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    <title>Research in Progress (RIP)</title>
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    <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>Innovative Approaches to Maintenance Funding for Active Transportation Infrastructure on State Highways Research</title>
      <link>https://rip.trb.org/View/2712169</link>
      <description><![CDATA[Currently, no substantial detailed advice or research exists in the leading national work on active transportation maintenance or active transportation policy on innovative maintenance funding strategies. More robust resources do exist for capital expenditures and new projects. Current funding maintenance recommendations generally do not go beyond mentioning that consistent funding is good and having a plan is good. Good public policy requires more detailed thought and specific guidance, which are especially critical during the planning and scoping phases to build maintainability in from project initiation.

Research is needed to address this gap and build from recently completed maintenance research. A comprehensive list, an analysis of funding sources and their constraints, and a cost-to-benefits comparison will provide the necessary groundwork for an informed assessment of innovative research strategies and enable good case studies to be identified. Focusing on ongoing maintenance needs, guidance for planning and scoping phases, and guidance on how active transportation maintenance fits into the larger transportation context would help ensure that these strategies work for current maintenance teams and result in better new projects coming into maintenance obligations.

The objective of this research is to identify innovative funding strategies for active transportation facility maintenance on state highways. Appropriately maintained facilities provide road safety, economic development, and land value benefits that could be factored into strategies. The research will address how these strategies align with ongoing maintenance needs for these facilities and provide guidance on maintenance in the planning and scoping of projects.]]></description>
      <pubDate>Tue, 09 Jun 2026 12:38:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712169</guid>
    </item>
    <item>
      <title>Advanced Technologies and Data Analytics for Safe, Smart, and Efficient Transportation (ASSET)</title>
      <link>https://rip.trb.org/View/2709572</link>
      <description><![CDATA[This project assists the Massachusetts Department of Transportation (MassDOT) with (A) calibrating safety models for urban and suburban arterial intersections and developing artificial intelligence models for (B) detecting sidewalks and (C) counting multimodal trips.  

There are three main goals:

(A) Calibrate the Safety Performance Functions (SPFs) in Chapter 16.6.4 of the Highway Safety Manual, 2nd Edition (HSM2), along with the associated parameters, for the twelve types of urban and suburban intersections in Massachusetts using the most recent data.

(B) Develop an Artificial Intelligence (AI) model to automate the detection and mapping of sidewalks from publicly available aerial imagery. Also, the model will be used to identify changes in sidewalks using aerial imagery from multiple years.

(C) Leverage AI to automate the counting of pedestrians, active transportation modes (such as bicycles and e-scooters), and site-generated trips from new developments. The results of this task will form the basis for developing AI and/or statistical models to estimate multimodal trip counts required for transportation planning purposes.]]></description>
      <pubDate>Wed, 03 Jun 2026 15:27:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709572</guid>
    </item>
    <item>
      <title>Improving Crash Data for Active Transportation Users</title>
      <link>https://rip.trb.org/View/2701260</link>
      <description><![CDATA[In recent years, the United States has experienced sharp, inexplicable increases in the number of pedestrian fatalities. In response to this disturbing trend, the Governors Highway Safety Association, state highway safety offices (SHSOs), state departments of transportation (DOTs), and local transportation agencies have been conducting safety analyses to better understand the problem and develop remediation plans.  

Crash data is the primary source of information used for safety analysis. This critical data source, however, has many limitations, including inconsistencies in reporting, inaccurate or incomplete coding of crashes, and underreporting, especially for active transportation/non-motorized users (herein after referred to as active transportation users). Also, crash typing (used to describe events and movements prior to a crash) can lack details for pedestrian- and bicycle-involved crashes, and in some cases must be constructed using multiple variables.

Improving pedestrian and bicyclist injury and fatality data, adopting consistent typing methods at the national and local levels, improving data storage, sharing and accessibility, and integrating police and hospital crash data would help practitioners understand risk factors and potential countermeasures. It is important to understand the reasons for crash data limitations, related implications, and measures that can be taken to improve the completeness, consistency, and accuracy of crash data for active transportation users.

Research is needed to improve the current state of the practice for collecting injury and fatality data for active transportation users.

