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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>
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      <title>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Applying Telematics Data to Support Traffic Enforcement</title>
      <link>https://rip.trb.org/View/2720301</link>
      <description><![CDATA[Telematics is increasingly used to help traffic enforcement shift toward data-driven, risk-based, and preventive safety interventions. Telematics systems collect data such as vehicle speed, acceleration, braking, cornering, location, time of day, and sometimes phone distraction indicators. In a traffic enforcement context, this data can support hotspot identification and targeted enforcement deployment. Relevant traffic enforcement applications include: 1. Speed management - telematics can reveal where speeding is routine, not just where crashes have already occurred. This is especially useful on arterials, rural roads, school zones, and work zones. 2. Distracted driving risk - mobile-device telematics can indicate patterns of phone interaction while driving. This is better suited for network-level risk mapping than individual citation issuance. 3. Commercial fleet compliance - fleet telematics can support internal safety management, identify repeat risky driving behavior, and guide employer-based interventions. 4. Work-zone safety - telematics can help detect excessive speeds near work zones and support placement of speed safety cameras or police presence.

The Governors Highway Safety Association has recently promoted a shift toward using anonymized, aggregate telematics insights to identify risky conditions before crashes occur, such as repeated high-speed driving through school zones or distraction on rural roads.

Research is needed to develop a better understanding of the application of telematics data to support traffic enforcement.

