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    <title>Research in Progress (RIP)</title>
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    <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>
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      <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>Enhancing Transportation Safety for At-Risk Road Users through Behavioral Monitoring and Smart Infrastructure</title>
      <link>https://rip.trb.org/View/2563978</link>
      <description><![CDATA[Improving roadway safety for at-risk road users, specifically pedestrians, requires proactive strategies grounded in both technology and behavior science. This project focuses on enhancing transportation safety for at-risk road users, specifically pedestrians, by addressing distraction-related behaviors in high-risk environments such as crosswalks and intersections where user inattention or distraction commonly results in close calls or injuries. With mobile device usage increasingly contributing to inattention, the initiative seeks to proactively reduce incidents through a combination of behavioral, technological, educational, and policy-focused strategies. The research team will design and test a suite of integrated tools, ranging from mobile technology to educational outreach, aimed at improving situational awareness, reducing risky behaviors, and informing local safety strategies. Central to this effort is a smartphone application that delivers real-time alerts, via both phones and smartwatches, prompting pedestrians to stay attentive while crossing streets. A complementary behavioral study will use wearable devices and real-time diary entries to examine how social interactions and group dynamics influence pedestrian attention. These findings will inform targeted nudges and feedback to reduce distraction and improve coordination in group settings. To support long-term impact, policy recommendations will be developed through a comprehensive review of existing regulations and direct input from key stakeholders, including planners, law enforcement, and transportation safety experts. Surveys and interviews will help shape an enforceable framework for managing pedestrian distraction in high-risk areas. Educational efforts will engage students in hands-on research, data collection, and community outreach, building technical skills while increasing public awareness around distracted walking. Together, these efforts offer a proactive and scalable approach to improving the safety of at-risk road users in urban environments]]></description>
      <pubDate>Mon, 16 Jun 2025 15:04:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/2563978</guid>
    </item>
    <item>
      <title>A Cost-Efficient Digital Twin Approach Using Pan-Tilt-Zoom Cameras to Enhance Urban Mobility Situational Awareness</title>
      <link>https://rip.trb.org/View/2459070</link>
      <description><![CDATA[Pan-tilt-zoom (PTZ) cameras are widely deployed across U.S. cities to support Traffic Management Centers (TMCs) in real-time traffic monitoring and rapid response to incidents. Since 2009, the New York State Department of Transportation (NYSDOT)'s 511NY program has utilized over 1,700 PTZ cameras statewide, mainly at key intersections, for 360-degree coverage. This research leverages the extensive PTZ network in a three-stage approach to enhance urban mobility situational awareness. First, the project will employ cooperative control and spatio-temporal prediction methods to enable real-time, network-wide traffic monitoring. Next, it will integrate these controls with the SUMO traffic simulator to create an Urban Mobility Digital Twin (UMDT) for improved situational awareness. Finally, the project will validate the PTZ control scheme and UMDT using AIWaysion-provided data and devices. The UMDT will synthesize driver-centric information, detect safety and mobility risks, and support proactive decision-making in transportation management. Expected outcomes include enhanced insights and capabilities for states and communities using PTZ cameras.]]></description>
      <pubDate>Thu, 21 Nov 2024 17:02:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2459070</guid>
    </item>
    <item>
      <title>Investigation of Driver Adaptations in a Mixed Traffic Environment</title>
      <link>https://rip.trb.org/View/2341499</link>
      <description><![CDATA[Existing mathematical models for car-following are mostly descriptive and do not inherently estimate behavioral responses due to different traffic conditions, such as changes in roadway, environment, or vehicle conditions. These models include behavioral parameters (e.g., reaction time, degree of aggressiveness, etc.), which are calibrated with aggregate data collected under various traffic conditions. However, these models fail to capture changes in driver behavior caused by changes in the driving environment and thus fail to address vehicle interactions and the mechanisms that lead to breakdown phenomena.

This issue becomes more apparent with the emergence of vehicle automation and advanced vehicle technologies as these directly impact the driving task. Through automation, drivers do not have immediate or direct control of their speed; therefore, task demand is expected to be different. Since speed modifications are expected to be slower with automation, it may be challenging to control driver’s workload level. Automation constrains driver capability through slower reaction times, information-processing capacity and speed. Driver’s activation (arousal) level is diminished, and drivers are more prone to be distracted. As we transition to partially automated or fully automated systems, the development of models that incorporate explanatory psychological constructs will be crucial. 

