<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>Crash Analysis at Intersections with Transit Signal Priority</title>
      <link>https://rip.trb.org/View/2643022</link>
      <description><![CDATA[Transit Signal Priority (TSP) systems are widely used to improve transit operations at signalized intersections, but variations in signal display design may affect how drivers interpret and respond to these signals. Although recent updates to the Manual on Uniform Traffic Control Devices  (MUTCD) revised standards for certain TSP indications, many jurisdictions continue to operate legacy signal designs. The safety implications of these design differences are not well understood.

This project investigates the relationship between TSP signal display configurations and crash outcomes at intersections. The research will develop a national inventory of TSP implementation sites and associated signal design characteristics, compile crash histories for these locations, and analyze crash frequency and severity using statistical modeling. Crash report narratives will also be reviewed to identify behavioral factors linked to specific signal displays. Results will provide evidence-based guidance on transit signal design and support future updates to national standards, improving intersection safety while maintaining effective transit operations.]]></description>
      <pubDate>Thu, 18 Dec 2025 14:02:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2643022</guid>
    </item>
    <item>
      <title>Investigation of Vulnerable Road User Fatalities and Serious Injuries on Freeways</title>
      <link>https://rip.trb.org/View/2601433</link>
      <description><![CDATA[Although pedestrians, bicyclists, and other vulnerable road users are not “supposed” to be present on freeways and other high-speed limited access roadways, a substantial proportion of all vulnerable road user (VRU) crashes occur in these environments. Due to high speeds, casualty severity is often high, resulting
in many deaths and severe, life-changing injuries. These events heavily burden victims, families, and medical insurance programs funded by employers and taxpayers.

Spot-checks of the North Carolina Department of Transportation (NCDOT) Bicyclist and Pedestrian Crash Map and findings from research in other states indicate these casualties involve wide-ranging circumstances. A few examples include individuals attempting to cross a freeway or other high-speed limited-access roadway at grade, drivers walking to find help with a disabled vehicle, on-the-job incidents involving road workers and first responders, and crashes involving unhoused people who camp on the right-of-way.

This action-focused project will conduct a thorough review of previous research on freeway/expressway VRU casualties, develop a typology of non-overlapping categories that can be used to analyze them, compile the North Carolina VRU casualty data for freeways and other highspeed limited access roadways, review crash narratives to verify that they occurred on such a roadway (and not, for example, on the arterial level of a freeway overpass), manually assign each fatality and serious injury to one of the categories in the typology, prepare maps that illustrate their location and nature, and identify both locationally-specific and statewide actions that can be taken by NCDOT and other agencies to reduce the frequency and severity of VRU crashes on high-speed limited access roadways.​]]></description>
      <pubDate>Thu, 18 Sep 2025 00:57:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2601433</guid>
    </item>
    <item>
      <title>Field Trauma Triage Guidelines Demonstration Project</title>
      <link>https://rip.trb.org/View/2444690</link>
      <description><![CDATA[This new contract will provide support for, and measure the process and outcomes of, a demonstration project wherein State, tribal, territorial, and local emergency medical service (EMS) systems will receive funding to implement the newest revision (2021) of the Field Trauma Triage Guidelines. These guidelines improve decision-making in both treatment and transport for injured patients in the field, including motor vehicle crash victims.]]></description>
      <pubDate>Mon, 21 Oct 2024 15:29:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2444690</guid>
    </item>
    <item>
      <title>SafeSpeed: Enhancing Work Zone Safety through Speed Enforcement 
</title>
      <link>https://rip.trb.org/View/2440014</link>
      <description><![CDATA[The large number of work zone crashes has been a significant concern of transportation agencies and researchers. In the US, a work zone crash occurred every five minutes during 2015-2019. One approach for transportation agencies to reduce work zone crashes is to lower the speed within work zones, for example, posting speeding limits and installing speeding cameras. This approach is supported by studies that highlighted that average traffic speed is associated with crash risk. However, the findings of the relationship between traffic speed and crashes are inconsistent, which could lead to conflicting or even misleading interventions with the speed enforcement in work zones. Work zone presence could lead to the reduction of actual traffic speed that influences crash risk and, at the same time, directly impose effects on crash risks.  It is challenging to rigorously separate these direct and indirect impacts. Furthermore, the actual impact of speed enforcement countermeasures on work zone crash risk has been rarely studied among the literature, providing limited knowledge on whether these countermeasures are effective in reducing crash risk near work zones in practice.

