<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <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>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
    </image>
    <item>
      <title>Video-based Conflict Analysis</title>
      <link>https://rip.trb.org/View/2775488</link>
      <description><![CDATA[Video-based conflict analysis is emerging as a promising approach for proactive traffic safety management. Unlike traditional crash data, which requires multiple years to accumulate, video-based measures such as Post-Encroachment Time (PET) and Time-to-Collision (TTC) can reveal risks in near real-time, enabling the North Carolina Department of Transportation (NCDOT) to act more quickly and effectively. Several state agencies have piloted such methods, but independent validation remains limited, and no standardized specifications or vendor prequalification procedures currently exist.
This project will close these gaps by developing validated datasets, testing tools and methods, and producing practical guidance for NCDOT. A multi-stage methodology is proposed.]]></description>
      <pubDate>Wed, 09 Sep 2026 10:29:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775488</guid>
    </item>
    <item>
      <title>Assessing the Value of LiDAR in Detecting Conflicts at Intersections to Enhance Safety
</title>
      <link>https://rip.trb.org/View/2717656</link>
      <description><![CDATA[The goal of this research is to evaluate and compare the effectiveness of camera-based and LiDAR-based systems for detecting traffic conflicts.
]]></description>
      <pubDate>Wed, 24 Jun 2026 14:33:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2717656</guid>
    </item>
    <item>
      <title>AI-Enabled Vision System for Intersection Analytics </title>
      <link>https://rip.trb.org/View/2673053</link>
      <description><![CDATA[Phase I of this project revealed limitations of using a single camera per intersection to automatically extract key traffic performance and safety information from video feeds. To overcome these limitations and enhance data accuracy, the Phase II approach will deploy a second camera at selected high-impact intersections. By fusing the views from two different camera angles, the system can establish a true spatial relationship of objects in the intersection, essentially achieving a more complete 3D understanding of vehicle and pedestrian trajectories.]]></description>
      <pubDate>Tue, 24 Feb 2026 15:00:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2673053</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>AI Powered Conflict Detection and Signal Optimization for Right Turn FYAs in Mixed Modal Intersections</title>
      <link>https://rip.trb.org/View/2640185</link>
      <description><![CDATA[Right turn Flashing Yellow Arrows (FYAs) can support efficient traffic movement, but they also introduce uncertainty for drivers who must judge when to yield to pedestrians and cyclists. This uncertainty can increase the number of near miss interactions at mixed modal intersections. This project will create an artificial intelligence framework that uses video based detection to monitor turning vehicles, pedestrians, and cyclists in real time. The system will compute surrogate safety measures such as post encroachment time and time to collision to identify conditions that may increase the likelihood of a conflict.

The project will use these safety measures to support a signal timing optimization engine that balances safety with delay reduction. The research team will test the framework in simulation and explore opportunities for pilot deployment with the Connecticut Department of Transportation. The resulting tools will give agencies a practical method to assess right turn FYA performance, adjust timing plans when needed, and improve intersection safety through proactive conflict identification.]]></description>
      <pubDate>Thu, 11 Dec 2025 13:35:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2640185</guid>
    </item>
    <item>
      <title>Implementation Requirements for Work Zone Intrusion Technologies to Reduce Fatalities</title>
      <link>https://rip.trb.org/View/2596487</link>
      <description><![CDATA[The latest information published by the Oregon Department of Transportation (ODOT) on fatal crashes shows alarming trends. In 2022, there were 605 fatalities, in 2021, 599, and in 2020, 460. These values represent three consecutive years of ODOT’s highest recorded values, as reported over a 10 year period (Oregon DOT Crash Analysis Unit, 2020). Across the United States, roadway workers on foot being struck by vehicles (both construction equipment and travellng public) was the most prevalent cause of highway worker fatalities (2017-2019) and accounted for 53% of worker fatalities in 2020 (American Road and Transportation Builders Association (ARTBA), 2022). Preventing intrusions, and protecting workers, is a high priority for both ODOT and contractors. ODOT has an immediate need to address this safety aspect, as identified by near misses in the month of February 2023 from Administrator Lynde’s recent all-ODOT email (Lynde, 2023). This research will focus on work zone intrusion technologies, which may also have application in other areas of roadway safety.]]></description>
      <pubDate>Mon, 08 Sep 2025 11:58:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2596487</guid>
    </item>
    <item>
      <title>Effects of Automated Speed Enforcement on Crashes Involving Pedestrians and Bicyclists






