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    <title>Research in Progress (RIP)</title>
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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>
    <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>Using Large Language Models to Generate Synthetic Data for Proactive
Pedestrian Safety Prediction: Overcoming Data Collection Barriers in Surrogate Safety Analysis</title>
      <link>https://rip.trb.org/View/2663604</link>
      <description><![CDATA[Pedestrian fatalities remain a persistent and growing safety crisis, with over 7,500 pedestrians killed on U.S. roads annually. Effective countermeasure deployment requires identifying high-risk locations before crashes occur, yet traditional crash-based analyses are insufficient due to the rarity of pedestrian crashes at individual intersections. Surrogate safety analysis using pedestrian–vehicle close calls offer a proactive alternative, but comprehensive observational data collection is prohibitively expensive and time-intensive. Video monitoring requires specialized equipment, extended deployment periods, and substantial manual processing. These practical constraints severely limit the geographic coverage, temporal scope, and contextual diversity of available datasets, ultimately hindering agencies' ability to develop reliable predictive tools that generalize across diverse intersection types and support evidence-based
safety interventions statewide. This project addresses these fundamental challenges by introducing Large Language Models (LLMs) as a novel tool to generate high-quality synthetic pedestrian–vehicle interaction data. LLMs possess extensive pre-trained knowledge spanning transportation systems, human behavior, and urban
environments, successfully demonstrated in healthcare and climate science for data augmentation. Building upon the Minnesota Traffic Observatory (MTO) dataset, where 18 intersections with 3,314 interactions involving 4,941 pedestrians, the research team will develop a validated methodology to generate contextually realistic scenarios incorporating roadway geometry, traffic control, land use, pedestrian demographics, and temporal patterns. This approach directly tackles the data scarcity problem that prevents agencies from conducting comprehensive pedestrian safety analyses across their jurisdictions.
The project has three objectives: (1) develop a transparent LLM-based synthetic transportation-targeted data generation methodology with validation protocols ensuring realism and quality; (2) evaluate whether synthetic data-augmented models improve prediction accuracy and transferability across intersections compared to observational data alone, using precision-recall AUC, calibration diagnostics, leave-one-site-out validation and other appropriate approaches; and (3) determine the mechanisms driving performance improvements: whether from introducing realistic scenario diversity or addressing rare-event limitations, to guide best practices. The framework will incorporate probability calibration, split-conformal risk control, and decision-curve analysis to deliver deployment-ready tools with quantified uncertainty for operational use.]]></description>
      <pubDate>Tue, 03 Feb 2026 15:34:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663604</guid>
    </item>
    <item>
      <title>Spatio Temporal Graph Learning for Real Time Pedestrian Exposure Estimation</title>
      <link>https://rip.trb.org/View/2640189</link>
      <description><![CDATA[Pedestrian crashes occur infrequently and are often underreported, which makes it difficult for agencies to rely only on crash records when assessing safety. Traditional Safety Performance Functions do not capture short term patterns or local context, and therefore cannot fully represent changes in pedestrian activity. This project will create a new framework that uses spatio temporal graph neural networks combined with statistical modeling to estimate pedestrian exposure across different locations and time periods. The research will draw from computer vision systems, Streetlight data, manual counts, roadway characteristics, land use, and travel related factors to produce high resolution exposure estimates.