In this research project, the research team will apply a comprehensive causal analysis and Web-Geographic Information Systems (GIS) approach to enhance work zone safety through speed enforcement in Pennsylvania and Maryland. It contains three core initiatives. First, it develops a causal inference model to analyze the impact of work zones on crash risk controlling for traffic speed with the equational g-estimation and regression discontinuity design (RDD), using multiple large-scale and high-granular data sets. Second, it examines the work zone impact on crash risk under different speed enforcement countermeasures. Lastly, the research team creates an interactive Web-GIS platform for comprehensive traffic safety analysis in work zones, enabling stakeholders to access and analyze crashes related to work zones, speed enforcement measures, and other important crash contributors, with continuous data updates planned until 2025. This platform aims to identify high-risk areas and provide insights for safety improvements in work zones.

First, the team will establish a rigorous causal inference model to infer the causal impact of work zones on crash risk when the traffic speed is controlled with high-granular and multi-source data sets. The team proposes to use an innovative approach, i.e., the combination of the sequential g-estimation and RDD, to examine the causal effect of the presence of work zones on crash occurrences when the traffic speed is controlled. The sequential g-estimation removes the effect of traffic speed on crash risk. RDD mitigates the potential confounding bias caused by roadway characteristics. The proposed method will be implemented using high-granular and multi-source data of thousands of work zones in Pennsylvania (PA) and Maryland (MD) between 2018 and 2023 to control for the complex built and natural environments and reduce the associated bias of the estimation. The results can provide insights for most desired and actual traffic speeds to reduce work zone crash risk.

Second, the team will examine the impact of work zones on crash risk under different speed enforcement countermeasures. The team will apply the same framework in the first step to examine the heterogenous causal impact of work zones on crash risk under different speed enforcement countermeasures, including no speed enforcement, posting speed limit, and posting speed limit along with enforcement (e.g., automated speed enforcement and high-visibility enforcement), and compare the impacts for the work zones in PA and MD. In addition, the team will further estimate these heterogenous impacts (by speed enforcement countermeasure) under various work zone characteristics, time of day, and traffic volumes. The results can offer information on how different speed enforcement countermeasures modify the causal impact of work zones on crash risk and, accordingly, provide implications for better deploying these countermeasures.

