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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
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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>Leveraging Emerging Data for Traffic Safety Analyses</title>
      <link>https://rip.trb.org/View/2685697</link>
      <description><![CDATA[This project will leverage emerging large-scale vehicle trajectory data to help identify high-risk roadway segments. The analysis will focus on leveraging surrogate safety indicators extracted directly from vehicle movement patterns. Key indicators such as abrupt speed changes, harsh acceleration or braking, and irregular motion signatures are used as proxies for operational risk. These indicators will be aggregated at the roadway-segment level and compared with the “traditional” crash data and crash outcomes on the KABCO scale. This is to help proactively and more quickly identify roadway locations that pose a higher potential safety risk based on data from driving behaviors.
Project efforts will address technical workflows for handling high-volume trajectory data, including data preparation, event extraction, spatial segmentation, and identification of behavior-based patterns. This work aims to develop a structured approach for highlighting rural segments with surrogate safety risk indicators of elevated operational risk based on trajectory-derived metrics.
As a case study, the efforts will use a dataset obtained a data aggregator for portions of the state of Nevada. The dataset contains over a billion trajectories recorded from millions of unique trips between June 2024 and June 2025. Each record includes spatial, temporal, and motion-related attributes, offering a high-resolution view of driving behavior on roadways. These data can be obtained within days or weeks compared to traditional crash data which typically takes many months to obtain.
The outputs of this project include the illustration of the exploratory use of large-scale vehicle trajectory data to identify high-risk roadway segments, and the development of a structured approach to highlight road segments with surrogate safety risk indicators of elevated operational risk based on trajectory-derived metric.
This work will highlight how high-resolution telematics data can support early identification of potential safety concerns on road networks. These insights can assist transportation and law enforcement agencies to identify parts of the road network for design and operations review considerations, prioritize law enforcement priorities and practices, allocate resources efficiently, and strengthen data-driven safety management practices. This could also help effect changes in policies, programs, procedures, and practices to improve traffic safety outcomes such as reduce fatalities and injuries.
]]></description>
      <pubDate>Sun, 29 Mar 2026 18:53:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2685697</guid>
    </item>
    <item>
      <title>Enhancing Utah's Rest Area Safety Through AI-Based Near-Miss Detection and Risk Mapping</title>
      <link>https://rip.trb.org/View/2672765</link>
      <description><![CDATA[State-maintained rest areas are facing increasing safety risks as infrastructure ages and traveler demand grows, raising concerns about both user well-being and operational efficiency. Utilizing open-source data and video from surveillance cameras in rest areas managed by the Utah Department of Transportation (UDOT), this project proposes to incorporate a safety-focused analysis layer into rest area management in Utah. The research team will develop computer vision pipelines for multi-object tracking and scene calibration to extract vehicle and pedestrian precise trajectories and speeds, then calculate surrogate safety measures to identify near-miss interactions in parking areas and walkways. These metrics will be integrated into spatiotemporal hotspot maps and a site-level Safety Performance Index (SPI) that ranks locations and time periods with the highest safety risks. The SPI will guide a risk-based shortlist of targeted countermeasures, including signage enhancements, speed-calming features, bollards, and lighting upgrades, each accompanied by projected risk reduction and cost estimates to support informed decision-making.]]></description>
      <pubDate>Sun, 22 Feb 2026 10:38:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2672765</guid>
    </item>
    <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>Development of a Network-Level Data-Driven High Friction Surface Treatment Location Selection Approach Leveraging Remote Sensing Technologies</title>
      <link>https://rip.trb.org/View/2643023</link>
      <description><![CDATA[High Friction Surface Treatment (HFST) is an effective countermeasure for reducing crashes at horizontal curves, yet current site selection practices rely heavily on historical crash data and manual field inspections. These approaches limit agencies’ ability to proactively identify high-risk locations and efficiently allocate limited safety resources. This project addresses these limitations by developing a scalable, data-driven framework for HFST site prioritization at the network level.

