring the resilience of transportation systems with respect to cyberattacks and extreme events
(hurricanes and power outages). This will require developing an Advanced Traffic Management System (ATMS) simulator for a given road network system to simulate
potential cyberattacks and their impact on the traffic. The research team will propose combinatorial optimization algorithms for optimally attacking
the ATMS and measure the impact of such attacks to assess the resilience of the system. The team will also evaluate the impact of
concurrent extreme events on the transportation system, especially hurricanes and power outages. These extreme events are
expected to become more likely in the upcoming years due to climate change and are particularly relevant to the city of Houston, Texas, where the PI’s institution is located. The proposed approaches will be evaluated on publicly available datasets in collaboration
with other members of the center. Main findings will be summarized in at least one research paper and the final project report.
Software, datasets, and metadata produced through the project will be made publicly available.]]></description>
      <pubDate>Tue, 29 Apr 2025 16:56:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2548628</guid>
    </item>
    <item>
      <title>Enhanced Network Models for Multimodal Resiliency</title>
      <link>https://rip.trb.org/View/2519185</link>
      <description><![CDATA[This project will develop next-generation multimodal network resilience models.  The research team will examine the performance of networked transportation systems in disrupted conditions using field data, generate mathematical models to describe system performance and user behavior, and develop mitigation strategies based on this model.  This project is distinguished from previous efforts in this space by explicitly considering multiple travel modes.  The team specifically will consider road transport, freight waterways, and port terminals as the transportation systems in their scope.  The primary disruption the team will consider is a natural disaster, such as a hurricane, which can disrupt both transportation supply (closing roads, damaging port or waterway infrastructure) and demand (evacuation, closed businesses).]]></description>
      <pubDate>Fri, 07 Mar 2025 16:57:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2519185</guid>
    </item>
    <item>
      <title>Link Disruption Scenario Generation for Transportation Network Criticality Analysis</title>
      <link>https://rip.trb.org/View/2509039</link>
      <description><![CDATA[Policy makers need the criticality ranking of transportation network links so that they can act to suggest investment/improvement strategies. The criticality of transportation networks links is calculated through utilizing selected criticality metric(s) for various disruption scenarios. The disruption scenarios generally include link failure scenarios that can correspond to one link removal/degradation at a time (the most common) or simultaneous removal/degradation of multiple links. The common approach is to utilize singular component degradation/failure to calculate the individual criticality, i.e., run traffic assignment for each link removal scenario, calculate the difference of the network performance function (e.g., total system travel time) between the unaffected network and the failure scenario, and rank the links based on this functionality loss difference, i.e., the highest the difference, higher the ranking of the link. However, it is more likely that multiple links fail simultaneously, especially when considered within disaster conditions such as hurricanes and snowstorms. As shown in the literature, single link removals do not reveal the actual criticality of link(s) due to network dependencies, e.g., a link that may not create a large network performance change to be deemed critical, yet can cripple the system when fails in conjunction with others. In other words, the network dependencies make it difficult to isolate each link’s individual criticality. Multiple simultaneous link removal scenarios can capture the network interactions; however, the calculated criticality scores indicate the criticality ranking of scenarios than individual links, e.g., the links in scenario-1 is more critical than the links in scenario-2. In addition, running multiple link failure scenarios can be computationally infeasible due to the combinatorial nature of scenario creation. For example, for a medium size network with 100 links, the number of scenarios to include all simultaneous two-link removals are 4,950 (combinations of 2 links out of the total 100). For triple link removal, the number of scenarios increase to 485,100, and for quadruple removals, the total number of scenarios is 2,352,735. Considering that simultaneous quadruple link removals in a network size of 100 cannot reveal the network flow interdependencies, such multi-link failure analysis lead to impractical computation times.
In this context, there are two main research questions: 1) How to calculate criticality of individual links based on scenarios that include multiple simultaneous failures? and 2) What is the optimal scenario generation approach that reveals the network flow dependencies while it is computationally tractable? For the first problem, PI Yazici has developed an approach that calculates individual link criticality based on a given number of scenarios. The approach utilizes the distribution of the criticality scores for each link, i.e., the criticality score distribution for link #X is composed of the criticality scores of scenarios that include link #X. The criticality ranking for each link is calculated based on its criticality score distribution’s mean, coefficient of variation and skewness. The approach was tested on real-life networks and it was shown that it provides robust link criticality rankings that account for network interactions. Hence, this project focuses on the optimal scenario generation strategy that will provide a systematic approach to select a smaller subset of all link failure scenarios that enables transportation criticality analysis that account for network flow interaction patterns with reasonable computation times. The developed procedure will also account for the network size and topology that affect network flow dependencies, thus the criticality of individual links.
