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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=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0bG9naWMiIHZhbHVlPSJvciIgLz48cGFyYW0gbmFtZT0idGVybXNsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJsb2NhdGlvbiIgdmFsdWU9IjE2IiAvPjwvcGFyYW1zPjxmaWx0ZXJzPjxmaWx0ZXIgZmllbGQ9ImluZGV4dGVybXMiIHZhbHVlPSImcXVvdDtEYXRhJnF1b3Q7IiBvcmlnaW5hbF92YWx1ZT0iJnF1b3Q7RGF0YSZxdW90OyIgLz48L2ZpbHRlcnM+PHJhbmdlcyAvPjxzb3J0cz48c29ydCBmaWVsZD0icHVibGlzaGVkIiBvcmRlcj0iZGVzYyIgLz48L3NvcnRzPjxwZXJzaXN0cz48cGVyc2lzdCBuYW1lPSJyYW5nZXR5cGUiIHZhbHVlPSJwdWJsaXNoZWRkYXRlIiAvPjwvcGVyc2lzdHM+PC9zZWFyY2g+" 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>Advancing Pollinator Habitat Monitoring through Remote Sensing on Nebraska Roadsides</title>
      <link>https://rip.trb.org/View/2689394</link>
      <description><![CDATA[To meet monitoring and reporting requirements under the Monarch Candidate Conservation Agreement with Assurances (CCAA), Nebraska Department of Transportation (NDOT) must collect consistent data on milkweed stem density and nectar-plant cover across extensive roadside networks. Current field-based approaches, though effective, are resource-intensive, limited in spatial coverage, and require a specialized level of biological expertise. NDOT needs a scalable and cost-effective remote sensing strategy that can meet CCAA requirements. Furthermore, NDOT must understand the costs and benefits to applying this technology in-house or via external contract, and how the products could be applied to offer NDOT versatile imagery and data outputs that can support broader environmental review needs, planning, and maintenance decisions.]]></description>
      <pubDate>Fri, 05 Jun 2026 12:41:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2689394</guid>
    </item>
    <item>
      <title>SPR-5042: Performance and Safety Evaluation of Truck Mounted Debris Clearing Systems</title>
      <link>https://rip.trb.org/View/2709430</link>
      <description><![CDATA[The principal investigators will help the Indiana Department of Transportation (INDOT) evaluate truck-mounted debris clearing systems by achieving the following three main objectives: 1) Development of an event-triggered, multi-sensor data collection framework integrating multi-camera video and Global Positioning System (GPS) to enable automated, machine vision-based performance assessment. 2) Quantitative evaluation of system performance through field testing to measure debris removal effectiveness, roadway interaction, and operational efficiency across real-world conditions. 3) Assessment of safety and traffic impacts by analyzing worker exposure, operational risks, and vehicle interactions to quantify how these systems influence roadway safety and deployment practices.]]></description>
      <pubDate>Wed, 03 Jun 2026 13:31:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709430</guid>
    </item>
    <item>
      <title>SPR-5041: SPR-4517 Implementation: Wireless Data Collection and Model Development</title>
      <link>https://rip.trb.org/View/2709427</link>
      <description><![CDATA[SPR-4517 deployed an edge-enabled, solar-powered wireless monitoring system at the I-69 instrumentation section to evaluate pavement drainage and related performance indicators. This implementation study will sustain field hardware, formalize an automated data pipeline with quality assurance/quality control (QA/QC) protocols, develop and validate performance indicators and predictive models, and package the complete workflow for the Indiana Department of Transportation (INDOT). Outcomes include continued wireless monitoring, versioned data products with monthly health reports, validated drainage performance models, and a transferable implementation package enabling INDOT to maintain long-term field data collection and adopt similar capabilities at additional sites.]]></description>
      <pubDate>Wed, 03 Jun 2026 13:23:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709427</guid>
    </item>
    <item>
      <title>Developing a Standardized Framework for Real-Time Freight-Specific Traveler Information and Route Restrictions for Commercial Motor Vehicle Operators; Truck Parking Data Exchange Standards</title>
      <link>https://rip.trb.org/View/2709247</link>
      <description><![CDATA[Commercial motor vehicle (CMV) operations increasingly rely on maps and navigation systems that were not designed to address the unique needs of freight operations. This mismatch contributes to increased safety risks, including unplanned diversions, bridge strikes, congestion in freight corridors, lane geometry constraints, and other routing errors.

Today, the lack of a standard, consistent data structure or framework for sharing real-time freight-specific information remains a foundational challenge for public agencies and for the economy that depends heavily on the national roadway network. Public agencies currently lack a widely accepted standard or shared framework for communicating restrictions, alerts, and disruptions to CMV operators. Existing standards such as the Traffic Management Data Dictionary (TMDD) and SAE J2354 (Advanced Traveler Information Systems) support general traveler messaging but do not include freight-specific data elements.

