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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=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" 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>Digital Twin Modeling of NOx Formation from Transient Vehicle Operations in Hilly Terrains</title>
      <link>https://rip.trb.org/View/2652170</link>
      <description><![CDATA[The Paseo del Norte region is recognized for its high ozone (O3) concentrations, which are largely attributed to photochemical reactions between nitrogen oxides (NOx) and volatile organic compounds from transportation sources. This issue is particularly acute in the border city, where heavy traffic flows between the sister cities of El Paso, USA, and Ciudad Juárez, Mexico, significantly impact air quality. Additionally, the Paseo del Norte region is characterized by its hilly terrain, which can cause the transient operation of automobile engines with excessive NOx formation. The proposed project aims to quantify the contribution of transportation NOx from automobile transient operations in hilly terrains through collaborative research between mechanical and civil engineers. First, the NOx flow rate will be measured from on-road testing of diesel vehicles at varying traffic conditions near El Paso, Texas, while transferring the acquired data to the cloud in real time. Second, the collected data will be used to construct the digital twin model incorporating the engine simulation and NOx reaction kinetics. Lastly, the digital twin will be utilized to quantify the contribution of transient NOx at varying traffic scenarios. A thorough understanding of the NOx formation mechanism will enable policymakers to optimize transportation systems and mitigate excessive sources of NOx in urban areas. Additionally, the digital twin facilitates the evaluation of transportation NOx from neighboring regions, including Ciudad Juárez in Mexico, based on minimal traffic information. The interdisciplinary nature of the proposed study will foster future workforces in the transportation sector with unique capabilities. ]]></description>
      <pubDate>Tue, 13 Jan 2026 16:14:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652170</guid>
    </item>
    <item>
      <title>Bridging Data Gaps with Modeled Data from Generative AI: Advancing Health in Transportation Research</title>
      <link>https://rip.trb.org/View/2652171</link>
      <description><![CDATA[Transportation-related factors, such as air quality changes and exposure disparities, have significant impact on health outcome. Communities near high-traffic corridors experience elevated exposure levels, yet efforts to assess these impacts are hindered by the lack of high-resolution health and socio-demographic datasets. Traditional air quality models, such as dispersion and interpolation techniques, estimate pollutant distributions but struggle to capture localized exposure variations and real-world uncertainties due to their reliance on static assumptions. These limitations reduce the precision of transportation health impact assessments. 

This project addresses data gaps in air quality and health outcomes by integrating AI-generated data with  traditional modeling techniques. Bridging the data gap is essential to improving exposure assessments and provide a more comprehensive understanding of transportation-related health effects. The research develops and trains generative AI models for data augmentation, using harmonized datasets to create high-fidelity modeled data that reflects real-world patterns. Furthermore, we integrate the trained AI models with air quality simulation models to estimated transportation-related air quality scenarios and assess potential health impacts.
 
The project produces a validated generative AI model for data augmentation, generating high-resolution datasets that enhance geographic and demographic granularity in transportation health research. The application of scenario-based health impact simulations provides new insights into the relationships between air quality and health outcomes, improving the ability to evaluate transportation-related interventions. By combining AI-driven data synthesis with traditional modeling approaches, this research advances methodologies for transportation and environmental health assessments, providing more reliable data for exposure studies and policy evaluations. 