</title>
      <link>https://rip.trb.org/View/2570609</link>
      <description><![CDATA[Speed is a crucial factor in the probability of crashes occurring and crash severity. Automated speed enforcement (ASE) has been shown to reduce speeding and speed-related motor vehicle crashes. The National Highway Traffic Safety Administration (NHTSA) has identified automated enforcement as a speed management countermeasure in their Highway Safety Countermeasure Guide for State Highway Safety Offices (SHSOs). The Federal Highway Administration (FHWA) also lists speed cameras as part of their collection of proven safety countermeasures. ASE is a valuable tool that can help SHSOs and local agencies reduce speeding, speed-related crashes, and crash severity.

Many states and local jurisdictions are considering the use of speed cameras to reduce the frequency and severity of vulnerable road user crashes. However, the effects of ASE on crashes involving pedestrians and bicyclists is a gap in the research literature. Research is needed to quantify safety impacts and inform implementation strategies, and develop a better understanding of the influence of roadway context.

OBJECTIVE: The objective of this research is to develop a guide for SHSOs and other stakeholders that: Quantifies the effects of ASE on crashes involving pedestrians, bicyclists, and other nonmotorized users; Examines how roadway context and related factors influence the safety impacts of ASE; and Identifies key considerations for planning and implementing ASE programs to improve safety for vulnerable road users.
]]></description>
      <pubDate>Tue, 01 Jul 2025 14:37:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2570609</guid>
    </item>
    <item>
      <title>Managing and Sharing Traffic Management Systems Video

</title>
      <link>https://rip.trb.org/View/2558409</link>
      <description><![CDATA[Traffic management systems (TMSs), which integrate advanced technologies, software, and data, are essential tools for enhancing the safety, efficiency, and reliability of surface transportation. These systems play a vital role in helping agencies meet the growing and evolving mobility needs of travelers, service providers, partner agencies, and the general public.

Traditionally, TMSs provided only static images of roadway conditions, but technological advancements have transformed this practice into 24/7 live-streaming video feeds of traffic conditions. Increasingly, individuals and private companies are capturing, scraping, or archiving these video feeds, and often repackaging and selling the data to public or private customers, raising legal, technical, and operational challenges for transportation agencies.

Most TMSs do not record or archive video feeds due to concerns over legal obligations and public information requests, risks of releasing sensitive or personally identifiable information (PII), potential liability from unintended uses, and technical burdens of video management. The rising expenses of data storage and telecommunications add complexity to video management.

Research is needed to help agencies evaluate the implications, benefits, and risks of sharing TMS video.

OBJECTIVE: The objective of this research is to develop a guide for transportation agencies on managing and sharing access to TMS video. The research will identify current practices, challenges, unintended consequences, and opportunities for improvement.]]></description>
      <pubDate>Tue, 27 May 2025 20:58:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2558409</guid>
    </item>
    <item>
      <title>Laying the cornerstone of Advanced Air Mobility infrastructure: A Low-Altitude Airspace Surveillance System Optimized for Reliability, Robustness, Resilience, and Cost
</title>
      <link>https://rip.trb.org/View/2502108</link>
      <description><![CDATA[This proposed research project aims to: (1) analyze and assess the reliability, robustness, and resilience of surveillance systems for detecting and tracking Advanced Air Mobility (AAM) traffic in the low-altitude national airspace system (NAS); then, building on research findings, (2) develop a sensor network design software tool to design a surveillance sensor network for AAM optimized for reliability, robustness, resilience, and cost, across major cities of the state of Ohio; and, lastly, (3) formulate a safety protocol for AAM traffic managers and operators to follow during AAM surveillance outages or when the performance of the AAM surveillance system is impaired due to failure events. 
                     ]]></description>
      <pubDate>Mon, 03 Feb 2025 10:50:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2502108</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>Monitoring of Urban Roadway Safety Hazards from Existing Bus-based Video Imagery: Phase 2</title>
      <link>https://rip.trb.org/View/2440023</link>
      <description><![CDATA[Traffic safety is diminished by drivers’ changing lanes in queued traffic at signalized intersections, bus stops, and construction zones, mixes of vehicle classes, variability in speeds, and vehicle overtaking. Assessing locations with these recurring but dynamic hazards requires extensive and ongoing data collection. Traditional data collection methods rely on sensors at permanent or temporary fixed locations, which are costly, labor intensive, and provide limited collection over time and space and only of some hazard contributors. Moreover, the location of these sensors may be influenced by factors other than optimal sampling, such as requests from well-organized constituencies. Therefore, relying on the traditional methods could lead to missing high-risk conditions resulting in decreased safety and inequitable outcomes.