The modeling framework will include two tiers. The first tier will use generalized linear mixed models to build a baseline exposure structure, while the second tier will apply deep learning methods to capture spatial spillover effects and temporal variation such as peak periods and seasonal changes. The results will help agencies identify areas with elevated pedestrian activity and evaluate how different roadway or land use conditions influence exposure. These data will support improved pedestrian safety analysis and guide the development of timely, evidence based interventions.]]></description>
      <pubDate>Thu, 11 Dec 2025 13:45:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2640189</guid>
    </item>
    <item>
      <title>Calibration and Implementation of Highway Safety Manual Bicyclist and Pedestrian Intersection Crash Prediction Models</title>
      <link>https://rip.trb.org/View/2635921</link>
      <description><![CDATA[The forthcoming 2nd Edition of the AASHTO Highway Safety Manual (HSM2) will introduce dedicated crash prediction models (CPMs) for pedestrian and bicyclist crashes at intersections, midblock crossings, and roadway segments.  The goal of this research is to calibrate the HSM2 pedestrian and bicyclist intersection CPMs using Virginia-specific data. The research outcomes will enhance the accuracy of nonmotorized crash predictions and support Virginia Department of Transportation's (VDOT’s) broader goals of data-driven planning, design decision-making, and funding prioritization for safety improvements. To achieve this goal, the research will (1) assemble a comprehensive dataset for selected representative intersections in Virginia, including crash history, exposure data, and roadway and roadside design features required by the HSM2 CPMs, (2) develop appropriate methods for estimating pedestrian and bicyclist exposure at intersections, considering available data sources, (3) develop a robust calibration methodology that accounts for the variability of contextual settings, exposure ranges, facility types and jurisdictions, etc., and (4) design a practical tool and accompanying guidance to help VDOT implement and maintain the calibrated pedestrian and bicyclist CPMs.]]></description>
      <pubDate>Thu, 04 Dec 2025 08:52:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2635921</guid>
    </item>
    <item>
      <title>Exposing the Dangers of Distance: Mining Crash Narratives to Explore Why Pedestrians Face Severe Injury and Death Far From Home</title>
      <link>https://rip.trb.org/View/2625590</link>
      <description><![CDATA[Pedestrian fatalities in the United States have risen by 83% over the past 15 years, with much of the increase occurring on multilane suburban arterials. In Tennessee, deaths nearly tripled between 2009 and 2022, with studies linking crashes to high-speed midblock locations lacking pedestrian infrastructure. Spatial analysis shows pedestrians are being struck farther from home: in 2014 the median distance between residence and crash site was 1.5 miles, compared to four miles by 2023, while the share of crashes within one mile of home fell from 46% to 30%. Relative to city centers, crash locations remain stable, but the distance between city centers and pedestrian residences has grown, indicating that more crashes involve individuals living farther from urban cores. These shifts suggest pedestrians are traveling into distant, high-risk environments, raising essential questions about why they are walking in such areas and what broader urban trends contribute to this exposure. This study applies a hybrid methodology combining structured crash records with insights from unstructured police narratives from Tennessee’s Integrated Traffic Analysis Network (2014–2024). A home-based approach links pedestrian and driver addresses with U.S. Census block group characteristics, including income levels, vehicle ownership, education, commuting modes, and housing density, to better understand who is involved in these crashes. Artificial intelligence is used to analyze crash narratives for trip purposes such as traveling to grocery stores, bus stops, schools, or workplaces, offering contextual information not captured in standard crash reports. Specifically, this study will locally deploy an open-source large language model (e.g., Gemma or Grok) to serve as a traffic crash analysis agent, capable of addressing questions that help uncover the motivations behind pedestrian trips based on police crash narratives. By conducting all processing locally, this approach ensures the privacy of both pedestrians and drivers is preserved. Together, these methods distinguish between near-home and far-from-home crashes, highlight populations more frequently affected, and examine the role of broader urban development patterns. The findings will support city- and neighborhood-level safety strategies, helping target interventions on hazardous arterials and informing policies for improved safety.]]></description>
      <pubDate>Mon, 17 Nov 2025 16:49:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625590</guid>
    </item>
    <item>
      <title>Pedestrian Crashes at High-Speed Intersections: Applying AI for Crash Narrative Analysis to Identify Common and Edge Cases</title>
      <link>https://rip.trb.org/View/2625591</link>
      <description><![CDATA[High-speed intersections, with speed limits of 35 mph or higher, and long crossings are particularly risky for pedestrians. Tragically, in 2022, 983 pedestrians were killed at signalized intersections, representing about 16% of all pedestrian fatalities. Intersections are concerning due to pedestrians' difficulty navigating them and the numerous conflict points they present. The proposed research addresses a critical gap: understanding pedestrian crash descriptors using structured and unstructured (narrative) data on high-speed intersections and identifying simple and complex (or edge) cases—unusual or extreme crashes that deviate substantially from the typical ones. Complex cases represent exceptional circumstances with many contributing factors. Understanding them will help inform current pedestrian safety strategies and help improve how autonomous vehicle algorithms anticipate potential pedestrian conflicts.  The research question is: What are the different types of pedestrian crashes and injuries at intersections, and what are their complexity levels?  To answer this question, the team will use data from crashes from Tennessee’s Integrated Traffic Analysis Network (TITAN) and Wisconsin’s WisTransPortal, for which the research team has access to police crash narratives. The team will separate pedestrian crashes at high-speed signalized intersections (with speed limits of 35 mph or higher) and compare them to those at intersections with speed limits of less than 35 mph. The team will use AI to develop high-quality, detailed crash descriptors from the narratives of police reports and quantitative crash data. Natural language processing and feature extraction techniques will categorize pedestrian crashes into specific types based on detailed pre-crash actions, human errors, and circumstances obtained from structured and unstructured data. The study will identify edge cases and relevant safety countermeasures (e.g., conflict reductions) while providing a nuanced understanding of crash circumstances (relative to current practice).   The study will create a unique and comprehensive crash database that can provide deep insights into the range of injuries, crash attributes (e.g., crash location within the intersection or pedestrian and driver actions), precrash positions, driver and pedestrian impairment, and roadway conditions, and design (e.g., visibility, number of lanes, pedestrian crossing facilities). The study will apply rigorous analysis methods, including unsupervised learning techniques to identify complex cases and inference-based frequentist methods to quantify key correlates of crash injuries. Cluster analysis, specifically through hierarchical or k-means techniques, will differentiate complex crash cases from more common ones, effectively isolating extreme cases deviating from typical patterns.   In addition to highlighting the issue of pedestrian crashes at intersections and their correlates, a unique aspect of this study is the identification of complex cases. By doing so, the study aims to uncover the underlying patterns and risk factors that contribute to complex and unusual pedestrian crashes at intersections. Rather than focusing solely on the common crash situations, considering a wide range of possibilities and using a unique database helps to understand and address common and rare cases for high-speed and low/medium-speed intersections (especially relevant, given the adoption of vehicle automation and higher safety standards), ensuring a safer environment for vulnerable road users.]]></description>
      <pubDate>Mon, 17 Nov 2025 16:29:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625591</guid>
    </item>
    <item>
      <title>Mining Police Crash Report Narratives: A Natural Language Processing Approach to Identify Bus-Stop Related Crashes</title>
      <link>https://rip.trb.org/View/2625594</link>
      <description><![CDATA[Transit riders are a particularly vulnerable population, as they often walk to and from bus stops, wait in areas where multiple transportation modes interact, and cross the road at least once during a round trip. Prior studies have identified a significant relationship between transit elements (i.e., stops, corridors, and ridership levels) and pedestrian crash locations. National databases like the Fatality Analysis Reporting System (FARS) reported 196 transit bus stop-related pedestrian crashes (2014–2022), while the Crash Report Sampling System (CRSS) reported 93 (2016–2022). This small sample appears to contradict rising pedestrian crash trends in the U.S., suggesting potential underreporting due to inconsistent definitions, lack of standardized fields for transit bus stop-related crashes, or variation in how crashes are coded. To address this gap, artificial intelligence methods like natural language processing (NLP), specifically named entity recognition (NER), can extract transit bus stop-related details from police crash report narratives. NER will be applied to Minnesota and Tennessee datasets to identify such crashes. The model will be trained, validated, and tested for generalizability using metrics like precision and recall. Results will be cross-analyzed with national databases (FARS, CRSS) to test the hypothesis that transit bus stop-related crashes are underreported. Misclassified cases will also be analyzed to identify patterns. While NER has been widely used to improve crash data quality, it has not been applied to identify transit bus stop-related crashes specifically. This approach could streamline data collection, reduce manual review time, and enhance the accuracy of pedestrian crash data. By addressing a critical gap in crash reporting, this work will improve the ability to study risks faced by transit riders and inform safety improvements at bus stops.]]></description>
      <pubDate>Mon, 17 Nov 2025 15:03:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625594</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>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.