]]></description>
      <pubDate>Tue, 13 Jan 2026 16:10:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652171</guid>
    </item>
    <item>
      <title>Pedestrian Route Choice and Traffic Exposures: A Case Study in Downtown Atlanta</title>
      <link>https://rip.trb.org/View/2652174</link>
      <description><![CDATA[Walkable neighborhoods and active mobility provide human health benefits through exercise, stress relief,  and local sustainability, but concerns over near-roadway traffic-related air pollution remain significant (Luo, 2018). Exposure to pollutant concentration exposure assessments downwind from roadway sources not only undermines the health advantages of walking, but also poses economic burdens, as indicated by the substantial costs associated with air pollution-related mortality (World Bank, 2020). Recent studies are increasingly focused on understanding how pedestrian route choice influences exposure to traffic related air pollutants (Khreis, 2020), and integrating these exposure metrics into pedestrian route planning may improve health outcomes. Shortest path, impedance-based routing tools, such as SidewalkSim, can be used to predict pedestrian paths through the sidewalk network. Route selection can account for pedestrian asset design and condition, and even route wheelchair users around crossings that are missing a curb ramp (which imposes a significant impedance on the crossing link). When second-by-second pedestrian route data are combined with spatiotemporal predictions of pollutant concentrations downwind from roadways, analysts can assess how exposure accumulates over the course of each walking trip. Because the routing tools are impedance-based, pollutant concentrations can be converted to link impedance and potentially used to route pedestrians on slightly longer routes that result in much lower pollutant exposure. This project will apply impedance methods in the context of pedestrian travel in downtown Atlanta, testing a variety of impedance functions from the assessments to evaluate the tradeoffs between route circuity and pollutant exposure across these functions. The project will develop a framework that supports healthier, lower-exposure pedestrian pathways (while maintaining reasonable routes and convenience for all users). ]]></description>
      <pubDate>Tue, 13 Jan 2026 15:38:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652174</guid>
    </item>
    <item>
      <title>Improving Vehicle Cabin Air Quality in Daily Transportation</title>
      <link>https://rip.trb.org/View/2652182</link>
      <description><![CDATA[Drivers and passengers are exposed to traffic-related air pollutants during their ride. This exposure can be high even for a short ride if the driving route includes highly urbanized areas with a large amount of traffic. Unfortunately, the public is vulnerable to this type of exposure in the absence of proper regulations for vehicle cabin air quality. This project aims to assess the effectiveness of portable purifiers and adsorption type cabin filters and establish a test method. First, the study will conduct a survey on portable air purifiers and to identify widely used products. Second, the project team will evaluate the effectiveness of air purifiers when used inside vehicle cabin compared to the existing vehicle cabin filter for particles. Third, the project team will evaluate the effectiveness of air purifiers and adsorption type cabin filters for a gaseous pollutant—nitrogen dioxide—in vehicle cabin conditions. Fourth, the project team will develop a lab-test set-up to specifically evaluate the effectiveness of adsorption type cabin filters. This project will help mitigate the adverse health effects of traffic-related air pollution by reducing their concentrations in vehicle cabins using advanced filters or portable air purifiers.    ]]></description>
      <pubDate>Tue, 13 Jan 2026 14:25:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652182</guid>
    </item>
    <item>
      <title>Real-Time Corridor Modeling and Health Impact Assessment using Dynamic Vehicle Fleet Composition Data</title>
      <link>https://rip.trb.org/View/2652183</link>
      <description><![CDATA[Urban transportation systems support personal mobility, but are also a significant source of air pollution, with disproportionate impacts on communities near high-traffic corridors.  If electric vehicle (EV) adoption continues to increase, pollutant concentration distributions will change, potentially requiring more detailed assessments of air quality and health impacts.  Traditional air quality impact assessment for transportation projects employs microscale modeling using the MOVES and AERMOD models.  These models rely heavily on fleet composition data (vehicle classes, ages, and fuel types), yet existing assumptions often fail to capture the spatial and temporal variability in vehicle usage.  For example, research in Atlanta has revealed that the on-road freeway fleet during the morning peak tends to be a lot younger (and cleaner) than the average vehicle fleet, likely because commuters take their best vehicles to work.  This research to be conducted in this proposed project will develop an integrated framework that combines real-time traffic simulation, air quality impact assessment, and health impact assessment to assess the effects of different vehicle fleets on air quality and public health.  Using the TransportSim model, MOVES model, and AERMOD dispersion model, this study will analyze vehicle fleet dynamics across multiple urban corridors in the Atlanta metro area for different fleet compositions.  The research results will also identify shifts in pollutant concentration hotspots and their implications in spatial health impact assessment across neighborhoods as EVs enter the fleet.  By improving the accuracy of corridor-level pollutant modeling, this study will support the identification of strategies designed to mitigate air pollution and protect public health.  ]]></description>
      <pubDate>Tue, 13 Jan 2026 14:20:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652183</guid>
    </item>
    <item>
      <title>The Effects of Road Types and Construction Activities on Particulate Matter and Development of Best Practices for PM2.5 Reduction</title>
      <link>https://rip.trb.org/View/2604527</link>