Transit buses operate regularly over wide networks, and most bus fleets are already equipped with cameras that record the environment inside and outside buses for liability, security, and safety purposes. Consequently, the imagery is available for other uses at near-zero marginal cost, and the extensive spatial coverage of transit fleets would provide comprehensive views that could be used to determine times and locations of regularly occurring safety hazards. Moreover, this imagery has been shown by the principal investigators (PIs) to be effective in monitoring traffic volumes across time and space, information that provides exposure-based context for identifying safety hazards. In the current phase 1 project (year-1), the PIs are investigating the use of available, repeated, and extensive imagery recorded by cameras mounted on transit buses in regular operation to identify “hazardous hotspots”. The proposed phase 2 project (year-2) would build on the phase 1 investigations.

The PIs have been obtaining transit bus-based video imagery to estimate traffic flows across the Ohio State University (OSU) campus and providing summary results to campus planners and operators on a regular basis. The OSU campus will again be used as a living lab testbed. The size and diversity of land uses make the campus representative of urban areas. Moreover, the campus has been undergoing major construction activities, which allows investigation of different infrastructure conditions that could influence traffic safety. Using the campus as an experimental testbed also allows for in-situ ground-truth observations to assess the accuracy of the video-based results.

Hazards being considered in phase 1 include lane specific queue lengths at intersections and bus stops and vehicle type mix with an emphasis on vulnerable vehicles (e.g., bicycles, scooters, and motorcycles). Frequency of lane-changing in the presence of queues, speeds, and speed variation where autos conflict with vulnerable vehicles are also important safety factors. In phase 2, the ability to measure these hazards from the imagery would be investigated, and methods to do so would be developed. In addition, changes in speeds at construction zones measured from the imagery would be explored given the safety hazards associated with these zones. Moreover, while the identification of hazards in phase 1 is being demonstrated using semi-automatic techniques based on a Graphical User Interface, in phase 2 automation of the identification of hazards will be pursued.]]></description>
      <pubDate>Sun, 13 Oct 2024 09:34:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440023</guid>
    </item>
    <item>
      <title>An Automated Training Model for Selecting Maritime Traffic Monitoring and Tracking Model Types using AIS Data with Missing Information </title>
      <link>https://rip.trb.org/View/2406732</link>
      <description><![CDATA[ This project will utilize Automatic Identification System (AIS) datasets to design scalable Automated Maritime Traffic Monitoring and Analysis (AMTMA) applications and tools and work with two Computational data enabled science and engineering (CDS&E) Ph.D. students to produce two dissertations in this direction. Critical applications such as the detection of anomalies, offshore and onshore attacks and data intrusions, require fast mechanisms for Artificial Intelligence (Al) analysis of thousands of events per second, as well as efficient techniques for the analysis of massive historical AIS data with missing information. There have been major developments of Big Data Analysis Frameworks for analyzing the AIS historical data, but their applications and scalable analysis techniques to the AMTMA domain remains poorly understood and difficult to benchmark due to the frequency of missing information in the often collected AIS datasets. This project introduces a Least-squares regression model with missing data and a Principal Component model for sparse functional data using AIS data that will aid in monitoring maritime traffic and directly assist in averting accidents, tracking vessels, and support in avoidance of dangerous situations. The elements of a cleaned AIS dataset with missing information are often presented as curves (trajectories) rather than single points. Functional principal components can be used to describe models of variation of such curves. If one has complete measurements for each vessel trajectory or, as is more common, one has the dataset in a spreadsheet format collected at the same time points (time stamps) for all trajectories, then many standard data analytics techniques may be applied. However, vessel trajectory data as appeared in the AIS dataset is collected at irregular and sparse set of time stamps which can differ widely across individual vessels. This project will present a technique for handling this more difficult case using a reduced rank mixed effects framework. This project also explores a Least-square regression model with missing data to develop a Regression Learner app to automatically train a selection of different models on the AIS data. An automated training model will be developed to quickly try a selection of model types, and then explore promising models interactively. ]]></description>
      <pubDate>Tue, 23 Jul 2024 16:29:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2406732</guid>
    </item>
    <item>
      <title>SPR-4930:  Further Refinement and Integrated Platform for INDOT Traffic Management and Safety Toolset</title>
      <link>https://rip.trb.org/View/2401904</link>
      <description><![CDATA[This project addresses INDOT's need to use highway surveillance cameras to gather traffic information, such as flow rate, weaving data, and traffic anomaly detection.  TASI and INDOT will work on two objectives: (1) the expansion and refinement of the anomaly detection method being developed, (2) the development of a user-friendly system that integrates the software developed for lane-based flowrate detection, weaving analysis, and anomaly detection in the past and present, and other TASI and INDOT jointly developed traffic management tools in the near future. The final system will be deployed
to INDOT for daily operations.
]]></description>
      <pubDate>Tue, 09 Jul 2024 14:48:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/2401904</guid>
    </item>
    <item>
      <title>Enhancing Pedestrian and Bicyclist Safety through Abnormal Driving Behavior Detection</title>
      <link>https://rip.trb.org/View/2401754</link>
      <description><![CDATA[Abnormal driving poses a significant risk not only to the driver (referred to as the ego vehicle) but also to other road users, particularly pedestrians and bicyclists. Existing literature (Wu et al., 2018) has shown that early detection and intervention in cases of abnormal driving can prevent traffic accidents or at least reduce their severity. To this end, this project is focused on creating an application designed to improve the safety of pedestrians and bicyclists by identifying abnormal driving behaviors.