The objective of this research is to develop a guide for SHSOs and other stakeholders that:
(1) Quantifies the effects of ASE on crashes involving pedestrians, bicyclists, and other nonmotorized users; (2) Examines how roadway context and related factors influence the safety impacts of ASE; and (3) 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>Impacts of Turn Lanes on Speed and Crashes Involving Vulnerable Road Users in Urban Areas


</title>
      <link>https://rip.trb.org/View/2558380</link>
      <description><![CDATA[Turn lanes, one of the Federal Highway Administration’s (FHWA’s) proven safety countermeasures for motor vehicles, are recommended to reduce the risk of collisions involving vehicles turning left across opposing through traffic and rear-end collisions. However, a preliminary analysis of 2017–2024 crash data from Washington State indicated that pedestrian-involved fatal and serious injury crashes occurred more frequently at intersections with left turn lanes, and at intersections with both right and left turn lanes, compared to intersections with no turn lanes.

This raises important questions about how turn lanes influence intersection safety for all road users: What effect do turn lanes have on operating speeds at the intersection and along the broader corridor? How do turn lanes impact the risk of fatal and serious injury crashes for vulnerable road users (VRUs)?

While turn lanes may improve network efficiency and reduce vehicle conflicts, they may diminish a traffic calming feature that has a wider effect on vehicle operating speeds. Turn lanes also may influence VRUs’ conspicuity and predictability, increasing crossing distances, and creating additional vehicle–VRU conflict points through altered temporal and spatial separation between modes. Research is needed to quantify these effects, evaluate turn lanes (including their effects on crash exposure, likelihood, and severity), and provide actionable recommendations.