In addition, the growing need for timely and reliable truck parking information, coupled with the rapid expansion of truck parking information systems, demonstrates the need for standardized methods to collect and disseminate truck parking data. As technologies used in these systems become increasingly ubiquitous, and as industry expectations and preferences continue to evolve, standardization of both information and dissemination tools becomes a critical next step.

OBJECTIVES: The objectives of this research are: (1) to develop a unified data framework for delivering time-sensitive, relevant, and actionable freight-specific traveler information messaging to CMV operators; and (2) to develop proposed data standards for real-time, public and private truck parking availability and attributes (including the number of spaces, size, hours of availability, and available amenities).

]]></description>
      <pubDate>Tue, 02 Jun 2026 14:33:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709247</guid>
    </item>
    <item>
      <title>Low-Frequency High-Impact Travel for Data Analysis</title>
      <link>https://rip.trb.org/View/2709248</link>
      <description><![CDATA[Low-incidence travel behavior is difficult to capture in a traditional household travel study, where typically one to seven days of travel are collected from a representative sample of households. These behaviors may include travel modes used frequently by a small number of people (bicycling, carshare/vanpool), emerging modes not yet widely adopted (e-bikes, scooters, automated vehicles), complex household travel interactions, or infrequent behaviors such as rideshare use, long-distance travel, and trip replacement behavior such as home delivery of goods and services.

Because these behaviors occur infrequently, traditional survey methods often fail to collect enough observations for accurate estimation in travel demand models. A sufficient number of surveys—approximately 1,000 observations per market segment—is needed to support reliable analysis and forecasting. Despite their low incidence, many of these behaviors have significant impacts on transportation systems.

More than 40 state departments of transportation (DOTs) maintain statewide travel models that require accurate long-distance travel data to support costly intercity highway and rail investments. Emerging travel modes are also becoming critical policy issues in regional and statewide planning efforts.

The objective of this research is to identify and analyze methods for sampling people, households, and incidences of rare or emerging travel behaviors and determine how these methods can be incorporated into household travel survey data collection.]]></description>
      <pubDate>Tue, 02 Jun 2026 13:56:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709248</guid>
    </item>
    <item>
      <title>Scoping Study: Vertical Visibility Constraints -- Vertical Curvature Traffic Control Devices</title>
      <link>https://rip.trb.org/View/2709249</link>
      <description><![CDATA[Horizontal and vertical curves can obscure key roadway features or activity that may lie ahead of unaware drivers. Roadway curvature is a significant factor in roadway departure crashes, injuries, and fatalities. As land use has developed and activities on roads have changed, the potential for conflicts has grown. It is impractical and beyond the resources of roadway authorities to improve all roadway alignments to attain optimal sight distance. This is a growing concern as active transportation increases in many rural areas, especially those experiencing increased tourism. Horizontal curvature on roadways where drivers’ views are obstructed has been thoroughly researched, leading to well-accepted strategies for traffic control devices in the Manual on Uniform Traffic Control Devices (MUTCD). However, similar research has yet to be conducted for vertical curves.

The objective of this research is to develop a scoping study to clearly define and refine the research needs, objectives, and expected products necessary to address vertical visibility constraints, including exploring the relevance of crash data to vertical curves and developing a research work program to explore solutions. The intent of potential larger, follow-on, NCHRP study is to obtain data from vertical-curvature-related crashes to assess the details of occurrence, frequency, and severity, and to better understand road user needs, rather than relying on approaches used in prior studies.]]></description>
      <pubDate>Tue, 02 Jun 2026 13:49:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709249</guid>
    </item>
    <item>
      <title>Research for the AASHTO Standing Committee on Planning. Task 63. Making NAICS (North American Industrial Classification System) Work for Transportation</title>
      <link>https://rip.trb.org/View/2706284</link>
      <description><![CDATA[Census data historically have been reported using the Standard Industrial Classification (SIC) System. The North American Industrial Classification System (NAICS) was introduced in 1997 to address a need for a new, more rational approach to tallying industrial classes of workers. There are several differences between NAICS and SIC that make a straightforward comparison difficult. NAICS is based on how products and services are created, while SIC focuses on what is created.

