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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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    <item>
      <title>Audible Warning System on TMA and Maintenance Trucks</title>
      <link>https://rip.trb.org/View/2675924</link>
      <description><![CDATA[The project aims to increase the safety of truck-mounted attenuators by developing a system that uses automated audible and visual warnings to detect potential collisions and notify drivers. Researchers will optimize the design of audible warning devices based on physical, engineering and human factors to increase performance in complex operating environments. The research will help the Illinois Department of Transportation build and test prototypes in real-world conditions, especially around work zones, helping to reduce collisions, damage to equipment and traffic disruptions.]]></description>
      <pubDate>Mon, 02 Mar 2026 11:25:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2675924</guid>
    </item>
    <item>
      <title>Impact of Secondary Red Warning Lights on Incident Response Time – Phase 2 </title>
      <link>https://rip.trb.org/View/2669547</link>
      <description><![CDATA[In 2023, the Virginia Department of Transportation (VDOT) gained approval to install flashing red secondary warning lights on certain incident management coordinator (IMC) and safety service patrol (SSP) vehicles over a 2-year transition period. Previously, only amber lights were permitted. Although this does not constitute full emergency vehicle permissions (such as the ability to violate red lights on traffic signals), the flashing red lights may encourage motorists to pull to the shoulder. This may improve VDOT’s incident response during congestion. As the Red Lights Pilot Program started in November 2025, this study aims is to evaluate the effects of IMC red secondary warning lights on incident response, with an emphasis on changes in incident response time and clearance time. The findings will help VDOT make informed decisions on incident management strategies and investment.]]></description>
      <pubDate>Thu, 12 Feb 2026 10:50:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669547</guid>
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    <item>
      <title>Investigation of Wrong Way Pavement Alert Devices 
</title>
      <link>https://rip.trb.org/View/2319926</link>
      <description><![CDATA[In the State of Ohio between 2017-2021, there were a total of 426 wrong way crashes with 79 fatalities. In an effort to address wrong-way crashes, Ohio Department of Transportation (ODOT) has installed signalized systems at some locations with high volumes of these incidents. With safety being a top priority for ODOT there is a need for a low-cost, reliable system that can be installed on all exit ramps (including those in rural areas) that pose a safety threat for wrong way crashes. This new system would provide both a physical and visual warning to drivers that are entering the freeway in the wrong direction and add quicker response times from drivers once alerted to the issue. Currently ODOT is installing systems with cameras and flashing warning signs to notify drivers beyond the standard signage. Present systems are effective but are a very costly system and therefore is not the right solution for every roadway, especially low volume ramps. This would be a low-cost idea that could be used in all desired locations. They could be installed quickly and provide a physical alert to all drivers before they enter the interstates the wrong way. Some objectives for this project include, Identify/develop safe, reliable, and affordable methods to effectively alert wrong-way drivers, Demonstrate the performance of recommended solutions, as approved by ODOT, to validate safety, design, performance, overall effectiveness, and benefit/cost analysis and to Develop an implementation guide of tested solutions based on the best use-case scenarios for each recommendation.
]]></description>
      <pubDate>Mon, 08 Jan 2024 15:19:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2319926</guid>
    </item>
    <item>
      <title>Multimodal-AI based Roadway Hazard Identification and Warning using Onboard Smartphones with Cloud-based Fusion</title>
      <link>https://rip.trb.org/View/1923105</link>
      <description><![CDATA[Road hazard is one of the significant causes of fatality in road accidents. Accurate estimation of road hazards can ensure safety and enhance the driving experience. Existing methods of road condition monitoring are time-consuming, expensive, inefficient, require much human effort, and need to be regularly updated. There is a requirement for a flexible, cost-effective, and efficient process to detect road conditions, especially road hazards. In this study, we present a new method to deal with road hazards using smartphones. Since most of the population drives cars with smartphones onboard, we aim to leverage this
to detect road hazards in a more flexible, cost-effective, and efficient way. This study proposes a cloud based deep-learning road hazard detection model based on a Long-Short Term Memory network (LSTM) to detect different types of road hazards from motion data. To address the issue of large data requests for deep learning, this study proposes to fuse both simulation data and experimental data for the learning. The proposed approaches are validated by experimental tests, and the results demonstrate the accuracy of road hazard detection based on cloud-based fusion]]></description>
      <pubDate>Sun, 06 Mar 2022 15:14:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/1923105</guid>
    </item>
    <item>
      <title> Effective Bridge Deck Weather Warning Technologies </title>
      <link>https://rip.trb.org/View/1860934</link>
      <description><![CDATA[Weather related crashes continue to be a problem on Michigan roadways. Crashes caused by inclement weather are especially
problematic on bridges, where historically, MDOT has mounted seasonal warning signs at bridges to warn drivers of such
conditions. Recently, MDOT has begun installing new condition-responsive technology that uses environmental sensors(s) to
detect icy bridge decks and roadways that when the correct conditions are met, will light up flashing yellow or LED lights on a