The research will create an automated data-processing pipeline that extracts roadway geometry and surface characteristics from mobile LiDAR and video log imagery, including curve radius, superelevation, signage, and surface condition. These features will be integrated with pavement condition and crash data to identify high-risk and constructible HFST locations. The approach will be validated through a case study using Massachusetts Department of Transportation (MassDOT) roadway and crash data. Results will provide transportation agencies with a transferable methodology for proactive HFST deployment, improving safety outcomes and supporting more efficient infrastructure management.]]></description>
      <pubDate>Thu, 18 Dec 2025 14:36:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2643023</guid>
    </item>
    <item>
      <title>High Friction Surface Treatment Decision-Making Criteria </title>
      <link>https://rip.trb.org/View/2640691</link>
      <description><![CDATA[Between the years of 2019 and 2023, there were 203,662 run-off-road (ROR) crashes in Missouri resulting in 1,983 fatalities and 9,428 severe injuries. One safety countermeasure used by state departments of transportation (DOT) to address ROR crashes is called a high friction surface treatment (HFST). The goal of this research project is to provide the Missouri Department of Transportation (MoDOT) with criteria to help proactively identify locations for potential friction treatment applications. An ideal situation would be to calculate the minimum friction at a given curve location and have a list of friction treatment options (e.g., HFST, superelevation adjustments, curve realignment, and others). These criteria would be incorporated into MoDOT’s Engineering Policy Guide (EPG).]]></description>
      <pubDate>Tue, 16 Dec 2025 09:20:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2640691</guid>
    </item>
    <item>
      <title>Hotspot Stability of Freight Vehicle Crashes Involving Vulnerable Road Users: A Spatio-Temporal Perspective</title>
      <link>https://rip.trb.org/View/2625586</link>
      <description><![CDATA[This research will analyze the interaction between two of the most different transportation road users that interact on roads—freight vehicles and vulnerable road users (VRU), i.e., pedestrians and bicyclists. The research objective of this project is to identify the temporal stability of hotspots in (1) non-fatal crashes, (2) fatal crashes, and (3) all crashes (non-fatal and fatal) between freight vehicles and VRU in two U.S. States. This research proposes a novel spatiotemporal analysis to answer whether crash hotspots intensify over time (i.e., the number of crashes increases over time at the same location) or if it stays the same over time.  In terms of processes, the first one is collecting the data on fatal, non-fatal, and all crashes of both States into a single file, cleaning it, and ensuring its validity/accuracy/consistency. Once the data collection is ready, the second process focuses on merging the panel data into a space-time cube. This arrangement will host on a single data array geographical and temporal data of the total number of (1) non-fatal crashes, (2) fatal crashes, and (3) all crashes between freight vehicles and VRU for each State. The third process is calculating a Local Indicator of Spatial Association Statistic (the Gettis Ord*) to identify crash hotspot locations for each year of analysis for each State, and estimate emerging hotspot patterns based on the panel data results. The fourth process will use crash hotspot locations (identified in process three) and data from the County Business Pattern data, the Census Tract Data, and the American Community Survey to compare urban economic and built environment characteristics between different types of hotspots (e.g., recent versus consecutive hotspots), and identify common factors and differences. Specifically, the research team will compute an ANOVA and a post hoc test to identify statistical differences between crash hotspot locations. The last process focuses on visualizing the results on a geographic information system (GIS) software or tables for statistical analysis.  The results of the spatiotemporal analysis will be correlated with urban economic and built environment features to identify common factors in hotspot locations that could have influenced road crashes in both States. These factors include built environment attributes and the number of establishments by industry sector, among others.]]></description>
      <pubDate>Tue, 18 Nov 2025 14:19:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625586</guid>
    </item>
    <item>
      <title>Quantitative Assessment of Anti-Icing Efficacy on Highway Surfaces Using Light Reflectance</title>
      <link>https://rip.trb.org/View/2620722</link>
      <description><![CDATA[The project aims to quantify road surface condition under adverse weather in North Dakota. First, the current winter maintenance and winter road condition monitoring practices across North Dakota will be reviewed. Historical crash data will be used to identify high-risk roadway segments encountering elevated crash frequencies and/or severities related to adverse road surface conditions. The project aims to quantify the safety performance to estimate and predict crash occurrences related to adverse weather conditions using statistical modeling techniques and network screening analysis. Once the importance of road surface condition on safety is quantify, type of surface condition (ice, snow, slush) on highways using diffuse reflectance spectroscopy (DRS) will be identified. A physical model for near-infrared reflectance of road surface will be developed to classify the surface condition in a noncontact manner. The models will be developed under controlled conditions and will be evaluated under field condition by determination of the optical properties of water, snow, and ice (and black ice). In the next objective, DRS-based discriminative to measure brine eutectic point and efficacy will be developed by