The main objective of this report is to analyze the differences between demographic survey data and establishment-based data when they are reported in the same NAICS categories. Specifically, the research focuses on the mismatch between the two databases for the category “Management of Companies and Enterprises”.]]></description>
      <pubDate>Wed, 27 May 2026 15:06:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2706284</guid>
    </item>
    <item>
      <title>Damage Progression of Highway Bridges and Operational Vibration-Waveforms-Phase-2</title>
      <link>https://rip.trb.org/View/2706038</link>
      <description><![CDATA[Aging highway bridges are increasingly subjected to heavy truck traffic that can exceed design load expectations and accelerate structural deterioration. Undetected overload events may contribute to localized stress concentrations, fatigue damage, and reduced service life. Current bridge monitoring approaches typically rely on periodic inspection rather than continuous operational detection of extreme loading events.
This project advances a vibration-based monitoring methodology to detect, identify, and predict the weight of heavy vehicles causing extreme loading on highway bridges. Building on Phase 1 results, the research integrates multi-sensor data—including accelerometers, six-dimensional inertial sensors, strain sensors, gyroscopes, and radar-video systems—to identify overload events and correlate them with structural response and potential damage hot spots. Finite element modeling and moving-load simulations will be used to support weight estimation and validate field measurements. The methodology will be tested on single- and multi-span steel and concrete girder bridges in Iowa. The resulting system is designed to provide a practical, portable, and cost-effective approach for bridge overload detection and condition-informed decision-making.

]]></description>
      <pubDate>Sat, 23 May 2026 18:06:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2706038</guid>
    </item>
    <item>
      <title>Phase II: After Study Evaluation of Interstate 4 (I-4) Florida's Regional Advanced Mobility Elements (FRAME) Project (After Analysis)</title>
      <link>https://rip.trb.org/View/2706007</link>
      <description><![CDATA[Restart of BED26-977-08. The objective of this research project is to develop the evaluation plan for the after conditions of the I-4 FRAME project. Then, before/after study for the evaluation metrics will be conducted to identify the degree of improvement (or not) for every metric from the before to the after observations. The study findings will be analyzed and documented. To conduct the task, the research team will perform the following activities: (1)  determine the evaluation criteria tailored to the I-4 FRAME project objectives; (2) describe the data collection procedures tailored to these criteria that are needed to report on the achievement of project objectives; and (3) document how the I-4 FRAME project addressed the safety challenges on the project corridors compared with Phase ? (before).]]></description>
      <pubDate>Fri, 22 May 2026 09:10:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2706007</guid>
    </item>
    <item>
      <title>How Can NMDOT Use TSD Data to Support Pavement Design</title>
      <link>https://rip.trb.org/View/2704033</link>
      <description><![CDATA[TSD is a valuable technology for measuring surface deflections at short intervals and capturing data on roughness, texture, and rutting at traffic speed. Several highway agencies in the United States and other countries are currently either looking into how to use TSD data in their pavement management system (PMS) to ensure responsible expenditure of taxpayers’ dollars.]]></description>
      <pubDate>Wed, 20 May 2026 11:15:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2704033</guid>
    </item>
    <item>
      <title>Investigating the Critical Success Factors for Technology Adoption in Transportation Safety Systems</title>
      <link>https://rip.trb.org/View/2703924</link>
      <description><![CDATA[The United States continues to face challenges in maintaining safe and efficient transportation networks, as roadway crashes remain a leading cause of death and economic loss. Although considerable advancements have been made in vehicle and roadway safety, emerging technologies remain underutilized in practice. This limited adoption highlights a critical need to better understand the factors that influence the successful implementation of transportation safety technologies. This research addresses this gap by identifying the critical success factors and barriers affecting the adoption of digital transportation safety technologies in the U.S. The study adopts a multi-stage approach consisting of a literature review, an expert survey, and network analysis to identify key adoption factors and their interrelationships. The expected outcomes include the development of a structured framework of adoption factors and practical implementation guidelines to support transportation agencies in deploying digital safety technologies more effectively. By facilitating the transition from technological development to real-world implementation, the research supports safer and more resilient transportation networks.
]]></description>
      <pubDate>Wed, 20 May 2026 09:18:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703924</guid>
    </item>
    <item>
      <title>National Accessibility Evaluation Phase II</title>
      <link>https://rip.trb.org/View/2703751</link>
      <description><![CDATA[This project implements activities for the National Accessibility Evaluation (NAE) pooled-fund study, performing accessibility evaluations describing conditions in 2020, 2021, 2022, 2023, and 2024. The National Accessibility Evaluation creates national census block-level accessibility datasets that can be used by partners in local transportation system evaluation, performance management, planning, and research efforts. The project produced a series of annual reports describing accessibility to jobs by driving, biking, walking, and by transit in metropolitan areas across America.