This project builds on the research team's previous work (Modeling Driver Behavior and Driver Aggressiveness Using Biobehavioral Methods – PART I, II, and III) where the team developed an extension to the Intelligent Driver Model (IDM) for manual driving, which captures three cognitive parameters: workload, situation awareness, and level of activation. The objective for this research project is to assess car-following behavioral changes of these cognitive parameters due to vehicle automation and build a framework for capturing these changes in a car-following model (e.g., the IDM). ]]></description>
      <pubDate>Sat, 17 Feb 2024 16:29:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/2341499</guid>
    </item>
    <item>
      <title>Pedestrian Auditory Situational Awareness: Tesseract Crosswalk Module</title>
      <link>https://rip.trb.org/View/2244355</link>
      <description><![CDATA[Driver and environmental factors, perceptual limitations, and distractions typically dictate how vulnerable pedestrians are while crossing the street at signalized or unsignalized crosswalks. The series of research supported through Center for Advanced Transportation Mobility (CATM) funding seeks to systematically investigate critical factors associated with unsafe crosswalk activities. First, naturalistic observations of pedestrians performing street crossing on a rural higher education campus informed the develop of a 1:1 physical and virtual crosswalk testbed used to study pedestrian auditory situation awareness (ASA) with personal listening devices (PLD). Thereafter, the investigation of intervention technologies for low-vision pedestrians is ongoing to better understand how to design human-machine interfaces for implementing appropriate countermeasures to reduce pedestrian distractions at crosswalks. Lastly, a mobile testbed is to be developed for remote education and training of safe street crossings.]]></description>
      <pubDate>Wed, 13 Sep 2023 13:11:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/2244355</guid>
    </item>
    <item>
      <title>Robust Automatic Detection of Traffic Activity from Vehicle Perspectives</title>
      <link>https://rip.trb.org/View/2087442</link>
      <description><![CDATA[The accurate detection and prediction of actions by multiple traffic participants such as pedestrians, vehicles, cyclists and others is a critical prerequisite for enabling self driving vehicles to make autonomous decisions. Current approaches  to teach an autonomous vehicle how to drive use reinforcement learning which is essentially relies on already collected situations as examples relying purely on visual similarity without any understanding of the semantics of the situation and therefore no ability to reason about other similar situations that may have different appearance. This can be overcome by methods that provide situation awareness to the vehicle. The idea is to enable semantically meaningful representations of road scenarios which include the physical layout of the scene, the various participants prior and current activities. The ability to abstract this semantic representation and apply it to multiple scenes that are conceptually similar allows much more robust decision-making strategies by autonomous vehicles. Essentially this allows endowing autonomous vehicles with a reasoning process.]]></description>
      <pubDate>Wed, 21 Dec 2022 12:07:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2087442</guid>
    </item>
    <item>
      <title>Temporal Components of Warnings and Notifications for Safe Manual Re-engagement with the driving task in Automated Driving</title>
      <link>https://rip.trb.org/View/2062779</link>
      <description><![CDATA[Prior research with vehicle warnings has examined various aspects of warning designs and how they impacted driver reaction times.  Further, there is prior research that explored and modeled how and at what rate drivers lose information relevant to the driving task when they remove their attention and how and at what rate they regain information upon re-engagement relevant to the situational awareness. The current project intends to build off of this foundation by further examining temporal components of warnings and manual re-engagement requests by the system (such as in SAE Level 3 Automated Driving System (ADS) equipped vehicles) with an intent to gain insights on how long does it take to build sufficient situational awareness to safely resume full manual driving depending on different types and durations of disengagement.  This project will include a review of research to identify how driving automation might impact reaction times to warnings. This project will supplement this review with an empirical study of how the temporal design of warnings will impact safety outcomes for re-engagement in these circumstances.]]></description>
      <pubDate>Thu, 17 Nov 2022 11:56:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2062779</guid>
    </item>
    <item>
      <title>Methods to Transfer System Knowledge to Driver/ Operators to Enhance and/or Accelerate Situation Awareness During Handoff</title>
      <link>https://rip.trb.org/View/2050287</link>
      <description><![CDATA[In situations when automated driving systems (ADS) break down, and control needs to be either handed over to the driver or when the minimal risk condition must be automatically achieved, the ADS sensors and algorithms were aware of things (hazards, potential hazards, conditions, etc.) that could or should be communicated to the driver/occupant. Research in this area would include determining the kinds of information drivers/operators/ADSs may need or find useful regarding other road user behavior, different approaches to presenting road user intention, and evaluating these approaches and determining through industry collaboration which are potentially most effective.]]></description>
      <pubDate>Tue, 25 Oct 2022 10:24:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2050287</guid>
    </item>
    <item>
      <title>Development of a Monitoring System for Driver Readiness in Prolonged Automated Driving</title>
      <link>https://rip.trb.org/View/2008004</link>