nearby warning sign(s) to help the motoring public understand the upcoming road conditions. This research project will explore
more dynamic messaging strategies, such as displaying a warning message on a nearby Dynamic Message Sign (DMS) or
incorporating vehicle to infrastructure communication to improve driver’s behavior response to adverse weather conditions;
thereby hoping to reduce the number of these types of crashes]]></description>
      <pubDate>Mon, 21 Jun 2021 10:54:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/1860934</guid>
    </item>
    <item>
      <title>Work Zone Safety III: Calibration of Safety Notifications through Reinforcement Learning and Eye Tracking</title>
      <link>https://rip.trb.org/View/1844340</link>
      <description><![CDATA[Despite increased regulations, restrictive measures, and devices used for warnings, work zone injuries and fatalities are still observed at highway construction projects with alarms/notifications being ignored. With a vision to reduce the number of injuries and fatalities, Phase 3 of the research team's worker safety project extends the original scope and adds two new main components, including the addition of eye-tracking for identifying worker attention under dangerous situations and a reinforcement learning model used to optimally send alarms to workers to maximize their attentions along with wide deployment and demonstration of the team's previous C2Smart research effort. This phase of the project aims to make sense of the biometric sensor data (i.e., heart rate and pupil movements while workers omit or accept safety notifications) through state-of-the-art reinforcement learning approaches. The outcomes of this research will bring an understanding to the unknowns of worker behaviors on why they decide to ignore/accept notifications for calibration of when and at what frequency to send notifications to workers for a better acceptance rate. Key questions this research answers are, at what conditions workers ignore/response to warnings at work zones? How we can calibrate notification systems for getting responsive actions from workers? What are the modalities, frequencies, and timings of pushing notifications in these calibrated systems? Through wearable sensors, hardware integrated realistic representations of work zones in virtual reality and eye tracking, in this phase of the project the team will widely have pilot demonstrations of the integrated platform to collect worker behavioral and biometric (heart rate, eye-tracking) responses to alarms/warnings/notifications issued under realistic scenarios and modalities of warning mechanisms (e.g., sensory, visual, audial) that were developed in earlier phases of this project, and mine these captured data towards understanding human behaviors in response to modalities of notifications. ]]></description>
      <pubDate>Thu, 01 Apr 2021 20:02:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/1844340</guid>
    </item>
    <item>
      <title>Development of a Smartphone Application to Warn the Driver of Unintentional Lane Departure Using GPS Technology</title>
      <link>https://rip.trb.org/View/1678168</link>
      <description><![CDATA[Unintentional lane departure is a significant safety risk. Currently, available commercial lane departure warning systems use vision-based or GPS technology with lane-level resolution. These techniques have their own performance limitations in poor weather conditions. We have previously developed a lane departure detection (LDD) algorithm using standard GPS technology. Our algorithm acquires the trajectory of a moving vehicle in real-time from a standard GPS receiver and compares it with a road reference heading (RRH) to detect any potential lane departure. The necessary RRH is obtained from one or more past trajectories using our RRH generation algorithm. This approach has a significant limitation due to its dependency on past trajectories. To overcome this limitation, we have integrated Google routes in addition to past trajectories to extract the RRH of any given road. This advancement has been incorporated into a newly developed smartphone app, which now combines our previously developed LDD algorithm with the enhanced RRH generation algorithm. The app effectively detects lane departures and provides real-time audible warnings to drivers. Additionally, we have designed the app's database structure and completed the programming of the necessary algorithms. To evaluate the performance of the newly developed smartphone app, we performed many field tests on a freeway. Our field test results show that our smartphone app can accurately detect all lane departures on long straight sections of the freeway irrespective of whether the RRH was generated from a Google route or past trajectory.]]></description>
      <pubDate>Thu, 09 Jan 2020 15:16:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/1678168</guid>
    </item>
    <item>
      <title>Evaluation of the Performance of Rumble Strips on Pavements Where Seal Coats Have Been Applied</title>
      <link>https://rip.trb.org/View/1647625</link>
      <description><![CDATA[The repeated application of seal coats on pavements with rumble strips may reduce the effectiveness of the rumble strips to alert drivers of inadvertent lane departures. The goal of this project is to determine how many layers of seal coat can be applied on a pavement with rumble strips before rumble strip performance and, consequently, driver safety is compromised. The Proposing Agency will: 1) Review the literature for rumble strip performance thresholds for safety specifically relating to noise and vibration. 2) Develop a field-based testing plan using a statistical D-optimal design methodology. The plan will either be a before-after design of several projects scheduled for seal coat construction, or a repeated measures design of a few projects with controlled application and testing of multiple seal coat applications. Rumble strip location, seal coat grade, vehicle type, and speed are also considered. 3) Identify test sections according to the plan. 4) Measure interior noise and vibration for sections before and after seal coat application. Also measure texture with a laser profiler on select sections. 5) Determine how many seal coats result in an unacceptable drop in rumble strip performance.]]></description>