Third, the team will build an interactive Web-GIS platform for work zone traffic safety analysis using the safety data in PA and MD. The digital platform provides users with an online interactive interface to explore all work zones in PA and MD by multiple aspects, including speed enforcement countermeasures, average speed, traffic volumes, roadway characteristics. In addition, the platform can help users identify high-risk locations, highlight potential crash contributors, and offer suggestions on how to improve work zone safety for each work zone based on their characteristics and locations.  In addition, the team will continue to collect and archive up-to-date data from various data providers in both PA and MD from 2024 to 2025 and enhance the web platform. The safety data providers include Pennsylvania Department of Transportation (PennDOT), Maryland Department of Transportation (MDOT SHA), Waze, NOAA, and private data sources, including INRIX, TomTom, and Replica. The team will integrate and analyze large-scale crash data and develop an additional function to the platform to visualize and forecast crash types, frequencies, and severity for each road segment in the two states, especially those with work zones and different speed enforcement countermeasures. With that said, the platform allows transportation agencies and other related stakeholders, such as urban planning departments, local communities, consulting firms, and academic institutions, to access historical, real-time, and forecasted traffic safety metrics for all work zones. The team will continue to interview various data providers to enhance the quality and quantity of massive data in both states.
]]></description>
      <pubDate>Sat, 12 Oct 2024 12:18:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440014</guid>
    </item>
    <item>
      <title>Smart AI-Technology Employment for Crash Data Analysis</title>
      <link>https://rip.trb.org/View/2431593</link>
      <description><![CDATA[Statistics from the National Highway Traffic Safety Administration  show that the United States in 2022 there were 42,795 fatalities and about 2.5 million injuries resulting from motor vehicle traffic crashes. Among many crashes, pedestrian-related car crashes hold significant importance due to their potential to cause severe injuries and loss of life, as well as their broader societal impact. These crashes underscore the vulnerability of pedestrians in collisions with vehicles. The consequences extend beyond individuals involved; the crash outcomes affect families and communities. Addressing pedestrian crashes requires a holistic approach that combines improved infrastructure, traffic regulations and enforcement, education efforts and public awareness campaigns, emergency / trauma medical care, and innovative vehicle safety technologies. The US DOT’s National Road Safety Strategy and the Safe Systems Approach reinforce the need to create more pedestrian-friendly environments and reduce the human and
economic toll of these crashes, while fostering safer and more inclusive communities. In this regard, this research will take an initiative effort with crash narrative data – type of data that have not been exploited well historically  to extract new insights about pedestrian-related vehicle crashes. Crash narratives include crash-related details, facilitating a deeper comprehension of each incident. By examining a collection of crash reports, one can discern recurring patterns and trends associated with specific attributes, such as particular human, roadway, vehicular, traffic control, or geographical factors. The primary objective of this research is to uncover new insights that could serve as fundamental stepping stone to foster advancements in traffic safety management. Moreover, this study aims to augment the existing knowledge base by creating an
innovative methodology that harnesses Artificial Intelligence (AI) and Natural Language
Processing (NLP) to efficiently delve into crash narratives, thus enhancing our level of
understanding of such crashes. Methodological advancement and findings key to transportation safety will be incorporated into various educational and outreach programs at University of Nevada, Las Vegas (UNLV).]]></description>
      <pubDate>Tue, 17 Sep 2024 17:38:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2431593</guid>
    </item>
    <item>
      <title>Investigating safety and risk disparity between personally owned and shared micromobility modes</title>
      <link>https://rip.trb.org/View/2401752</link>
      <description><![CDATA[This research examines the safety implications of the increasing popularity of micromobility, particularly focusing on shared e-bikes and bicycles. The study has three main goals: comparing crashes involving shared e-bikes and bicycles, understanding how safety trends for personally owned e-bikes are changing, and identifying differences in safety between personally owned and shared e-scooters. The research seeks to uncover patterns, risk factors, and disparities in micromobility-related accidents by analyzing existing data and collaborating with industry partners to conduct surveys. By analyzing detailed crash reports and new injury codes related to micromobility, the project aims to provide evidence to inform policies and improve infrastructure, ultimately enhancing overall transportation safety. By fostering collaboration between academia and industry, the project enhances our understanding of micromobility safety and provides invaluable learning experiences. Ultimately, the research endeavors to inform policymakers, practitioners, and the public on strategies to mitigate safety risks associated with the proliferation of micromobility modes in urban environments.]]></description>
      <pubDate>Mon, 08 Jul 2024 14:54:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2401752</guid>
    </item>
    <item>