Current research on detecting abnormal driving behaviors largely depends on the use of onboard sensors that monitor various aspects of the driver’s physical state and driving patterns, such as facial and gaze direction, blood pressure, and heart rate. However, this approach requires drivers to equip their vehicles with specific devices, often at their own expense, which may hinder the popularization of such technologies. An alternative and more cost-effective solution involves using roadside sensors, like cameras, LIDAR, and RADAR, to detect abnormal driving. This method analyzes and predicts vehicle trajectories based on live-streamed data from roadside sensors. If a vehicle’s trajectory significantly deviates from its predicted path, it can be identified as abnormal. Despite its promising results, this method has strict requirements regarding the accuracy of trajectory prediction. Otherwise, too many false alarms could erode public trust in the technology. Addressing these challenges and improving the reliability and accuracy of abnormal driving detection methods is a key goal of this project.]]></description>
      <pubDate>Mon, 08 Jul 2024 14:54:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2401754</guid>
    </item>
    <item>
      <title>Evaluating the Impact of Speed Safety Cameras in Interstate Work Zones</title>
      <link>https://rip.trb.org/View/2395031</link>
      <description><![CDATA[This project aims to assess the effectiveness of Speed Safety Cameras (SSCs) within interstate work zones across Virginia, following legislation enacted in 2020 that supports the deployment of SSCs to automatically monitor vehicle speeds in designated areas such as work zones. This project focuses on five pilot work zones to evaluate the impact of SSCs on reducing traffic flow speeds, specifically examining changes in mean speed, speed distribution, and the proportion of vehicles exceeding the speed limit. This research will collect traffic data from all five pilot sites using traffic sensors strategically placed upstream, downstream, and at the SSCs to capture speed data before and after SSC installation. This setup aims to measure the immediate and sustained impacts of SSCs on speed compliance and to explore the spatial extent of these effects. Additionally, the project will gather data on traffic crashes and injuries to assess safety implications, although it may be challenging to achieve statistical significance. Parallel to quantitative analysis, this research will also facilitate a discussion with key stakeholders involved in the implementation of SSCs to gather operational insights and implementation challenges. Anticipated outcomes of this project include detailed analytical reports that offer insights into the specific conditions under which SSCs are most effective in reducing speeds. Additional outcomes may include optimal placement of SSCs or recommendations for future pilots to better understand the factors impacting SSC effectiveness.]]></description>
      <pubDate>Thu, 20 Jun 2024 09:33:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2395031</guid>
    </item>
  </channel>
</rss>