OBJECTIVE: The objective of this research is to quantify impacts of turn lanes on vehicle operating speeds and VRU fatal and serious injury crashes in urban areas.]]></description>
      <pubDate>Wed, 28 May 2025 14:15:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2558380</guid>
    </item>
    <item>
      <title>Development of an arterial-based pedestrian exposure and crash risk model for North Carolina</title>
      <link>https://rip.trb.org/View/2536233</link>
      <description><![CDATA[Pedestrian safety is a significant concern for transportation planners and safety engineers both within North Carolina and across the country. While a relatively small number of trips are made via walking and pedestrians were involved in less than 1% of crashes in North Carolina, pedestrians represented roughly 15% of traffic fatalities or serious injuries; further, pedestrian fatalities have continued to increase in the state, with a peak total of 265 in 2022. More broadly across the United States, pedestrian fatalities are increasing annually; in fact, annual pedestrian fatalities within the U.S. claimed the lives of more than 7,500 people in 2021, the highest in over forty years. To better identify factors that contribute to pedestrian fatalities/injuries and identify the highest risk locations for these crashes, it is critical to understand which locations have the most pedestrian activity so that the most risk-prone locations can be identified.

As part of research report 2022-057A: Quantification of Systemic Risk Factors for Pedestrian Safety on North Carolina, the North Carolina Department of Transportation (NCDOT) discovered several risk factors that were most associated with serious injuries and fatalities among pedestrians involved in a crash on the roadway. However, these risk factors did not include a critical aspect of pedestrian crash risk – pedestrian activity or exposure – due to a lack of available data. The research collected traffic counts from NCDOT and local governments, but many of these counts reflected sites selected for standard motor vehicle turning counts; further, these sites were not selected to understand the dynamics that influence pedestrian activity, but rather as a secondary component of a more traditional traffic study.

The overall objective of this project is to identify the most critical locations that pedestrian counts are needed to support the estimation of a more reliable pedestrian exposure model for arterials and obtain pedestrian exposure information from these locations. These counts will then be integrated into an updated pedestrian exposure model that will be used to better quantify pedestrian risk on arterials within NCDOT (both individual roadway segments and intersections) to identify the highest risk locations for pedestrian crashes. Both the exposure and risk models will be built using publicly available data on land use, roadway features, speed limits, as well as bicycle and pedestrian infrastructure locations available through the Pedestrian and Bicycle Infrastructure Network (PBIN) dataset. As a part of this project, specific location types that are underrepresented in terms of available pedestrian counts will be identified so that additional pedestrian counts can be performed to supplement the available data and improve model accuracy/reliability. The final risk factors identified through this effort should be applicable to the highly diverse spectrum of environmental contexts and communities that make up North Carolina.