OBJECTIVE: The objective of this research was to develop recommendations to improve the completeness, consistency, and accuracy of crash data for active transportation users. The project sought to (1) document current shortcomings related to existing data for crashes involving active transportation users (including crashes that do not involve motor vehicles in transport), and (2) consider non-motorist victim characteristics. ]]></description>
      <pubDate>Tue, 12 May 2026 15:48:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2701260</guid>
    </item>
    <item>
      <title>Monitoring Active Transportation Demand and Safety with Computer Vision</title>
      <link>https://rip.trb.org/View/2696847</link>
      <description><![CDATA[Monitoring demand for and safety of active transportation has been a challenge for decades. With a history of designing roads for cars and monitoring efforts similarly aimed at the flow of cars, transportation researchers and professionals lack system-level knowledge of active transportation. The current state of bicycle and pedestrian counting practice in most cities deploys a few costly permanent counters using inductive loops and passive radar, combined with a few days of manual peak hour traffic counts at few intersections. This neither monitors system-wide demand nor safety. However, recently several companies have produced video- and LiDAR-based sensors and multiclass tracking technology to monitor active transportation demand and unsafe events. These sensors can be installed permanently, or temporarily, and are generally lower in cost to install than other permanent counting devices. This research will leverage an ongoing Caltrans project with these sensors to validate safety metrics, and a mobile version of the sensors to collect active transportation count data for modeling system level active transportation volume in Davis, California as a pilot for other cities and agencies. It will include the prediction of network-wide travel volumes for planning the intervention purposes, and two safety metric evaluations. The final report is expected to not only provide information on the state-of-the-art in active transportation monitoring, but will have direct policy impacts by informing the Active Transportation Data program within Caltrans Traffic Operations, among other programs such as the Active Transportation Resource Center research-to-practice education elements.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:05:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696847</guid>
    </item>
    <item>
      <title>Observational Intersection Traffic Safety Analysis</title>
      <link>https://rip.trb.org/View/2655705</link>
      <description><![CDATA[While planners and engineers design intersections with safety in mind, the intended use and actual use of facilities do not always align. This misalignment can lead to increased safety risks for all intersection participants, particularly non-motorized users including pedestrians and cyclists. Although facility utilization mismatches can be detected through observation, typical monitoring occurs only during limited peak hours, failing to fully capture comprehensive usage patterns and emerging safety concerns.

This research proposes long-term intersection monitoring to uncover emerging facility utilization patterns and assess inherent intersection safety. The approach leverages existing traffic camera infrastructure combined with modern deep learning techniques for accurate detection and tracking of vehicles, bicycles, and pedestrians. As an explicit use case, the study examines unprotected left-turns to characterize both vehicle-vehicle conflicts through time gap analysis and trajectory conflicts involving other road users. The project develops a computer vision system capable of processing trajectories to quantify left-turns with insufficient gaps, instances where vehicles fail to yield appropriately, and average time gaps, collectively providing metrics to characterize intersection safety.

This interdisciplinary project combines computer vision algorithm development expertise from the University of Nevada, Las Vegas (UNLV) with programming support from Howard University. System evaluation will occur at intersections in both the Washington, DC area and the Las Vegas metropolitan area, utilizing purpose-built high-resolution monitoring equipment for short-term deployment as well as existing lower-resolution traffic cameras for long-term analysis. The project leverages intersection equipment acquired through NSF Award Number 2216489.