]]></description>
      <pubDate>Wed, 12 Feb 2025 17:56:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2509039</guid>
    </item>
    <item>
      <title>Generating reliable freight disruption measures with freight telematics data (Year 2)</title>
      <link>https://rip.trb.org/View/2422948</link>
      <description><![CDATA[In the aftermath of disasters that challenge the resilience of transportation networks, the urgency for planning for rapid mobility and recovery has been underscored. The primary objective of resilience is to enable transportation agencies to prepare more effectively for such events. In this light, resilience measures serve as a critical tool, providing a means to assess the impact of disruptions and inform strategic investments to mitigate these occurrences. The first year of the study team’s research addressed the critical challenges faced by states and agencies in measuring freight network systems: the scarcity of comprehensive data and the inadequacy of analytical methods. While there is substantial data available on the movement of people and passenger vehicles, understanding freight movements—especially under disruptive scenarios—poses distinct challenges. Freight movements, governed by corporate supply chain decisions, are subject to constant change due to various economic conditions and span multiple jurisdictions and transport modes. Moreover, methods to capture and analyze data that encompasses these complex dynamics have been limited. With a focus on these challenges, the study team’s initial research presented a novel framework that leveraged Robinsight telematics data to bridge this gap. In the first year, the study team has delved into the telematics data to explore its capacity for developing robust freight network resiliency measures, with the trucking sector in Tennessee and the Pacific Northwest.]]></description>
      <pubDate>Thu, 29 Aug 2024 17:29:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2422948</guid>
    </item>
    <item>
      <title>Analysis on Traffic Safety and Mobility for Tribal Communities under Severe Weather Conditions</title>
      <link>https://rip.trb.org/View/2343910</link>
      <description><![CDATA[Under the conditions extreme weather, potential power outage, loss of wireless signal coverage, and difficulty in accessibility may cause malfunction to the traffic signals and remain unaccounted for. The safety and mobility management for tribal communities under severe weather conditions is an under-resourced area. This research will provide scenario comparison based on scenario simulation on different alternatives for the case study region with selection and development of car following models. Accordingly, suggestions will be to tribal area traffic management authorities that can provide a safer and more mobile tribal community during the extreme weather conditions. This research can also serve as pioneering research for further traffic analysis in tribal areas.]]></description>
      <pubDate>Thu, 22 Feb 2024 16:17:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2343910</guid>
    </item>
    <item>
      <title>Mitigating Cascading Failures for Safety in Transportation Networks in the Era of Autonomous Vehicles</title>
      <link>https://rip.trb.org/View/2292657</link>
      <description><![CDATA[Bridge collapses, road closures, disruptions in the public transportation system, and major issues caused by autonomous vehicles (AVs) are everyday realities of our transportation infrastructure that not only cause inconvenience to the public but also constitute a major safety concern. When a particular component of the transportation system fails (e.g., due to an AV blocking a road), the failures and the associated congestion will likely be propagated to other parts of the transportation system, which may lead to further failures, and so on, potentially leading to a cascade of failures and a catastrophe in the whole city. A real-world example of this phenomenon took place on July 21, 2012, when a heavy rain shut down a metro line in Beijing and caused 100 bus routes to detour, skip stops, or cancel operation completely. Similarly, increasing deployment of AVs in the form of robotaxis by companies like Waymo and Cruise have not only led to several accidents but also events where seemingly confused AVs blocked certain roads for several hours. Cities such as Pittsburgh are particularly vulnerable to such cascade of failures and congestion propagation due to harsh weather conditions and existence of many bridges/tunnels creating bottlenecks. Given also the fact that increased congestion levels will likely lead to an increase in traffic incidents, there is a clear need for a better understanding of the impact of these cascading failures on the safety of the transportation system and the role that AVs play, both positive and negative, in them.   