generating samples with different portions of water, salt, and beet juice (primarily used in North Dakota) under different temperature. Through noncontact quantification of deicer efficacy, the project contributes to preservation of transportation infrastructure and safety. The research team will develop deterministic and data-driven models to correlate DRS features under different brine conditions with respect to eutectic points in a controlled environment, and benchmarking and compare the results of DRS brine models with observations. The final task of this objective is to develop a low-cost in-situ optical sensor framework for field deployment using diode lasers, super luminescent LEDs, hyperspectral camera, single-pixel and array photodiodes, and spectral filters. The payload SWaP (size, weight and power) analysis for potential Unmanned Aerial Systems (UAS) applications. The research team will establish an UAS operation training program for students to get UAS licensed, to shadow UAS-assisted inspections, to analyze UAS data.]]></description>
      <pubDate>Mon, 10 Nov 2025 09:43:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2620722</guid>
    </item>
    <item>
      <title>AI-Driven Telematics Solutions for Detecting Near-Crash Events and Safety Hotspots in Texas Transportation Networks</title>
      <link>https://rip.trb.org/View/2606398</link>
      <description><![CDATA[The research team will leverage telematics data to proactively identify near-crash events and hotspots, strengthening transportation safety management across Texas. The work will advance analytical methodologies in four areas: (1) trajectory-based analysis of vehicle movement patterns; (2) event-based analysis of critical driving behaviours such as hard braking and abrupt manoeuvres; (3) multi-criteria analysis integrating mobility, safety, and environmental performance; and (4) data fusion techniques that combine telematics with other sources, including traffic sensors and historical crash records. Building on this foundation, the research team will apply spatial-temporal analyses and machine learning predictive models to detect current and forecast future near-crash hotspots. An interactive artificial intelligence (AI)-powered decision-support system will be developed to provide transportation agencies with actionable insights for targeted safety interventions. Rural and urban case studies will demonstrate the platform’s applicability and validate its effectiveness through comparisons with historical crash data. An implementation roadmap will guide integration into agency safety management practices. The project will deliver robust analytical tools and evidence-based recommendations that can be seamlessly integrated with existing platforms—such as geographic information system (GIS) systems, roadway networks, and performance dashboards— ensuring compatibility and significantly enhancing proactive traffic safety measures statewide.]]></description>
      <pubDate>Thu, 02 Oct 2025 09:44:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2606398</guid>
    </item>
    <item>
      <title>Proactive Strategy For Climate Resilient Corridors: Landslide Hotspot Prioritization, Planning, And Management Tool</title>
      <link>https://rip.trb.org/View/2594026</link>
      <description><![CDATA[Landslides are frequent hazards that affect Oregon highway infrastructure, resulting in negative economic, environmental, and social impacts for Oregon communities. Goals from the Oregon Transportation Plan (OTP) include proactive preparation of lifeline routes to reduce possible hazards before events occur, starting with the strategy to map and assess multi-hazard threats to the transportation system. While Oregon Department of Transportation's (ODOT's) Climate Adaptation and Resilience Roadmap and Climate Hazard Risk Map identify resilience corridors to help prioritize investment, a higher resolution analysis to prioritize between sites along these corridors is needed for development of compelling business cases for investment and competitive funding opportunities. ODOT is already under financial strain reacting to landslide hazards as they happen. Given that these hazards are projected to increase in frequency/magnitude with climate change, reactive approaches for returning to site functionality will exacerbate ODOT’s fiscally constrained reality. High resolution landslide hazard site vulnerability analysis followed by site prioritization will provide planning and management teams practical science-based investment strategies aimed at improving safety, ensuring working emergency lifeline routes, preventing community isolation, and reducing rising maintenance costs for hazard removal.]]></description>
      <pubDate>Thu, 28 Aug 2025 15:56:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2594026</guid>
    </item>
    <item>
      <title>Multimodal 3D Perception System for Active Safety at Accident-Prone Locations</title>
      <link>https://rip.trb.org/View/2562265</link>
      <description><![CDATA[Accident-prone intersections in Michigan continue to account for a disproportionate share of severe crashes, highlighting the need for infrastructure-based active safety capabilities that can detect, anticipate, and mitigate imminent conflicts in real time. This project develops and demonstrates a full-stack roadside sensing and warning system that integrates LiDAR and wide-area cameras with edge computing, cloud-based data management, and V2X communications. By fusing complementary sensor modalities using Bird’s Eye View (BEV) fusion and conflict prediction, the system aims to improve detection accuracy, 3D localization, robustness under varied conditions, and early identification of potential collisions. The resulting system will generate timely, targeted safety warnings via roadside units (RSUs) and will be validated through staged data collection, model training, and controlled field testing at Mcity, establishing a scalable technical foundation for future deployment at high-risk intersections and similar safety-critical locations.