OBJECTIVES: The objectives of this research are to: Document the existing state of knowledge regarding the application of telematics data to support traffic enforcement; Assess pilot projects underway in various states; Assess primary issues and unintended consequences, and propose measures states can take to manage risks; Develop a guide for state highway safety and other state agencies indicating (1) how they can use this technology to promote traffic safety and (2) recommendations for standardizing the use of telematics data to meet user needs; Propose a study design for use in potential future BTSCRP research to address knowledge gaps.]]></description>
      <pubDate>Thu, 02 Jul 2026 20:00:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2720301</guid>
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    <item>
      <title>Exploring AI-Driven Approaches to Quantify and Mitigate Driver Distraction</title>
      <link>https://rip.trb.org/View/2655701</link>
      <description><![CDATA[As automated systems and assistive driver features continue to advance, driver distraction is increasing in both manual and semi-autonomous modes. Automated vehicles (AVs) offer promising technology solutions to reduce distraction risk. However, automation can create out-of-the-loop periods, encouraging non-driving tasks and masking gradual disengagement. Current AVs are semi-autonomous, so drivers must be ready to retake control for issues like missing lane markers. The takeover process is cognitively and physically demanding, unfolding within seconds, which is especially challenging when drivers are distracted. Traditionally, distractions in vehicles have mainly been due to phone use or infotainment systems. New technologies have expanded the sources of distraction. These distractions include visual distractions where drivers’ eyes are diverted from the road (e.g., using a GPS), manual distractions where drivers remove their hands from the wheel (e.g., using a phone or eating while driving), and cognitive distractions where a driver’s mind is not focused on driving (e.g., mind wandering). Emotions and fatigue that pull attention from driving also reduce engagement. Social pressures and smartphone habits further contribute to distraction. This emphasizes the need to broaden the definition of distraction to include driver engagement levels to better reflect real-time mental states and driving performance.
Given the many types of distractions, there is a need to systematically quantify their effects using both driving and physiological measures acquired by various sensors under a more universal paradigm (e.g., engagement level). With the rapid advancements in artificial intelligence (AI), particularly in the domain of deep learning, sophisticated multimodal models have emerged as powerful tools for integrating diverse data sources. These models offer an unparalleled ability to combine acquired features from various sensors into a cohesive understanding of driver states. Leveraging such techniques enables us to capture nuanced indicators of distraction, which can ultimately enhance the system’s ability to predict and respond to varying levels of engagement. The research team proposes using federated learning with multimodal sensor fusion, where each sensor model is trained locally on-device to maintain privacy and adapt to specific driving conditions. Federated learning enables these models to share knowledge without centralizing data, creating a global model that can recognize diverse distractions across different driving contexts.
Therefore, the goals of this project have two parts: (1) Identify and quantify the effects of traditional and emerging distractions, and (2) Build an AI-driven framework that categorizes and quantifies all types of driver distractions based on multisensory data. Two phases of studies will be conducted to achieve the two goals, respectively. Phase I will identify and quantify both traditional and emerging forms of driver distraction. Specifically, the team will first conduct a national survey to better assess sentiments, attitudes, and key themes regarding driver distraction as well as correlations and trends among demographics, distraction types, and self-reported behaviors. With the knowledge gained, the team will then quantify the effects of distractions on driving performance and
physiological measurements. The team will categorize types of distractions based on established classifications such as visual, manual, and cognitive distractions, as well as emerging types identified in the survey. Then the team will conduct an in-lab experiment using a high-resolution driving simulator. Participants’ driving and physiological measures. All data collected herein will then be used in Phase II, the development of an AI-driven framework.
Phase II will develop an AI-driven framework to categorize distraction types using
multisensory data. The team will integrate three sensors, eye tracking, a depth sensor, and face video, to enable a complete view of distraction. Multimodal models will fuse these signals to improve accuracy and robustness. The team will aggregate insights from each modality to achieve a more reliable categorization of distraction types. Federated learning will build a global model while keeping data on the device, which supports privacy and adaptation to different platforms and roads. The global model will serve as the core engine in Phase II, classifying distraction types based on combined sensory inputs. This layered AI approach will strengthen the understanding of driver distraction and support real-time interventions.]]></description>
      <pubDate>Mon, 19 Jan 2026 16:14:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655701</guid>
    </item>
    <item>
      <title>Explanatory Sequential Mixed Methods Study: Integrating Surveys and Semi-Structured Interviews to Explore Cell Phone Use While Driving and Emerging Technologies for Behavior Mitigation</title>
      <link>https://rip.trb.org/View/2472696</link>
      <description><![CDATA[Distracted driving, predominantly due to cell phone use, remains a critical road safety issue, causing thousands of fatalities annually in the United States. This project investigates the patterns and contexts of cell phone use while driving and evaluates the acceptance and effectiveness of emerging preventive technologies, such as texting prevention apps and device-based solutions. Using a sequential mixed methods approach, the study will analyze survey data from Massachusetts drivers and conduct interviews with both users and developers to explore attitudes and behaviors related to these technologies. The findings aim to inform evidence-based interventions and policy recommendations, advancing road safety through innovative, user-centered solutions.]]></description>
      <pubDate>Mon, 09 Dec 2024 10:08:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2472696</guid>
    </item>
    <item>
      <title>Telematics Technology and Distracted Driving </title>
      <link>https://rip.trb.org/View/2447243</link>
      <description><![CDATA[The current project aims to develop an understanding of how smartphone-based telematics technologies can be used to measure information on individual driver behavior and performance in general and in terms of distracted driving.  This study will help the 
National Highway Traffic Safety Administration (NHTSA) understand the limitations of this kind of data for research data collection in comparison to other kinds of data collection such as vehicle-based systems.  This study will also look at age and sex differences in a group of drivers in terms of distracted driving and other risky driving behaviors, with a particular view of how the data collection systems differ.  This project will involve a literature review and scan of technologies that can be used for studying data collection of driver behavior and distracted driving engagement.  Next, the project will focus on different forms of data collection, e.g., focusing on NDS DAS and smartphone-based telematic apps in terms of accuracy at providing information, resources required for utilizing each technology, and ease of use and distribution (Study 1). Last, the project will involve data collection and analyses using these kinds of technologies in terms of general driving and distracted driving engagement according to the identified forms of technology (Study 1) and according to participant sex and age, which will be analyzed using the current project’s data sets (Study 2).  The intent of this project is to create two (2) final reports, one for each study, a journal article, and data sets from each study and the raw data sets that can be analyzed in the future. This project will support NHTSA’s efforts to study distracted driving engagement, giving the agency information regarding what measurement techniques are effective for different forms of distracted driving, and behavior, in general, as well as providing the agency with prevalence estimates for distracted driving engagement.