This project aims to study the cascading effects of transportation network failures with an eye towards developing mitigation policies that maximize overall public safety. The research team will be particularly interested in accounting for the increased presence of AVs, both to understand their impact on initiating or amplifying these failures, and to reveal how AVs can help mitigate cascading failures. For example, a prior project supported by Mobility 21/Big Ideas fund laid out the initial work demonstrating how AVs can help reduce congestion more effectively by their ability to react in real time to vehicles around them, and their ability to be remotely and centrally controlled by fleet owners. Building on these initial results where the goal was to minimize the overall delay/congestion, this project will seek to reveal the impact of AVs on the safety of the overall transportation system. The plan is to develop a comprehensive model that quantifies the safety impact of different failure events while taking into account the potential cascading effects. For example, a stalled robotaxi blocking an intersection in San Francisco would initially pose a safety threat to vehicles and pedestrians in its vicinity. In addition, depending on how long it blocks the road, this event may cause a congestion which can then cascade to neighboring roads, potentially leading to increased accident rates in the entire city. To the best of the team's knowledge, this project will be developing the first set of metrics for quantifying the safety impacts of these failures with their cascading effects also included. The team would like to add that this project is synergistic with a concurrently submitted proposal entitled “Evaluating Autonomous Vehicles’ Safety Benefits in Mixed Autonomy Scenarios,” where the goal is to evaluate the safety impact of AVs from the perspective of their accident rates with other vehicles and human pedestrians. The current project on other hand focuses on revealing the overall safety impact of AVs including their impact on congestion and cascading road failures. As such, the two projects will nicely complement each other can be combined together at a total budget of $150,000 if so preferred.]]></description>
      <pubDate>Tue, 21 Nov 2023 20:14:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2292657</guid>
    </item>
    <item>
      <title>Improving MDOT’s Movable Bridge Reliability and Operations</title>
      <link>https://rip.trb.org/View/2040362</link>
      <description><![CDATA[Despite effective inspection and maintenance programs, MDOT’s movable bridges occasionally experience unscheduled downtime due to
electrical and mechanical component malfunction. Responding, troubleshooting, and performing repairs can be costly, and more importantly be
disruptive to users of the bridge.
Research is needed to do the following:
(1) identify best practices throughout the nation on movable bridge reliability and maintenance.
(2) determine what performance data to collect and parameters to track to allow workers to predict component malfunction proactively,
and how best to collect and display that information.
(3) identify enhancements or modifications to movable bridge components/hardware to improve reliability.
(4) validate MDOT’s current maintenance strategy and determine opportunities for improvement based on benefit-cost analysis.
(5) determine effective ways to optimize traffic operations during movable bridge downtime. A strategy using Intelligent Transportation Systems (ITS) to improve customer messaging is appealing to allow users to make more informed decisions on when to seek an
alternate route or wait for services to be restored.
Unscheduled downtime of movable bridges has negative mobility impacts, affecting emergency response services, the motoring public as well
as marine traffic. For example, the detour for the Charlevoix bascule bridge is over 60 miles in length and approximately 1.3 hours in additional
travel time. Component malfunction also often results in MDOT personnel responding outside of normal working hours to troubleshoot the
problem and perform repairs, including nights, weekends, and holidays.]]></description>
      <pubDate>Mon, 31 Jul 2023 08:01:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2040362</guid>
    </item>
    <item>
      <title>TRC2303 - Evaluation of Impacts Due to a Bridge Closure: A Case Study of the Mississippi River Bridges in Arkansas</title>
      <link>https://rip.trb.org/View/2215486</link>
      <description><![CDATA[The objective of the proposed research is to quantify the multi-modal impacts due to a Mississippi River bridge closure. This study will consider different scenarios/combinations of bridge closures (i.e., full and partial bridge closures that take account of one/both directions, single/multiple lanes, and day/night closures), including all the Mississippi River bridges in Arkansas (i.e., I-40, I-55, HWY 49 and HWY 82 bridges). A comprehensive multi-modal analysis will be performed that considers the number of vehicles, trucks, and marine vessels/barges disrupted due to the bridge closure (with all potential scenarios/combinations) and applies detailed cost conversions to monetize direct (delays) and indirect (safety, infrastructure, operations) impacts. Also, this study will develop an Excel-based tool to conduct “what-if” analyses for decision-making purposes, whether for operation, maintenance, or planning activities.]]></description>
      <pubDate>Wed, 19 Jul 2023 17:39:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2215486</guid>
    </item>
    <item>
      <title>Access to Food in a Severe Prolonged Disruption: The Case of Grocery and Meal Shopping During the COVID-19 Pandemic</title>
      <link>https://rip.trb.org/View/2087516</link>