Expected outcomes include research contributions in computer vision and machine learning for trajectory analysis, workforce development through student training across both institutions, and technology transfer through publications on intersection safety scoring and practitioner engagement for field deployment.]]></description>
      <pubDate>Mon, 19 Jan 2026 16:30:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655705</guid>
    </item>
    <item>
      <title>Transportation and Mental Health in Central Texas Using 211 Call Center Data – An Exploratory Analysis</title>
      <link>https://rip.trb.org/View/2652177</link>
      <description><![CDATA[Mental health is an important part of an individual’s well-being and has been included as a key topic by the U.S. Centers for Disease Control and Prevention. Lack of access to affordable and efficient transportation can isolate individuals, limiting their ability to maintain employment, attend healthcare appointments, or engage in social and recreational activities—all of which are vital for mental well-being.  Long commutes, traffic congestion, and unreliable transit can contribute to chronic stress, anxiety, and fatigue, especially in urban environments. Active transportation options like walking and cycling not only reduce stress but also promote physical activity, which can reduce symptoms of depression.  
The research project aims to understand the multifaceted relationships between transportation and mental health by conducting a literature review using Latent Dirichlet Allocation (LDA) in topic modeling to identify prevailing themes and research trends in transportation and mental health. Also, through collaboration with United Way for Greater Austin, this project will incorporate insights from 211 Call Center staff and volunteers to better understand transportation-related mental health concerns, from a frontline service perspective. The project will then analyze 211 Call Center data provided by the United Way for Greater Austin. This analysis will explore spatial and temporal variations in mental health-related issues and examine how transportation correlates with mental health concerns. Caller comments, when available, will complement the quantitative data by providing personal context and deepening the understanding of lived experiences. Ultimately, the findings will inform policy recommendations aimed at addressing transportation barriers as a means to improve mental health outcomes in communities.    
]]></description>
      <pubDate>Tue, 13 Jan 2026 15:19:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652177</guid>
    </item>
    <item>
      <title>Assessing Quick Builds and Safe Streets for Non-Motorized Safety Using Simulations and Portable Sensing Technology</title>
      <link>https://rip.trb.org/View/2606402</link>
      <description><![CDATA[This research establishes a comprehensive evaluation framework for Quick Build interventions using portable LiDAR technology, driving simulation, and field data to quantify safety impacts for non-motorized road users including pedestrians and cyclists. Building on Phase 1 simulation-based foundations, the study addresses the challenge of limited long-term effectiveness data for quick-build street treatments such as curb extensions, protected bike lanes, and temporary traffic calming features. The methodology combines portable detection systems including LiDAR sensors, edge computing, and 5G connectivity to capture high-resolution vehicle and non-motorized transport trajectories, speeds, conflict points, and crossing patterns before and after intervention implementation. UC Win Roads driving simulator environments will replicate selected corridors to analyze road user behavior and interactions under controlled conditions, while survey questionnaires assess community perceptions of safety improvements. The research develops multimodal safety performance metrics tailored to temporary installations including Post Encroachment Time, Time to Collision, and crash modification factors for non-motorized transport modes. The study produces Standard Operating Procedures enabling Maryland Department of Transportation staff to implement evaluation methods independently for future Quick Build projects, supporting evidence-based decision-making for permanent infrastructure investments.]]></description>
      <pubDate>Thu, 02 Oct 2025 14:57:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2606402</guid>
    </item>
    <item>
      <title>RES2025-07: Active Transportation Quick-Build Program Guidelines</title>
      <link>https://rip.trb.org/View/2499109</link>
      <description><![CDATA[The primary objective of this research is to develop comprehensive guidelines for implementing quick-build safety countermeasures at high-crash locations for pedestrians, bicyclists, and other low-speed users in Tennessee. Principles in this guide can be utilized in other local jurisdictions and may have less-restrictive design constraints. This research aims to address the identified need for temporary safety interventions to completement permanent measures, fostering community engagement and reducing fatalities and serious
injuries of all road users.]]></description>
      <pubDate>Wed, 29 Jan 2025 10:29:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2499109</guid>
    </item>
    <item>
      <title>How Do Mode-Specific Network Metrics Impact Safety Outcomes?</title>
      <link>https://rip.trb.org/View/2472695</link>
      <description><![CDATA[Public transit and active transportation networks have been associated with improvements in multimodal traffic safety, yet their impacts in rural and peri-urban areas remain underexplored. This research evaluates the role of mode-specific network metrics—quantifying size, structure, and connectivity—on crash outcomes across New England using crash data and community characteristics. Predictive models incorporating regression and machine learning methods will identify which network features influence safety, providing actionable insights for planners and policymakers. Outputs include an open-access dataset of network metrics, a dashboard for visualizing results, and a decision-support tool for improving roadway safety across varied community contexts.
]]></description>
      <pubDate>Mon, 09 Dec 2024 10:04:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2472695</guid>
    </item>
    <item>
      <title>Investigating Travel Survey Representativeness: Who’s Missing and What Can We Do?</title>
      <link>https://rip.trb.org/View/2440263</link>
      <description><![CDATA[The core source of data for transportation planning and forecasting comes from household travel surveys. Travel surveys are used to obtain insight into the behavioral decisions of travelers; for example: (1) trip purposes such as work or shopping; (2) means/mode of transport such as car, walk, bus, etc.; (3) travel time; and (4) time of day/week. However, these surveys tend to underrepresent the views and needs of people of color and low-income travelers, precisely the groups that depend most on historically underfunded travel modes like public transit, biking, and walking. In addition to this underrepresentation, it is increasingly difficult to obtain high quality data from those who do respond (e.g., response biases, measurement errors for underrepresented groups), as well as to obtain the detailed contextual and psychological attribute information needed for accurate behavioral forecasting. The goal of this project is to investigate household travel survey biases to identify the causes and propose potential solutions.]]></description>
      <pubDate>Thu, 10 Oct 2024 16:16:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440263</guid>
    </item>
    <item>
      <title>Statewide Lane Reconfiguration “Road Diet” Screening for Louisiana</title>
      <link>https://rip.trb.org/View/2394474</link>
      <description><![CDATA[This research is to investigate opportunities for and feasibility of implementing road diets on roadways to help Louisiana develop a network accommodating non-motorized travels and meet other potential needs (e.g., speed management).]]></description>
      <pubDate>Tue, 18 Jun 2024 10:47:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2394474</guid>
    </item>
    <item>
      <title>Synthesis of Information Related to Highway Practices. Topic 56-05. Traffic Analysis Practices for Non-Motorized Modes