The primary products of this research would be: (1) additional pedestrian exposure counts at critical representative locations along arterials within North Carolina; (2) an updated pedestrian exposure model that can be used to predict the level of pedestrian activity at individual intersections (and applied to adjacent segments) along arterials in North Carolina; and, (3) a suite of models to estimate the level of risk of a pedestrian crash at individual roadway segments and intersections in North Carolina. In addition to
the models themselves, the research team will provide a suite of geographic information system (GIS) map layers that have the pedestrian exposure and risk models implemented directly. Specific sites will be identified as discussed with the NCDOT technical panel; e.g., top 1% or 10% risky sites and top 1% or 10% sites with the highest anticipated pedestrian exposure. In addition, the research team will provide guidance on how these models can be used as a part of systemic pedestrian safety analysis in North Carolina, focusing on the identification and use of pedestrian risk factors along arterials in urban areas. This guidance document will be a standalone document, separate from the final research report. The guidance document will include a summary of the process followed to develop the systemic analysis model and possible opportunities for future analysis model updates. Case studies or example corridor profiles will be used to demonstrate how the models can be applied and what to look for outside of what information the models can provide, such as local land uses, pedestrian generators, and evidence of disadvantaged populations.]]></description>
      <pubDate>Fri, 11 Apr 2025 01:43:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2536233</guid>
    </item>
    <item>
      <title>Enhancing the Safety of Georgia’s Senior Drivers and Pedestrians by Analyzing Crash Characteristics and Behavior</title>
      <link>https://rip.trb.org/View/2508953</link>
      <description><![CDATA[The objectives of this research are to enhance senior driving and pedestrian safety by identifying the types of collisions and contributing factors involving senior drivers and pedestrians. The research will lead to recommendations for the most effective countermeasures for improving senior driver performance and assisting senior pedestrians. ]]></description>
      <pubDate>Wed, 12 Feb 2025 07:20:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2508953</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>Identify and Evaluate Innovative Pedestrian Safety Countermeasures for Rural and Nighttime Environments</title>
      <link>https://rip.trb.org/View/2420076</link>
      <description><![CDATA[The research team will investigate effective countermeasures to reduce nighttime pedestrian crashes across the state of Texas. A systems-oriented approach will be central to the analysis, recognizing that pedestrian safety involves infrastructure, driver behavior, and pedestrian actions. This approach moves beyond assigning blame to individuals and focuses on collective responsibility for creating a safer environment. This work will focus on nighttime-specific pedestrian crossing safety countermeasures by identifying and evaluating the costs and feasibility of implementation. Based on experiences in other cities and communities, this work will identify high-interest pedestrian safety crossing treatments. The research team will develop a targeted, prioritized list of potential benefits, cost, considerations, and gaps in knowledge about these treatments.]]></description>
      <pubDate>Thu, 22 Aug 2024 16:43:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2420076</guid>
    </item>
    <item>
      <title>Pedestrian Level of Traffic Stress (PLTS) Application and Validation</title>
      <link>https://rip.trb.org/View/2401757</link>
      <description><![CDATA[Many of the existing methods to evaluate pedestrian and bicyclist suitability require a large number of inputs, some of which are not available in typical roadway inventory data (e.g., pavement condition, on-street parking coverage, heavy vehicle proportion), making them impractical for most agencies to apply. Some of these methods also require statistical modeling expertise or specialized software to run, further putting them out of reach for many agencies. Occasionally, their outputs do not make intuitive sense. The Year 1 
Center for Pedestrian and Bicyclist Safety (CPBS) project created a well-researched, standardized version of a table-based, Pedestrian Level of Traffic Stress (PLTS) tool. It incorporates many of the most important and easy-to-collect roadway factors associated with pedestrian suitability from a) existing pedestrian suitability methods and b) the pedestrian safety literature. This Year 2 project will build on the previous effort to apply the method in at least two case study communities (including the City of Milwaukee, Wisconsin) and validate the PLTS categories in a sample of locations against real pedestrian stress ratings from public surveys and police-reported pedestrian crash data. The goal is to establish a validated, practical PLTS method that agencies across the country can use to estimate suitability and stress for pedestrians in various contexts, ultimately leading to safer and more enjoyable walking and rolling conditions.  

As done for the BLTS in 2012, the research team will produce a final technical report that includes a description of the PLTS method. This report will include PLTS tables and example PLTS maps from communities where the method has been tested. The final report will discuss how well the PLTS method works for practitioners and matches with public perceptions of pedestrian stress and pedestrian crash locations.  As done for the BLTS in 2012, the research team will produce a final technical report that includes a description of the PLTS method. This report will include PLTS tables and example PLTS maps from communities where the method has been tested. The final report will discuss how well the PLTS method works for practitioners and matches with public perceptions of pedestrian stress and pedestrian crash locations.]]></description>
      <pubDate>Mon, 08 Jul 2024 14:54:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2401757</guid>
    </item>
    <item>
      <title>Mid-Block Pedestrian Crossing Exposure: Count Protocol and Database</title>
      <link>https://rip.trb.org/View/2401756</link>
      <description><![CDATA[Nearly 80% of US pedestrian fatalities occur at mid-block locations, away from intersections. Despite the problem of the most serious pedestrian crashes occurring at mid-block locations, very few transportation researchers and agencies have collected pedestrian crossing counts at these locations. Therefore, the traffic safety profession has almost no understanding of pedestrian exposure at mid-block crossing locations. This prevents researchers and agencies from calculating pedestrian crash rates and therefore understanding which roadway and adjacent land use characteristics may produce the greatest risk at these crucial locations. This pilot project will be conducted in the City of Milwaukee, WI and will explore the following research questions: 1) What are the most effective methods to collect mid-block crossing counts? 2) What roadway, adjacent land use, and other contextual characteristics can be collected efficiently and included in a database of mid-block crossing counts? 3) What characteristics are associated with pedestrian mid-block crossing crash rates? Answering these questions will also help provide the foundation to eventually explore which roadway, adjacent land use, and other contextual characteristics are associated with mid-block pedestrian crossing volumes. This can lead to mid-block pedestrian crossing volume models and predictive models (safety performance functions) for mid-block pedestrian crossing crashes.]]></description>
      <pubDate>Mon, 08 Jul 2024 14:54:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2401756</guid>
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