      <title>The Role of Built Environment Factors in Enhancing Pedestrian and Bicycle Safety: A Comprehensive Analysis and Policy Implications</title>
      <link>https://rip.trb.org/View/2401750</link>
      <description><![CDATA[Despite recent efforts to achieve Vision Zero goals in the US, pedestrian and bicycle safety remains a critical issue that affects individuals and communities. The nearly 7,500 pedestrian fatalities annually and 1,000 bicyclist fatalities in recent years highlight the urgent need to address pedestrian and bicycle safety, particularly in urban, suburban, and rural areas where exposure and crash risks are changing. Importantly, the role of the built environment in such environments is changing, e.g., disadvantaged communities, including low-income neighborhoods and communities of color, can face higher risks of pedestrian and bicycle crashes. This project focuses on enhancing pedestrian and bicycle safety through a detailed analysis of built environment features at the neighborhood level. As part of the Center for Pedestrian and Bicyclist Safety's (CPBS's) priorities on Safety Design, it explores the impact of factors such as street lighting, sidewalk availability, road design, land use type/mix and density, and traffic volumes on pedestrian and bicycle crashes frequency and their severity. With a particular emphasis on safety disparities in disadvantaged communities, this research utilizes a variety of data sources, including police crash reports, census data, land use data, and the Equitable Transportation Community (ETC) data released by the US Department of Transportation. The study will employ both traditional statistical methods and explainable artificial intelligence techniques to analyze data and identify key contributors to crash occurrences and severity during both day and night. Techniques such as negative binomial models, ordered probability models and structural equation modeling will be used to understand the direct and indirect effects of built environment features on safety outcomes. Special attention will be given to the role of these features in disadvantaged communities, aiming to develop targeted interventions to reduce crashes and enhance pedestrian and bicycle safety.]]></description>
      <pubDate>Mon, 08 Jul 2024 14:54:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2401750</guid>
    </item>
    <item>
      <title>An Analysis of Pedestrian Safety and Injury Severity at Bus Stops Using the CRSS Database</title>
      <link>https://rip.trb.org/View/2401749</link>
      <description><![CDATA[Ensuring safe pedestrian and bicycle access to bus stops is essential to the success of public transit systems. Prior studies have analyzed crash data to investigate bus stop-related pedestrian and bicyclist safety. However, most prior research assumes that all pedestrian and bicyclist crashes within a given distance of a bus stop are associated with the bus stop, which may not be true. A significant challenge in transit-related safety research is the absence of crash data that distinctly identifies stop/station-related safety incidents. Recently, the National Highway Traffic Safety Administration has begun to categorize bus stop-related crashes within the Crash Report Sampling System (CRSS) and Fatality Analysis Reporting System (FARS). This study will expand on the Center for Pedestrian and Bicyclist Safety (CPBS) Year 1 project, Identifying Research Priorities to Improve Safety for Pedestrians and Bicyclists Accessing Bus Stops, which used the FARS dataset to conduct a bus stop-related pedestrian and bicyclist safety analysis. This study will analyze bus stop-related pedestrian and bicyclist safety using the CRSS database, which provides nationally representative crash data. The overarching goal is to identify bus stop-related crash characteristics and determine which factors lead to more severe outcomes.]]></description>
      <pubDate>Mon, 08 Jul 2024 14:54:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2401749</guid>
    </item>
    <item>
      <title>Examine Crash Responsibility of Alcohol- and Drug-Positive Drivers</title>
      <link>https://rip.trb.org/View/2377869</link>
      <description><![CDATA[This study will conduct a responsibility analysis for alcohol and other drugs and their roles in causing crashes. That is, does a crash participant’s positivity for alcohol or another drug increases the odds of being responsible for a crash? The study will also conduct analyses of potential bias in assigning responsibility of a crash. A research question is, does knowledge regarding alcohol or other drug presence bias a non-blinded reviewer towards assigning crash responsibility to that individual?
]]></description>
      <pubDate>Tue, 07 May 2024 08:03:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2377869</guid>
    </item>
    <item>
      <title>Investigate the Impact of Rumble Strips on Motorcyclists</title>
      <link>https://rip.trb.org/View/2342050</link>
      <description><![CDATA[
The goals of this research project are to investigate the effects of rumble strips on motorcycle crashes and to engage with motorcycle communities to promote communication about rumble strips and their impact on safety.]]></description>
      <pubDate>Tue, 20 Feb 2024 12:07:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2342050</guid>
    </item>
    <item>
      <title>Unlocking Forensics Data for Vehicles Involved in Motor Vehicle Crashes</title>
      <link>https://rip.trb.org/View/2321643</link>
      <description><![CDATA[Electronic crash data collection represents a critical advancement in the field of traffic safety and accident analysis. By capturing vehicle and environmental data in the moments leading up to a collision, this technology provides invaluable insight into crash causation, driver behavior, and vehicle performance. This paper explores the framework and significance of pre-crash data acquisition through integrated electronic vehicle systems and connected vehicle technologies.