      <description><![CDATA[The research team will evaluate the impact of roads (paved and unpaved roads, and unpaved shoulders) and construction on 2.5 microns (PM2.5) emissions and develop strategies for reduction. Objectives include quantifying PM2.5 emissions from roads and construction sites, identifying high-risk areas using thematic mapping, and designing cost-effective mitigation measures like dust suppression and optimized paving practices. The research team will create a user-friendly decision-support toolkit to help prioritize interventions and assess emission reduction strategies. The project outcomes will provide actionable solutions for air quality planning at both project and regional levels, enabling the Texas Department of Transportation (TxDOT) to meet environmental regulations, improve public health, and reduce PM2.5 impacts. The research team will collaborate with the TxDOT project 0-7256 "Monitoring and Speciation of Particulate Matter Under 2.5 Microns (PM2.5) Composition across Texas Counties" to enhance data collection and analysis, ensuring effective mitigation efforts.]]></description>
      <pubDate>Mon, 29 Sep 2025 16:24:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2604527</guid>
    </item>
    <item>
      <title>Monitoring and Speciation of Particulate Matter Under 2.5 Microns (PM2.5) Composition across Texas Counties</title>
      <link>https://rip.trb.org/View/2593191</link>
      <description><![CDATA[Texas needs a detailed understanding of statewide particulate matter under 2.5 microns (PM2.5) sources, as current regulatory monitoring lacks the granularity for source apportionment. Since speciated PM2.5 data is limited, the research team will collect and analyze samples from multiple regions nearing or exceeding the 9.0 µg/m3 threshold set by the Environmental Protection Agency (EPA). The research team will conduct source apportionment analysis and highlight the prominent sources of PM2.5 emissions by region. This will support future research and regulatory efforts, such as developing and implementation appropriate emission reduction strategies. The research team will collaborate with state and local governments, academia and other stakeholders, to acquire any existing data and ensure local regulations and best practices are met. The research team will collaborate with the Texas Department of Transportation (TxDOT) project 0-7257, "The Effects of Road Types and Construction Activities on Particulate Matter and Development of Best Practices for PM2.5 Reduction" by sharing data, resources, and coordinating efforts to enhance data collection and analysis.]]></description>
      <pubDate>Tue, 26 Aug 2025 12:42:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2593191</guid>
    </item>
    <item>
      <title>Cabin Air Safety</title>
      <link>https://rip.trb.org/View/2582997</link>
      <description><![CDATA[The Federal Aviation Administration (FAA) needs to conduct a study pertaining to cabin air quality and any risk of, and potential for, persistent and accidental fume or smoke events onboard a passengercarrying aircraft operating under part 121 of title 14, Code of Federal Regulations. This study was directed in Section 362 of the 2024 FAA Reauthorization Act, with the study commencing not later than 3 years after the date of enactment of the Act.  
Sec. 362 - Cabin Air Safety Act which was included in the FAA Reauthorization Act of 2024. 
Here's a breakdown of what this legislation entails for cabin air safety:
Enabling Standards: This act empowers the Federal Aviation Administration (FAA) to establish standards for cabin air quality.
Mandatory Training: It mandates training for airline personnel regarding toxic smoke/fumes on aircraft.
Onboard Detectors: It requires airlines to install and maintain onboard air quality detectors.
Studying Bleed Air Contaminants: It directs the FAA to study bleed air contaminants in the cabin and issue recommendations.
Standardized Reporting System: It also mandates the FAA to develop a standardized and centralized system for flight attendants, pilots, and aircraft maintenance technicians to report and track fume and smoke events. ]]></description>
      <pubDate>Tue, 05 Aug 2025 18:22:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2582997</guid>
    </item>
    <item>
      <title>Evaluate Speed Estimation Techniques for EPA Moves Model Input Using Big Data and Travel Demand Models for Regional Conformity Analysis</title>
      <link>https://rip.trb.org/View/2562324</link>
      <description><![CDATA[Michigan Department of Transportation's (MDOT’s) Statewide and Urban Travel Analysis (SUTA) Section is required to run the Environmental Protection Agency’s (EPA) MOtor
Vehicle Emission Simulator (MOVES) Model for regional transportation conformity analysis, and for other purposes. One crucial input
to the MOVES model is an average speed distribution configured into sixteen-speed bin classifications, categorized by MOVES road
type and source type, hour of the day, and for base and future analysis years. SUTA’s current methodology incorporates average
speeds from their statewide and urban [regional] travel demand models (traditional four-step trip-based models administered in the
Caliper TransCAD Platform). There exists a gap between the travel demand model outputs (average speed) and the required inputs
(sixteen speed bin distribution) to the MOVES Model. SUTA staff are proposing a research project to identify how archived real time
speed data available to MDOT (currently INRIX speed data available through the CATT Lab’s RITIS Platform) can be used to fill this
gap between travel demand and air quality models, as well as forecast speed distributions using the results from the travel demand
model(s). The results are to be organized into a useable format to generate a reliable average speed distribution that can be used as
input into the EPA MOVES Model.]]></description>
      <pubDate>Mon, 09 Jun 2025 08:02:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2562324</guid>
    </item>
    <item>
      <title>Framework for Enhanced Model Evaluations for Project-Level Air Quality Analyses


</title>
      <link>https://rip.trb.org/View/2558418</link>
      <description><![CDATA[State departments of transportation (DOTs) conduct project-level air quality analyses as part of the National Environmental Policy Act and 1990 Clean Air Act Amendments to meet transportation conformity rule requirements. These analyses are conducted with travel demand forecasting models; vehicle emissions models, such as the Motor Vehicle Emission Simulator (MOVES) and the EMFAC (EMission FACtor) model; and air quality dispersion models, such as the American Meteorological Society (AMS)/Environmental Protection Agency (EPA) Regulatory Model (AERMOD). A model evaluation process (MEP) is typically employed to assess the contextual suitability of available models and conformance with the regulatory modeling chain (RMC) of traffic, emissions, and, as applicable for the pollutant(s) involved, dispersion, including the determination of representative background concentrations. 