      <description><![CDATA[The COVID-19 pandemic has revealed the fault lines in society. Whether it be remote work, remote learning, online shopping, grocery and meal deliveries, or medical care, there are disparities and inequities among socio-economic and demographic groups that leave some segments of society more vulnerable and less adaptable. This project aims to identify vulnerable and less adaptable groups in the context of access to food. Using a comprehensive behavioral survey data set collected during the height of the pandemic in 2020, this project aims to provide insights on the groups that may have experienced food access vulnerability during the disruption when businesses and establishments were restricted, the risk of contagion was high, and accessing online platforms required technology-savviness and the ability to afford delivery charges. The project proposes and presents estimation results for a simultaneous equations model of six endogenous choice variables defined by a combination of two food types (groceries and meals) and three access modalities (in-person, online with in-person pickup, and online with delivery). The model estimation results show that attitudes and perceptions play a significant role in shaping pandemic-era access modalities. The model revealed that, even after controlling for a host of attitudinal indicators, minorities, low-income individuals, and individuals residing in rural low-density areas are particularly vulnerable to being left behind and experiencing challenges in accessing food during a severe and prolonged disruption. Social programs should aim to provide these vulnerable groups with tools and financial resources to leverage online activity engagement and access modalities.]]></description>
      <pubDate>Wed, 21 Dec 2022 12:17:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2087516</guid>
    </item>
    <item>
      <title>Machine Learning for Improving Air Mobility under Emergency Situations</title>
      <link>https://rip.trb.org/View/1937026</link>
      <description><![CDATA[Emergency situations in aviation pose serious risks to life and result in huge negative impacts on air mobility, causing a significant economic and reputation loss to airlines and airports. However, the decisions to deal with emergencies are usually made by flight dispatchers according to their experience, and they merely consider local-view optimization. Therefore, there is an urgent need to design a decision-making assistant system to alleviate the negative impact of perturbations on aviation air mobility in the global-view perspective. In this project, the research team will develop a framework based on machine learning that captures the patterns of emergency situations and optimizes the operation schedules quickly and accurately for maximum air mobility efficiency at both micro-level and macro-level. The team will utilize multi-source data and leverage deep learning models to predict the consequence of emergency events considering the spatial-temporal characteristics of the events. Based on a prediction model, the team will optimize air mobility output by adopting a deep multi-agent reinforcement learning model. The goal is to provide pre-alert and decision-aid system for passengers and airport staff when emergency events occur, and to adjust the original schedule for quick recovery of disrupted air mobility.]]></description>
      <pubDate>Sat, 02 Apr 2022 11:16:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/1937026</guid>
    </item>
    <item>
      <title>Integration of Contingency Planning for Small Airports</title>
      <link>https://rip.trb.org/View/1872915</link>
      <description><![CDATA[Recent events, such as the COVID-19 pandemic, have shown how small airports (general aviation (GA), non-hub, and small-hub commercial service), which have limited resources and expertise, are often ill-prepared to address airport disruptions. Airport disruptions are situations that impact staffing, financial and information technology (IT) resources, infrastructure, facilities, and supply chain shortages.  
Specifically, these airports have challenges with effective coordination and integration of contingency planning (operational and business continuity, emergency response, financial sustainability and resiliency). Research is needed to help small airports benefit by integrating all aspects of contingency planning to sustain operations and build resiliency.    
The objective of this research is to develop guidance to assist small airports to effectively integrate plans for operational and business continuity, emergency response, financial sustainability, and resiliency to respond to airport disruptions. 
Considerations for development of the guidance should include at a minimum:  
(1) Identification of existing and available reports and resources that would assist in development of a reference library;  
(2) A sampling of case studies at a variety of relevant types and sizes of small airports and other industry entities (e.g., rail, highways, ports, utilities) that reflect best practices of integration; 
(3) Identification or development of tools (e.g., flow charts, checklists, decision trees) that will assist in the integration;
(4) Flexibility for integration with respect to event complexity, airport size, and its resources (internal and external); and
(5) A process for conducting a cost benefit analysis of integration. ]]></description>
      <pubDate>Wed, 18 Aug 2021 09:27:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/1872915</guid>
    </item>
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