In 2012, NHTSA proposed to convert 49 CFR 563 ‘‘if equipped” requirements for EDRs (Electronic Data Recorders) into a new Federal Motor Vehicle Safety Standard (FMVSS) mandating the installation of EDRs in most light vehicles. 


These systems collect variables such as speed, brake application, steering input, and throttle position, which are essential for reconstructing crash scenarios and enhancing vehicle design, regulatory standards, and roadway infrastructure. This paper emphasizes the role of real-time data in improving post-crash investigation accuracy, informing predictive crash modeling, and guiding proactive interventions in traffic safety. Additionally, the integration of such data with centralized traffic databases could assist in smarter transportation systems and more efficient emergency responses.]]></description>
      <pubDate>Fri, 12 Jan 2024 11:07:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2321643</guid>
    </item>
    <item>
      <title>Analysis of Lane Departure Crashes Leveraging Advanced Driver Assistive Systems (ADAS) Machine Vision Sensing Data
PROBLEM</title>
      <link>https://rip.trb.org/View/2310056</link>
      <description><![CDATA[The goal of this research is to develop a new technical framework to quantify contribution factors of lane departure crashes in Washington leveraging emerging machine vision data collected by Advanced Driver Assistive Systems (ADAS)-equipped vehicles. Under this goal, the research teams (WSU and VSI lab) aim to achieve three specific objectives:
(1) collect machine vision data using ADAS-equipped vehicles; (2) develop methods to retrieve lane marking and other road conditions from the collected machine vision data;  and (3) quantify contributions of different factors in lane departure crashes.]]></description>
      <pubDate>Thu, 14 Dec 2023 13:52:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2310056</guid>
    </item>
    <item>
      <title>Breaking Down Commercial Motor Vehicle Crashes: What are the Main Causes?</title>
      <link>https://rip.trb.org/View/2298714</link>
      <description><![CDATA[The number of commercial motor vehicle (CMV) crashes in Idaho showed a notable increase in the past year. Safety for all roadway users is a core mission of the Idaho Transportation Department. Crashes can have a lifetime impact on Idaho residents as well as resulting in traffic congestion and lost mobility for people and goods. This project will examine CMV crash data to determine common causes and contributing circumstances. Researchers will develop recommendations for effective countermeasures for mitigating CMV crashes.]]></description>
      <pubDate>Wed, 29 Nov 2023 14:30:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2298714</guid>
    </item>
    <item>
      <title>Urban Demographic Shift of Pedestrian and Bicyclist Collisions, Equity, and Police Enforcement</title>
      <link>https://rip.trb.org/View/2232673</link>
      <description><![CDATA[The project aims to address the rising pedestrian fatalities nationally by examining the geographic shifts in pedestrian fatalities and injuries and their relationship with neighborhood demographic changes, police enforcement, and social equity patterns. The research team will analyze multi-year crash and injury data from various regions and collaborate with police departments to study the correlation between police enforcement and collision rates. The project will produce a technical report, interactive data visualization tools, and recommendations for safety treatments and equitable police enforcement.]]></description>
      <pubDate>Wed, 23 Aug 2023 20:51:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2232673</guid>
    </item>
    <item>
      <title>Safety Evaluation of Turning Maneuvers at California Intersections</title>
      <link>https://rip.trb.org/View/2232677</link>
      <description><![CDATA[This research will provide an evaluation of the safety of California intersections in a context-sensitive manner. Specific objectives of this project include: Analyze statewide collision data to identify infrastructure and/or community context(s) that contribute to conditions leading to crashes in intersections, including left turns, right-turns on red, etc. Based on the analysis of collision data, and deeper investigations using Google Street View, the research team will identify context-sensitive strategies based on the safe systems approach that can address the safety concerns. The strategies would include a prohibition of the maneuver and installation of protected intersections based on NACTO recommendations, among others.]]></description>
      <pubDate>Wed, 23 Aug 2023 20:44:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2232677</guid>
    </item>
  </channel>
</rss>