However, current MEPs typically focus on individual models and not the overall RMC or the full array of regulatory transportation applications (e.g., project types and associated configurations, operating conditions and settings, pollutants and compliance tests) required to be modeled. NCHRP Research Report 1058: Assessing Air Pollution Dispersion Models for Emissions Regulation first recommended the implementation of an enhanced model evaluation process (EMEP) for the RMC to supplement existing evaluation processes that focus on individual models. Responding to an EPA notice of proposed rulemaking (NPRM), the American Association of State Highway and Transportation Officials (AASHTO) indicated additional support for the development of an EMEP in its Comments on Notice of Proposed Rulemaking, Guideline on Air Quality Models Enhancements to the AERMOD Dispersion Modeling System. Research is needed to further develop a framework to develop this EMEP and update existing recommendations. 

OBJECTIVE: The objective of this project is to further develop the framework for an EMEP for transportation air quality models for surface transportation projects and support its implementation. ]]></description>
      <pubDate>Mon, 26 May 2025 22:32:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2558418</guid>
    </item>
    <item>
      <title>An Economical and Sustainable Dust Suppressant for Gravel Roads</title>
      <link>https://rip.trb.org/View/2508967</link>
      <description><![CDATA[The 66,000-mile-long network of unpaved gravel roads connect 1.2 million rural Iowa population and serves as a backbone to Iowa’s $27 billion per year agrarian economy. On unpaved roads, fugitive dust emanates from the mechanical interaction between the moving vehicles and the crushed aggregates. Fugitive dust primarily comprises of soil minerals (e.g., oxides of silicon, aluminum, calcium, and iron) with particulate material sizes lower than 10 μm (PM10) [4]. According to the National Transportation Statistics (NTS) report published in 2018, approximately 18.5 million short tons of PM10 and 5.34 million short tons of PM2.5 particulates (size lower than 2.5 μm) are entrained into the air annually. About 35% of this particulate material comes from unpaved roads. From the health, economic, and safety points of view, the generation of fugitive dust poses a serious threat to road users and people living in the vicinity of the unpaved roads. Furthermore, the unpaved roads will deteriorate faster due to the loss of fines that bind the larger aggregates. Fugitive dust lowers the visibility on gravel roads leading to accidents. Examples of some accidents occurred in the past due to fugitive dust include a chain of vehicle crashes near I-39 Wisconsin, accidents near Interstate 5 in Coalinga, California, a fatal ATV rollover crash in Carlton country, Minnesota, crashes in the intersection of Conejo Avenue and Highway 41, California; crashes on U.S. Highway 87 between Great Falls and Fort Benton, accidents in Butler County, Missouri, etc. Currently, chlorides especially Calcium Chloride are applied on gravel roads to lower the fugitive dust. Calcium chloride being a hygroscopic material absorbs moisture from the atmosphere that cements the fine particulate material. However, chlorides are detrimental to concrete, corrode automobiles, lower the fertility of soils, and contaminate water bodies. The objective of this project is to synthesize and characterize a low-cost and sustainable dust suppressant that has both hygroscopic nature and agglomeration capability. To this end, both wet and dry formulations will be synthesized. Evaporation tests and wind tunnel tests will be conducted followed by field tests. Preliminary studies suggest that the wet formulation is at least 6 times better than traditional chloride-based dust suppressants.  ]]></description>
      <pubDate>Wed, 12 Feb 2025 12:37:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2508967</guid>
    </item>
    <item>
      <title>Examining the Impacts of Land Use on Air Quality in Chicago: Application of Street View Imagery and Hyperlocal Urban Climate Sensing</title>
      <link>https://rip.trb.org/View/2459119</link>
      <description><![CDATA[Urban climate sensors are being installed in cities all over the world in order to proactively address the growing issues posed by climate change and to monitor air quality in real time. This study focuses on the City of Chicago and evaluates air quality using Microsoft's recently released Project Eclipse sensors. Using a combination of conventional land use extraction methods and street view data from Google Street View (GSV), land use features close to the sensor locations were recovered. The distinctive qualities of street view photos were examined and spatial data was broken down using principal component analysis (PCA). In order to investigate the variables affecting air quality, the study also used SHapley Additive exPlanations (SHAP) and XGBoost machine learning regression. It is recommended that the built environment and land use in this area be addressed by the city and local authorities in order to mitigate future dangers.]]></description>