Accessibility calculations rely on detailed travel-time calculations for both driving and transit, using commercially available, global positioning system (GPS)-based speed measurements and published transit schedules. Each NAE partner received digital access to the accessibility datasets covering their jurisdictions. These datasets quantify access to jobs, health care, schools, grocery stores, and other essential destinations. The annual Access Across America reports provide summaries of the detailed job accessibility datasets for the 50 most populous metropolitan areas across America. These reports were released to national and local media outlets and supported by publicity and communications efforts.]]></description>
      <pubDate>Fri, 15 May 2026 15:29:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703751</guid>
    </item>
    <item>
      <title>Assessing the Reliability and Usability of Mobile Ticketing App Data for Transit Analytics: A Case Study of Unitrans in Davis, California</title>
      <link>https://rip.trb.org/View/2702581</link>
      <description><![CDATA[Mobile ticketing apps have become increasingly popular among transit agencies due to their cost efficiency and ability to streamline payments. Beyond operational efficiencies, these apps also generate vast travel data with the potential to support transit agencies in decision-making. However, this data contains incomplete trip information and suffers from representation bias. Several questions remain unanswered: Is this data representative of all transit riders? If so, what are the potential applications? 

This project will address this gap by evaluating the potential applications and representativeness of app data. The research will focus on ZipPass, a mobile ticketing app used by Unitrans in Davis, California. To date, ZipPass has already generated over one million spatial activation records. The project team devised a strategy to integrate ZipPass data with the onboard transit survey and the UC Davis campus travel survey. The team will also conduct a targeted survey of active ZipPass users to supplement rider-specific and trip-level information. The project will explore how ZipPass data, along with support from supplementary data sources, can be used for two potential applications to support the agency: (1) estimating transit ridership and (2) understanding riders' origin-destinations. 

The research will provide valuable insights to transit agencies looking to harness mobile ticketing data for operational purposes. Since periodic onboard transit surveys are required for federal funding, both mobile ticketing data and transit survey data are available to agencies at no extra expense. Small agencies can leverage our findings to integrate at least these two datasets and effectively utilize them for operational improvement.]]></description>
      <pubDate>Thu, 14 May 2026 16:51:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2702581</guid>
    </item>
    <item>
      <title>Evaluating Behavioral Responses to Mobility Credits and Ridehailing Integration in a Digital Mobility System</title>
      <link>https://rip.trb.org/View/2702725</link>
      <description><![CDATA[Digital mobility platforms are increasingly adopted by public agencies to coordinate multimodal travel, streamline fare payment, and improve efficiency. However, there is limited empirical evidence on how users respond to platform-based incentives and integrated services in real-world settings, as most studies rely on stated preference data or simulations. This project analyzes user behavior on Vamos-EZHub, a public digital mobility platform that integrates trip planning, fare payment, and access to services including local transit and ridehailing. It evaluates behavioral responses to two sequential interventions on Vamos-EZHub: (1) the introduction of prepaid mobility credits and (2) the integration of a transit-triggered ridehailing credit. 

Using longitudinal platform telemetry, ridehailing trip records, transit fare activation data, and General Transit Feed Specification (GTFS) data, the project examines how mobility and ridehailing credits affect platform engagement, transit and ridehailing use, first/last-mile connectivity, and spatial and temporal patterns of linked travel. Two-way fixed effects and event-study models are used to identify behavioral changes associated with each intervention. A geospatial-temporal algorithm classifies ridehailing trips connecting to transit, and stop- level regression models identify transit service and network characteristics associated with demand for linked trips. 

Expected outcomes include quantitative estimates of the influence of mobility credits and ridehailing integration on multimodal coordination, identification of service characteristics associated with higher demand for linked trips, and a reproducible analytical framework. The results will inform data-driven platform design, operational planning, and integration strategies for public agencies managing digital mobility platforms, while providing evidence to guide coordination with private ridehailing partners to improve system efficiency and reliability.]]></description>
      <pubDate>Thu, 14 May 2026 16:36:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/2702725</guid>
    </item>
    <item>
      <title>How Much Do Attitudinal Variables Improve Travel Demand Models? Evaluation Using an Overlap Sample from an Attitude-rich Survey and the 2017 National Household Travel Survey</title>
      <link>https://rip.trb.org/View/2702856</link>
      <description><![CDATA[A line of research has recently been launched on attitude imputation using machine learning (ML) functions trained on variables common to two survey datasets (Mokhtarian, 2024). It was discovered that using a handful of attitudinal marker variables (i.e., the one or two attitudinal items most strongly associated with each attitude) as common variables for imputation (Shaw, 2021; Soria and Mokhtarian, 2024) far outperforms other approaches such as using socio-demographic and land-use variables (Malokin et al., 2019) and targeted marketing variables (Shaw, 2021). The basic idea is to use one survey dataset (the “donor sample”) to train an ML function that predicts attitudinal factor scores using marker variables, and then apply that function to another dataset (the “recipient sample”) that contains the same marker variables, to impute attitude scores into it. This allows attitudinal information to be attached to the respondents in the recipient sample without measuring the whole set of attitudinal variables used to reveal the attitudinal factor structure in the donor sample.]]></description>
      <pubDate>Thu, 14 May 2026 15:45:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2702856</guid>
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