]]></description>
      <pubDate>Fri, 01 Nov 2024 12:24:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2447243</guid>
    </item>
    <item>
      <title>Evaluating the Impacts of Age and Health on Different Aspects of Mobility and Driving Behaviors</title>
      <link>https://rip.trb.org/View/2325917</link>
      <description><![CDATA[This research project conducted by the Massachusetts Institute of Technology (MIT) AgeLab is set against the backdrop of an aging American population, with an increasing emphasis on maintaining driving ability and mobility in older age. This study aims to explore two main questions: the dynamics surrounding older adults' decisions to retire from driving and their subsequent mobility transitions, and how older adults' self-reported driving behaviors are influenced by their age, health, and access to vehicle technologies. Utilizing existing data sets, including a nationally representative survey on driving habits and the MIT AgeLab’s Lifestyle Leaders Panel data from individuals aged 85 or older, the study will analyze patterns of self-regulation in driving, the use of advanced vehicle technologies, and the effects of health and age on driving behaviors including distracted driving. The project also plans to integrate findings from annual surveys on attitudes towards automation, encompassing a variety of transportation modes and technologies. Key goals include generating insights for public outreach materials, enhancing support for older adults in driving retirement decisions, and contributing to academic and professional discourse through conferences and peer-reviewed publications. Involving young researchers and undergraduate interns, this study aims to deepen our understanding of mobility and driving behavior among older adults and to inform interventions for safer and more supportive transportation options for this demographic.]]></description>
      <pubDate>Tue, 23 Jan 2024 14:11:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2325917</guid>
    </item>
    <item>
      <title>Evaluating Older Drivers’ Reaction to Forward Collision Warning (FCW) Systems Under Conditions of Distraction Using a Driving Simulator</title>
      <link>https://rip.trb.org/View/2321728</link>
      <description><![CDATA[This research aims to evaluate older drivers’ responses to Forward Collision Warning (FCW) and Automatic Emergency Braking (AEB) systems under visual distraction, with the goal of enhancing road safety and supporting broader adoption of driver assistance technologies among aging populations. The primary objective is to assess how these systems affect the driving performance of individuals aged 65 and older when distracted, using a high-fidelity driving simulator. In addition, the study explores older drivers’ perceptions, trust, and attitudes toward FCW and AEB technologies to identify barriers and opportunities for increased user acceptance. The project is divided into two phases. Phase 1, documented in this report, involved a literature review, development of experimental protocols, questionnaire design, Institutional Review Board (IRB) approval, and creation of simulator scenarios. The findings and materials developed in Phase 1 provide the foundation for Phase 2, which will involve participant recruitment, data collection, and behavioral analysis.]]></description>
      <pubDate>Tue, 16 Jan 2024 12:24:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2321728</guid>
    </item>
    <item>
      <title>Office of Behavioral Safety Research Topic Area Meetings: Older Drivers, Novice Drivers, Distracted Drivers, &amp; Seat Belt Use</title>
      <link>https://rip.trb.org/View/2256372</link>
      <description><![CDATA[The objective of this project is to plan and hold four (4) one-day meetings, one for each of the following topic areas: Older Adults’ Mobility, Novice Driver Safety, Distracted Driving, and Seat Belt Use. Meeting participants will present findings from recently concluded projects, ongoing research activities, and planned projects related to each of the relevant topic areas. The meetings will be held around the time of, and near the site of, the Transportation Research Board (TRB) Annual Meeting to facilitate attendance of those attending TRB. Following the meetings, four (4) Meeting Summary Reports summarizing the presentations and discussions will be developed and distributed.]]></description>
      <pubDate>Thu, 28 Sep 2023 13:30:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/2256372</guid>
    </item>
    <item>
      <title>Reducing Adverse Driving Behaviors in Work Zones: Strategies and Guidelines</title>
      <link>https://rip.trb.org/View/2219015</link>
      <description><![CDATA[In 2020, 857 fatalities and 44,000 injuries occurred in work zones across the country. When adverse driving behaviors (excessive speeding, tailgating, aggressive driving, distracted driving, confusion, etc.) occur in work zones, their consequences are often magnified due to restricted geometrics and unexpected changes in operating conditions or travel patterns in and around those zones. Mitigating such behaviors requires a broad range of engineering, education, and enforcement strategies tailored to the unique operating characteristics in work zones. 

There is an urgent need for research to evaluate existing strategies and identify innovative strategies that are both effective and scalable to reduce adverse driving behaviors in work zones.

The objective of this project is to develop guidelines and decision-making tools to enhance road safety in work zones by addressing adverse driving behaviors.]]></description>
      <pubDate>Tue, 25 Jul 2023 07:58:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2219015</guid>
    </item>
    <item>
      <title>Teen Driving Performance Associated with Distraction, ADHD, and Other Risk Factors



</title>
      <link>https://rip.trb.org/View/1996244</link>
      <description><![CDATA[BTSCRP Research Report 15: provides insights into traffic safety risks for teen drivers with different levels of attention-deficit/hyperactivity disorder (ADHD) screen scores. The report uses naturalistic driving data to assess the incidence of eyes off road (EOR) and crash risk for teen drivers with and without ADHD. The report concludes that teen drivers with ADHD tend to look away from the road more frequently and may be at an elevated risk for missed hazards. This report will be of interest to state highway safety offices (SHSOs) and other stakeholders concerned with young-driver safety.

Young drivers with neurodevelopmental disabilities may be at more risk for motor vehicle crashes due to behavior characteristics commonly associated with these conditions. In recent years, a growing body of research has examined driving risks for teens with autism and those with ADHD. Research has identified concerns about the driving skills of teenagers with ADHD, as well as their increased tendencies to become distracted while driving and to drive at higher speeds. Determining the role of distracted driving in crashes is difficult and inexact for many reasons, including a general lack of evidence. 