      <description><![CDATA[Vehicle automation technology is being designed to handle driving tasks for human drivers. However, this technology is not expected to handle all possible driving conditions successfully in the foreseeable future. The system can fail anytime and may require drivers to take over control within a short period 
of time. Additionally, automation can induce boredom, daydreaming, and drowsiness due to driver inactivity and can worsen driver readiness to take over control of the vehicle. Driver readiness can be measured using their postural data, gaze behaviors, and emotional expressions. Specific thresholds for these measures can be used to alert drivers to be ready to take over from automated driving or avert them from driving, when necessary. This proposal aims to develop a driver readiness monitoring system to improve their takeover performance using a driving simulator study. The objectives are to identify effective measures to define driver readiness and assess the association between divers’ takeover performance and their readiness measures. Researchers will measure (i) driver readiness using postural data, gaze and head orientations, and emotional status from video recordings analyzed with Face Reader software; (ii) drivers’ categorical subjective responses on readiness; and (iii) takeover 
performance using reaction time, collision rate, and driving behavior. Different machine learning algorithms will be applied for (i) feature extraction, (ii) feature selection, and (iii) developing classification and prediction models for readiness monitoring. The rationales for this research are to: (a) inform policy makers about an effective driver-readiness monitoring system for prolonged automated driving; (b) provide safer driving conditions and address equity during prolonged driving for older adults, and occupational drivers (Uber, taxi, or city transportation) driving long hours; and (c) enhance educational and research infrastructures combining human-computer interactions and machine learning. Stakeholder involvement from the city, state, and automobile manufacturer will strengthen our tech-transfer and project outcome dissemination.
]]></description>
      <pubDate>Tue, 16 Aug 2022 18:15:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/2008004</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>Impact of Rail Trespasser Fatalities &amp; Suicide on Mental Health and Safety Culture of Rail Transportation Workers</title>
      <link>https://rip.trb.org/View/1982054</link>
      <description><![CDATA[Trespasser fatalities and railroad suicide are significant concerns of the rail industry and the Federal Railroad Administration. Approximately 289 individuals commit suicide each year on the U.S. rail system accounting for almost one third of the fatalities related to railroad operations in the U.S. Railroad carriers alone cannot solve this tragic loss of life. The proposed project will assess the impact of exposure to trespasser fatalities and suicide on the mental health, safety and perceived safety culture of railroad workers and related first responders, law enforcement, and others. Several measures designed to assess compassion fatigue, secondary trauma, awareness of common myths associated with suicide and corporate safety culture will be administered to a sample of railroad workers and other key first line responders who will be attending railroad safety briefings and a rail suicide awareness program. The proposed research is expected to benefit railroad employees and related personnel by documenting the impact of railroad trespasser suicide and fatalities on perceived safety culture and lead to creating a checklist for identifying the effects associated with these events. The project will also promote awareness of effects and possible prevention techniques for responding to these events.]]></description>
      <pubDate>Wed, 15 Jun 2022 15:42:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/1982054</guid>
    </item>
    <item>
      <title>Transportation Safety Training in Rural Areas: An Exploration of Virtual Reality and Driving Simulation in Driver Response and Awareness</title>
      <link>https://rip.trb.org/View/1762369</link>
      <description><![CDATA[This project determines the effectiveness of utilizing simulated environments in form of virtual reality applications and console driving simulators as technologies that can be used to improve transportation safety in rural areas. The fatality rate associated with transportation accidents are significantly higher in rural areas (55%) given a national collective population of only 19%. This discrepancy means that little work has been done to address these safety issues with significant challenges arising from the investment cost spurred by low population densities. This project aims at addressing a part of this challenge which costs hundreds of millions of dollars in impact. The key element of the project is to address safety training around events that are high frequency while being irregular making them difficult to predict and thus challenging to provide training.]]></description>
      <pubDate>Thu, 07 Jan 2021 14:15:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/1762369</guid>
    </item>
    <item>
      <title> Creating a Situation-aware Sensing Environment for Cyclists: An Innovative and Cost-effective Smartphone-based Approach</title>
      <link>https://rip.trb.org/View/1756017</link>
      <description><![CDATA[This research will assess the feasibility and effectiveness of a Biker Assistance System (BAS) in different roadway contexts using a prototype mobile application. The application would make use of smartphones’ onboard speaker and microphones to monitor potential hazards and help bicyclists avoid crashes. The application will detect potential hazards by emitting an imperceptible sound and interpreting its reverberations, thus becoming a “mini-sonar system.” When certain potential hazards are detected, the smartphone will alert bicyclists of the hazard. This new approach to preventing bicycle crashes has yet to be developed or tested to the researchers’ knowledge. 
