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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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      <title>Work Zone Worker Alert Systems via a VR-Based Human-in-the-Loop Simulation</title>
      <link>https://rip.trb.org/View/2611276</link>
      <description><![CDATA[This research addresses work zone safety hazards through human-in-the-loop Virtual Reality simulation to evaluate alert system effectiveness for worker protection, motivated by the tragic March 2023 Maryland incident that killed six workers. The study employs VR-based simulation where participants act as work zone flaggers to empirically assess how different alert modalities affect worker reaction times and safety compliance during hazardous events. The methodology adapts existing UC-win/Road driving simulation models from driver perspective to worker first-person viewpoint, incorporating repeatable errant vehicle scenarios for standardized stimulus delivery. A within-subjects experimental design exposes participants to five alert conditions including no alert control, visual warning with flashing beacon, audio alarm, haptic vibrating controller feedback, and combined multimodal alerts. Alert scenarios are paired with auditory, visual, and haptic distraction conditions to test robustness under realistic work environment challenges. Data collection includes pre-simulation demographic and safety attitude surveys, objective reaction time and compliance measurement during simulation, and post-simulation feedback on alert effectiveness and simulation realism. Statistical analysis using repeated measures ANOVA compares performance across scenarios, synthesizing quantitative results with qualitative survey feedback for comprehensive human factors understanding.]]></description>
      <pubDate>Mon, 20 Oct 2025 16:15:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2611276</guid>
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    <item>
      <title>Evaluation of One-Way Flagging Operations</title>
      <link>https://rip.trb.org/View/2286546</link>
      <description><![CDATA[The objective is to establish a more defined and reliable standard for determining appropriate construction times and limitations to minimize traffic delays and increase safety during one-way flagging operations. This would be done by evaluating the roadway capacity, queue lengths, and travel times during one-way flagging operations throughout Utah. This research could also identify times and locations where one-way flagging operations could be extended, thereby reducing project timelines and costs. Ultimately, by improving construction operations, the safety of flaggers, other highway workers, and the traveling public will improve. ]]></description>
      <pubDate>Mon, 06 Nov 2023 14:54:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2286546</guid>
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      <title>SPR-4837:  Preventive Flagging Operations for Enhancing Stationary and Mobile Work Zone Safety</title>
      <link>https://rip.trb.org/View/2253920</link>
      <description><![CDATA[This project assesses flagging operation alternatives to better protect flaggers who work in dangerous environments, provide motorists with more effective traffic and warning, and develop a decision-making matrix based on contextual analysis to help select the most appropriate work flagging operation practices depending on a roadway, work zone, and activity characteristics.]]></description>
      <pubDate>Fri, 22 Sep 2023 14:37:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2253920</guid>
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      <title>Some Core Techniques for Safe Autonomous Driving</title>
      <link>https://rip.trb.org/View/1841396</link>
      <description><![CDATA[Reliable curb detection is critical for safe autonomous driving in urban contexts. Curb detection and tracking are also useful in vehicle localization and path planning. Past work utilized a 3D LiDAR sensor to determine accurate distance information and the geometric attributes of curbs. However, such an approach requires dense point cloud data and is also vulnerable to false positives from obstacles present on both road and off-road areas. In this effort, the research team proposes an approach to detect and track curbs by fusing together data from multiple sensors: sparse LiDAR data, a mono camera and low-cost ultrasonic sensors. The detection algorithm will be based on a single 3D LiDAR and a mono camera sensor used to detect candidate curb features and it effectively removes false positives arising from surrounding static and moving obstacles. The detection accuracy of the tracking algorithm is boosted by using Kalman filter-based prediction and fusion with lateral distance information from low-cost ultrasonic sensors. The team will also conduct a complementary effort with the following goals. Autonomous vehicles promise significant advances in transportation safety, efficiency and comfort. However, achieving the goal of full autonomy is impeded by the need to address several operational challenges encountered in practice. Gesture recognition of flagmen on roads is one such set of challenges. An autonomous vehicle needs to make safe decisions and facilitate forward progress in the presence of road construction workers and flagmen. However, human gestures under diverse environmental conditions are very varied and represent significant complexity. In this effort, the team proposes (1) a taxonomy of challenges for organizing traffic gestures, (2) a sizeable flagman gesture dataset, and (3) extensive experiments on practical algorithms for gesture recognition. The team will categorize traffic gestures according to their semantics, flagman appearances, and the environmental context. The team will then collect a dataset covering a range of common flagman gestures with and without props such as signs and flags. Finally, the team will develop a recognition algorithm using different feature representations of the human pose and perform extensive ablation experiments on each component.]]></description>
      <pubDate>Wed, 17 Mar 2021 16:23:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/1841396</guid>
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    <item>
      <title>Safety Circuit Rider Program Workplan for 2018</title>
      <link>https://rip.trb.org/View/1507569</link>
      <description><![CDATA[The proposed activities for the 2018 Safety Circuit Rider program include:
(1) Organize and coordinate multidisciplinary Road Safety Assessment (RSA) efforts for local agencies; 
(2) Conduct on-site, on-call training in work zone and flagger safety;
(3) Organize, coordinate and moderate the annual multidisciplinary Local Road Safety Workshops;
(4) Provide support for the Governor's Traffic Safety Bureau (GTSB) High Five program, including coordination with county engineers and conducting RSAs;
(5) Plan and facilitate traffic safety workshops on topics such as crash history, safety countermeasures, data mining, etc;
(6) Conduct on-call training and outreach on topics such as signing, marking, and retroreflectivity;
(7) Assist in planning and provide instruction for the annual Iowa Department of Transportation (DOT) Work Zone Safety Workshop series;
(8) Participate in association meetings and conferences and provide safety presentations, demonstrations, and moderator services when requested;
(9) Develop articles on safety-related topics for use in newsletters and other publications;
(10) Provide technical assistance to and feedback on safety-related questions received from local agencies;
(11) Serve on safety-related committees at the request of the Iowa DOT (e.g., HSIP-Secondary, SHSP, etc.); and
(12) Manage safety-related equipment loan program (digital ball banks, retroreflectometer).]]></description>
      <pubDate>Mon, 02 Apr 2018 13:00:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/1507569</guid>
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    <item>
      <title>Reducing Traffic Crashes at Road Construction Access Points</title>
      <link>https://rip.trb.org/View/1486053</link>
      <description><![CDATA[Technologies that can detect when a construction vehicle is about to enter main-lane traffic and warn approaching vehicles through electronic signing are available, but their effectiveness in reducing crash risk has not been thoroughly evaluated. Clear guidance is needed on the technologies available and of interest to Texas Department of Transportation (TxDOT), including design, placement, delineation of ingress and egress points for work zones on roadways. These crash reduction technologies need to be tested for their effectiveness in the field at a sample of construction zones. This guidance will enhance the safety of road workers and road users by preventing crashes and adequately protecting workers in work zones. The purpose of this research is to compile crash and operational data and perform a comprehensive assessment of the risk factors and root causes of crashes at road construction and maintenance access points and document crash history and causal factors such as road speed limit, traffic volumes, type of facility, the available access point warning technologies, and a review of flagging methodologies, ultimately providing a evaluation of the various methods in reducing crash risks at work zones entrances and exits.]]></description>
      <pubDate>Fri, 20 Oct 2017 12:15:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/1486053</guid>
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