Under BTSCRP Project BTS-28, “Teen Driving Performance Associated with Distraction, ADHD, and Other Risk Factors,” Virginia Polytechnic Institute and State University was asked to (1) gauge the association between confirmed instances of distracting behaviors and inattention to the driving task by teen drivers with crash and near-crash (CNC) involvement, in relation to their incidence during baseline events; (2) determine whether these instances contribute to CNCs, and if and how these relationships change with increasing driving experience; and (3) compare exposure-based CNC involvement rates, and self-reported risky driving behaviors, for teen drivers with different levels of ADHD screen scores, taking into account the potential influence of other behavioral and demographic factors captured in naturalistic driving study (NDS) data. 

]]></description>
      <pubDate>Tue, 19 Jul 2022 12:47:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/1996244</guid>
    </item>
    <item>
      <title>Improving Commercial Vehicle Safety through University Partnerships with a Focus on Distracted Driving and Work Zone Safety</title>
      <link>https://rip.trb.org/View/1987582</link>
      <description><![CDATA[Working with the Federal Motor Carrier Safety Administration (FMCSA) in a cooperative agreement, the overall goal of this project is to improve commercial vehicle safety through sharing research and best practices in the areas of distracted driving and safety in work zones. This will be accomplished through two strategies. The first strategy will be to document and share best practices of university partnerships with state law enforcement and driver licensing agencies to improve commercial vehicle safety related to distracted driving and safety in work zones throughout the Western Service Center region. The second strategy will be to provide resources through the Commercial Vehicle Safety Center at NDSU-UGPTI as a point of contact for universities, law enforcement, and driver licensing agencies seeking assistance to establish partnerships to improve commercial vehicle safety, in particular in the areas of distracted driving and safety in work zones.]]></description>
      <pubDate>Fri, 01 Jul 2022 13:05:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/1987582</guid>
    </item>
    <item>
      <title>Objectives, Components, and Measures of Effective Traffic Safety Public Awareness and Education Efforts







</title>
      <link>https://rip.trb.org/View/1716998</link>
      <description><![CDATA[BTSCRP Research Report 14: Evaluating Traffic Safety Campaigns: A Guide provides insights into current practices for measuring the effectiveness of behavioral-based traffic safety campaigns. It also presents a framework for evaluating traffic safety campaigns, with the goal of designing and conducting future campaigns to more effectively promote safer road user behaviors. This report will be of interest to state highway safety offices (SHSOs) and other stakeholders concerned with understanding the effectiveness of traffic safety campaigns and associated outcomes. 

Most states have engaged in some sort of behavioral-based traffic safety programs using education and enforcement to change road user behavior. Well-known examples include NHTSA’s Click It or Ticket and Drive Sober or Get Pulled Over campaigns. With law enforcement agencies across the nation now facing resource challenges, many states are experiencing less participation in these types of campaigns.

Some states have launched new behavioral-based traffic safety campaigns focused more on public awareness, education, and individual responsibility. These campaigns can present some evidence of effectiveness, but such effects are often limited to communication metrics (e.g., number of impressions) rather than behavioral outcomes. A better understanding of how to measure the effectiveness of such campaigns would help create successful and sustainable initiatives. 

Under BTSCRP Project BTS-18, “Objectives, Components, and Measures of Effective Traffic Safety Public Awareness and Education Efforts,” Virginia Polytechnic Institute and State University was asked to (1) identify current practices used by SHSOs and other entities to evaluate the effectiveness of traffic safety campaigns and associated outcomes, and (2) develop a practical and scalable framework for evaluating how to engage road users, through traffic safety campaigns, to change behavior and improve safety performance. The focus of this research was public awareness and education campaigns regarding nonenforcement traffic safety. 

Appendix A, Costing Tool, and an evaluation matrix are supplemental products to BTSCRP Research Report 14. These products can be found on the National Academies Press website (nap.nationalacademies.org) by searching for BTSCRP Research Report 14: Evaluating Traffic Safety Campaigns: A Guide. 

BTSCRP Web-Only Document 7 is a companion to BTSCRP Research Report 14. The web-only document describes the research methodology and can be found on the National Academies Press website (nap.nationalacademies.org) by searching for BTSCRP Web-Only Document 7: Objectives, Components, and Measures of Effective Traffic Safety Public Awareness and Education Efforts.]]></description>
      <pubDate>Tue, 30 Jun 2020 12:06:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/1716998</guid>
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