This project has four components. First, the project team proposes to analyze existing crash data sources to understand the types of crashes that can be prevented or mitigated with BAS. Second, the team proposes the development of the BAS for at least two hazardous scenarios – right turning vehicle detection and front/overtaking vehicle nearing. Additional scenarios may be added based on the crash data assessment. Third, a bike simulator study will be conducted to determine effective alerts for selected hazards. Based on the simulator study outcomes, a list of multi-modular alerts will be recommended which can be easily understood and interpreted by cyclists under both day and night lights. These alerts will be included in the BAS prototype. Finally, the project team proposes testing the efficacy of BAS in these scenarios via physical testing and naturalistic observation using an instrumented bicycle. This naturalistic database will be used to identify the critical cyclists-vehicle interaction regions and scenarios. Future research will expand the sensing capacity to function in different crash scenarios, investigate cyclists’ interactions with different road users, and provide cyclists with feedback to avoid different types of on-road hazards.
]]></description>
      <pubDate>Sat, 05 Dec 2020 18:16:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/1756017</guid>
    </item>
    <item>
      <title>Maintaining Situational Awareness when Operating Automated Vehicles: Findings from Other Modes</title>
      <link>https://rip.trb.org/View/1747530</link>
      <description><![CDATA[This project will produce a report in which each chapter contains a comprehensive systematic literature review that summarizes, evaluates, and synthesizes research published since 1975 on topics related to maintaining operator engagement in automated systems; primarily transportation systems. The primary objective of this project is to understand the current state of knowledge
regarding driver/operator engagement with motor vehicles within the context of other
highly automated systems in other transportation modes, such as rail and aviation. The
planned activities will include a synthesis of the existing research literature regarding
methods to enhance driver/operator knowledge of and engagement with automated
systems with an emphasis on research that evaluates the effectiveness of various
approaches for helping maintain situational awareness. The synthesis will include
research on engagement across a variety of transportation modes and potentially other
automated systems (e.g., power plants). The synthesis will identify technologies
currently being used as well as near-term solutions. This work will help identify research
gaps and needs from the behavioral perspective, and it will provide interested
stakeholders with new and innovative ideas from other transportation modes.]]></description>
      <pubDate>Wed, 28 Oct 2020 16:17:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/1747530</guid>
    </item>
    <item>
      <title>SPR-4522: Design of Educational Material &amp; Public Awareness Campaigns for Improving Work Zone Driver Safety</title>
      <link>https://rip.trb.org/View/1727172</link>
      <description><![CDATA[The overall goal of this project is to improve work zone driver safety in Indiana through driver education and public awareness campaigns. This project will build on the findings of the public opinion survey under SPR 4441 and inputs from Indiana Department of Transportation (INDOT) Statewide Safety and INDOT’s back of queue Task Force. The project goal is broken down into two specific objectives: (1) prepare educational materials to be incorporated into driver’s education or training curriculum prior to taking driving test and getting a driver’s license issued, and (2) design a public awareness campaign.]]></description>
      <pubDate>Thu, 06 Aug 2020 11:14:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/1727172</guid>
    </item>
    <item>
      <title>Acoustic Situation Awareness and Its Effects on Pedestrian Safety within a Virtual Environment</title>
      <link>https://rip.trb.org/View/1706841</link>
      <description><![CDATA[A major component of the U.S. Department of Transportation’s (DOT) mission is to focus on pedestrian populations and how to enable safe and efficient mobility for vulnerable road users. However, evidence states that college students have the highest rate of pedestrian accidents. Due to the excessive use of personal listening devices (PLDs), vulnerable road users have begun subjecting themselves to reduced levels of achievable situation awareness resulting in risky street crossings. The ability to be aware of one’s environment is critical during task performance; however, the desire to be self-entertained should not interfere or reduce one’s ability to be situationally aware. The current research seeks to investigate the effects of acoustic situation awareness and the use of PLDs on pedestrian safety by allowing pedestrians to make “safe” vs. “unsafe” street crossing within a simulated virtual environment. The outcomes of the current research will (1) provide information about on-campus vehicle and pedestrian behaviors, (2) provide evidence about the effects of reduced acoustic situation awareness due to the use of personal listening devices, and (3) provide evidence for the utilization of vehicle-to-pedestrian alert systems.]]></description>
      <pubDate>Fri, 15 May 2020 17:12:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/1706841</guid>
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