      <pubDate>Mon, 26 Aug 2019 15:02:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/1647625</guid>
    </item>
    <item>
      <title>Increasing Work Zone Safety: Worker Behavioral Analysis with Integration of Wearable Sensors and Virtual Reality</title>
      <link>https://rip.trb.org/View/1607555</link>
      <description><![CDATA[Despite increased regulations, restrictive measures, and devices used for warning, work zone injuries and fatalities are still observed at highway construction projects with alarms/notifications being ignored. With a vision to reduce the number of injuries and fatalities, this project aims to understand the key parameters (e.g., work zone location characteristics, personal vigilance levels, types of construction work) that play roles in achieving responsive behaviors in workers. Key questions this research answers are, at what conditions people ignore/response to warnings at work zones? How we can calibrate notification systems for getting responsive actions from workers? What are the modalities, frequencies, and timings of pushing notifications in these calibrated systems? Through wearable sensors and realistic representations of work zones in virtual reality, the research team plans to collect worker behavioral and physiological (heart rate) responses to alarms/warnings/notifications issued under various realistic scenarios and modalities of warning mechanisms (e.g., sensory, visual, audial). With a reinforcement learning based approach, the collected data will be used for determining expected worker/driver behaviors (validated through subjects’ heart rate data) when prompted with an alarm/warning/notification learned from similar behaviors. The outcome of this research will help to calibrate when, at what frequency, and how to (with what modalities) share warnings with habitants of work zones for effective responses towards reduction of incidents.   ]]></description>
      <pubDate>Wed, 22 May 2019 13:19:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/1607555</guid>
    </item>
    <item>
      <title>SPR-4306: Back of Queue Warning and Critical Information Delivery to Motorists</title>
      <link>https://rip.trb.org/View/1530598</link>
      <description><![CDATA[Based on the existing INDOT real-time queue-monitoring systems, this proposal focuses on developing a back-of-queue warning and critical information delivery to motorists approaching congestion queues on Indiana highways. The proposed research aims to investigate feasible solutions for deploying end-of-queue alarms through different smartphone Apps, tune parameters and optimize HMI for better user experience; develop a prototype back-of-queue alerting system based on probe vehicle data, and evaluate the benefits via driving simulator study and limited on-road driving test.
]]></description>
      <pubDate>Mon, 06 Aug 2018 16:34:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/1530598</guid>
    </item>
    <item>
      <title>Full-Scale Wall of Wind Testing of Variable Message Signs (VMS) Structures to Develop Drag Coefficients for AASHTO Supports Specifications</title>
      <link>https://rip.trb.org/View/1474339</link>
      <description><![CDATA[The use of Intelligent Transportation Systems (ITS) technologies on highways is an attractive option for traffic facility operators. Variable Message Signs (VMS) are the cornerstone of ITS infrastructure as they relay messages to motorists for warnings of hazards ahead such as fog, traffic congestion, accidents, construction, and lane closings. VMS messages are of paramount importance in ensuring safety and avoiding fatal crashes (such as the multi-vehicle accident and fatalities along I-75 of Gainesville, Florida, in January 2012 when drivers were blinded by a combination of fog and smoke caused by a nearby brushfire). The objective of this project is to develop accurate drag coefficients for incorporation in the American Association of State Highway and Transportation Officials (AASHTO) Supports Specifications to foster safer and more economic design of VMS structures. The research will: (1) perform full-scale wind (and rain) testing of VMS structures (or portions thereof) at the 12-fan Wall of Wind facility (WoW) of Florida International University (FIU) and measure drag, gust effects, system responses, and failure modes (if any) under a range of service level and extreme wind conditions; (2) compare WoW data to available field measurements for fatigue wind velocities; (3) develop drag coefficients for both fatigue and extreme wind (and rain) conditions; (4) determine fatigue behavior and extreme event failure of connections, members, and foundations subjected to WoW test-based data using dynamic finite element modeling at the University of Alabama, Birmingham (UAB); (5) quantify possible economic benefits gained when using separate drag coefficients for fatigue and ultimate strength design and assess the impact of new coefficients on the design of structural supports; and (6) develop new specifications for AASHTO by stipulating separate drag coefficients to use with fatigue and extreme wind loading for design of VMS structures.]]></description>
      <pubDate>Thu, 13 Jul 2017 01:02:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/1474339</guid>
    </item>
    <item>
      <title>SPR-3901: Synthesis Study: Best Practices for Maximizing Driver Attention to Work Zone Signs (End of Queue Warning Devices)</title>
      <link>https://rip.trb.org/View/1356206</link>
      <description><![CDATA[Methods for attracting driver's attention either within or in advance of those work zones will be the study focus and the intent will be to provide a database that will comprehensively review solutions that will alert drivers entering work zones from previously conducted studies.  The document will potentially assist INDOT in identifying and selecting candidate solutions for improving driver alertness for future implementation and evaluation in construction work zones.]]></description>
      <pubDate>Wed, 03 Jun 2015 01:00:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/1356206</guid>
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