      <pubDate>Sat, 23 Nov 2024 11:00:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2459119</guid>
    </item>
    <item>
      <title>Understanding of Tire-Wear Gaseous Emissions and their Impact on Secondary Aerosol Formation from On-Road Vehicles</title>
      <link>https://rip.trb.org/View/2427675</link>
      <description><![CDATA[With the implementation of stringent emission regulations and the growth of electric vehicles, more attention should be shifted to road traffic-derived non-exhaust emissions. This study will investigate two novel topics that have not yet been thoroughly investigated by the scientific community but may potentially have significant air quality and health impacts. The first topic relates to the investigation of gaseous emissions from tire-wear. This phenomenon is characterized under the umbrella of tire off-gassing and can include gaseous compounds that are toxic, mutagenic, and carcinogenic to humans. The second topic relates to the assessment of secondary organic aerosol (SOA) formation from tire-wear gaseous emissions. An increase in SOA from non-exhaust vehicle emissions (i.e., tires) could lead to air quality degradation and increased health impacts. In this study, the researchers will measure the gaseous emissions from the off-gassing of tires during laboratory and real-world testing conditions. The researchers will also evaluate the SOA forming potential from the off-gassing of tires during only laboratory conditions using state-of-the-art instrumentation. It is expected that the results from this study will contribute to the creation of tire-wear gaseous emission factors and to a better understanding of their impact on SOA formation. The findings from this study will help address the impacts of tire-wear emissions from mobile sources to communities living near roadways. ]]></description>
      <pubDate>Thu, 12 Sep 2024 15:20:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2427675</guid>
    </item>
    <item>
      <title>Curbing Emissions: Enhancing Sustainability Through Collaborative
Shipment in Horizontal Supply Chains
</title>
      <link>https://rip.trb.org/View/2420218</link>
      <description><![CDATA[In recent years, the importance of sustainable and resilient supply chains has become increasingly
important due to the growing concerns over energy crises, environmental pollution, and supply
chain disruptions. Amidst these challenges, battery electric vehicles (BEVs) have emerged as a
promising solution as they offer several key advantages that contribute to sustainable and resilient
supply chains. BEVs have the potential to significantly reduce greenhouse gas (GHG) emissions in
the logistics sector, thereby helping companies meet their sustainability goals and mitigate their
environmental impact. Moreover, BEVs can enhance supply chain resiliency by reducing reliance
on volatile fossil fuel markets and providing a more stable and predictable energy source for
transportation. However, the BEV technology is still evolving, and the significant initial investment
required for BEV adoption remains a deterrent, particularly among small companies. To address
this challenge, the research team proposes a cooperative mechanism for BEV adoption among multiple small
companies in a horizontal supply chain. The team specifically focuses on the logistics sector, where a set
of firms deliver their products to their customers through a shared distribution center using a
BEV fleet. Such collaborative shipment of products through BEVs would directly contribute to
the reduction of carbon emission and dependency on fossil fuels. Additionally, this approach
promotes better vehicle utilization, as the shared fleet is used more efficiently across participating
companies. This, in turn, will lead to reduced congestion and fewer vehicle miles traveled (VMT),
further contributing to the sustainability and operational efficiency in the logistics sector.]]></description>
      <pubDate>Sat, 24 Aug 2024 10:56:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2420218</guid>
    </item>
    <item>
      <title>Development of a Project-Scale Air Quality Screening Tool</title>
      <link>https://rip.trb.org/View/2417471</link>
      <description><![CDATA[This project’s goal is to estimate air pollution levels associated with transportation projects through various pollutants such as carbon monoxide, ozone, particulate matter and greenhouse gases. Researchers will develop an analysis tool for 
Illinois Department of Transportation (IDOT) to estimate project-specific air pollution levels. Development of the analysis tool will allow IDOT and its partners to better estimate and improve transportation-related emissions.]]></description>
      <pubDate>Fri, 16 Aug 2024 09:17:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2417471</guid>
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