]]></description>
      <pubDate>Fri, 06 Jun 2025 14:48:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2562265</guid>
    </item>
    <item>
      <title>Exploring Post-Crash Care with EMS Response to Impaired Driving Crashes in North Dakota</title>
      <link>https://rip.trb.org/View/2553706</link>
      <description><![CDATA[Alcohol- and drug-impaired driving leads to severe crashes in North Dakota, yet police crash reports lack critical EMS response and patient care data. This study leverages NEMSIS data to assess EMS response times, treatment quality, and patient outcomes for impaired driving crashes. Using statistical analysis, time-series trends, and spatial mapping, the research identifies delays, care disparities, and high-risk locations. Findings will inform EMS resource allocation and improve post-crash care strategies, aligning with USDOT’s safety goals through advanced analytics that will transform foundational knowledge in this space.]]></description>
      <pubDate>Thu, 15 May 2025 15:13:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553706</guid>
    </item>
    <item>
      <title>RES2023-30: I-24 Smart Corridor</title>
      <link>https://rip.trb.org/View/2539923</link>
      <description><![CDATA[The I-24 SMART Corridor takes a comprehensive approach to improving the safety and travel time reliability along the corridor utilizing existing infrastructure and emerging technology. Vehicle-to-Everything (V2X) technologies are a key initiative of 
Tennessee Department of Transportation (TDOT) by aligning with several strategic goals of TDOT including safety, mobility, sustainability, and consistent customer experience. To achieve the benefits of successfully applied V2X
technologies along the I-24 SMART Corridor, a clearly defined direction of V2X deployments needs to be established. The path towards applying V2X technologies throughout the I-24 SMART Corridor is described within the I-24 SMART Corridor V2X Roadmap. The I-24 SMART Corridor Roadmap provides an evaluation of
the existing Intelligent Transportation Systems (ITS) infrastructure along the corridor as well as an implementation plan for V2X applications that meet the goals of the I-24 SMART Corridor. The initial deployment locations for V2X applications were based on several safety factors including existing traffic volumes, crash history, and reoccurring
congestion. These safety factor hotspots led to the specific V2X application needs along the I-24 SMART Corridor. Along with the hotspots, geometric factors were included in determining which specific V2X applications were most applicable at each hotspot location. In addition to identifying and locating where specific V2X applications should be provided along the I-24 SMART Corridor, the Roadmap provides the costs associated
with implementing these applications. These costs include software, physical integration, vehicular integration, and annual operations and maintenance costs.]]></description>
      <pubDate>Thu, 17 Apr 2025 13:50:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2539923</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>	Identifying Locations with High Wrong-Way Driving Risk and Effective Methods to Reduce Wrong-Way Driving at Non-Conventional Access Points and Construction Zones on Limited Access Roadways</title>
      <link>https://rip.trb.org/View/2526499</link>
      <description><![CDATA[The main goal of this research is to utilize a proven wrong-way driving (WWD) hotspot methodology invented by Professor Al-Deek and his University of Central Florida (UCF) research team to help Florida Department of Transportation (FDOT) identify WWD hotspot roadway segments and individual exits with high WWCR on limited access facilities in District 5, with additional focus on WWD at the various types of NCAPs previously mentioned and in construction zones. Exit ramps, different types of NCAPs, and construction zones have different characteristics, so potential WWD countermeasures specific to each of these locations will be identified, along with the potential for improved data practices to better detect and monitor WWD. This research will allow FDOT to prioritize locations in D5 for future WWD countermeasure deployments or enhancements to existing WWD countermeasures, better understand the characteristics of WWD behavior at NCAPs and construction zones, identify arterial interchanges for investigation, and develop appropriate deployment plans and treatment strategies for any future WWD countermeasure deployments and construction zone operations to proactively reduce WWD on the district’s transportation network in the most effective manner, saving lives and helping achieve FDOT’s goal of Target Zero. The methodology and results from this project could also be applied to other FDOT districts to help them combat the WWD problem.]]></description>
      <pubDate>Thu, 20 Mar 2025 11:25:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2526499</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.
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      <pubDate>Sat, 12 Oct 2024 12:18:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440014</guid>
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