</title>
      <link>https://rip.trb.org/View/2384705</link>
      <description><![CDATA[The objective of this synthesis was to document the current state of the traffic analysis practice for non-motorized modes (or multimodal analysis). Research is complete. The final report will published in Fall 2026 as Synthesis Report 671. ]]></description>
      <pubDate>Fri, 31 May 2024 20:33:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2384705</guid>
    </item>
    <item>
      <title>Building an Effective Framework for Active Transportation Messaging</title>
      <link>https://rip.trb.org/View/2381710</link>
      <description><![CDATA[Active transportation consists of human-powered modes of transportation such as biking and walking. In addition to providing health benefits to users, active transportation can enable positive societal outcomes such as reducing vehicle usage and associated emissions, injuries, and air pollutants, and enhancing economic vitality in communities. As such, active transportation encompasses several complex and intersecting issues, such as public health, accessibility, data, economics, and safety. Historically, active transportation has been viewed in a silo, as an optional add-on, or as a design exception. Active transportation projects and strategies are developed by transportation and other government agencies, including state departments of transportation (DOTs), metropolitan planning organizations, localities, and municipalities. Communications and messaging are critical to raising the awareness of the benefits of active transportation and enabling the culture shift needed to consistently and sustainably provide safe active transportation. Research is needed to develop effective communication and messaging practices to reinforce and institutionalize active transportation investment.

The objective of this research is to develop a framework for state DOTs and other government agencies to communicate the processes and strategies for institutionalizing active transportation investment.
 ]]></description>
      <pubDate>Mon, 20 May 2024 21:57:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2381710</guid>
    </item>
    <item>
      <title>BikePed Portal: Pedestrian Volume Estimation Based on Push Button Actuations from Signals Data</title>
      <link>https://rip.trb.org/View/2361978</link>
      <description><![CDATA[This project translates research from Oregon DOT's "Active transportation counts from existing on-street signal and detection infrastructure" (SPR 857), into a practical application on BikePed Portal. ]]></description>
      <pubDate>Tue, 02 Apr 2024 13:42:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2361978</guid>
    </item>
    <item>
      <title>The Active Transportation Resource Center Administration</title>
      <link>https://rip.trb.org/View/2350818</link>
      <description><![CDATA[This project will enhance and expand the existing Active Transportation Resource Center (ATRC), which is a training and resource hub for local agencies, municipal planning organizations, and similar groups to utilize in planning, developing, applying for, and implementing projects and programs that support active transportation in California. The ATRC was established under the California Department of Transportation’s Active Transportation Program. The project team will use the lessons derived from their research to improve the variety of services ATRC provides, such as the latest in research, training, consultation, and coordination services. In detail, this project will focus on two long-term objectives: (1) Given the growing competitiveness in the Active Transportation Program funding process, the team will expand the reach of the ATRC to provide broad support for active transportation projects needing guidance in seeking alternative sources of funding. (2) The team will strengthen the ATRC’s ties to active transportation research by ensuring the ATRC functions as the primary portal for practitioners to access research, seeking new avenues of research, and looking for partnership opportunities.]]></description>
      <pubDate>Wed, 13 Mar 2024 17:48:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2350818</guid>
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