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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Preventative &amp; Proactive National Safe Roads Challenge Program to Improve Driver Behaviours &amp; Tailor Safety Interventions</title>
      <link>https://trid.trb.org/View/2643340</link>
      <description><![CDATA[This Safe Roads Challenge (SRC) project used a mobile app to gamify driver behavior improvement. Participants (1,112 drivers) were assigned to teams and competed in the Great Canadian Driving Games during March-September 2025. Weekly challenges included interventions such as driving tips, motivational messages, and prizes to encourage safe driving. Data was collected on phone handling, acceleration, braking, cornering, steadiness, swerving, and speeding. Two of the project goals were to determine which types of messages and incentives resulted in driver behavior change and to use real-time driver data to identify high-risk locations. Data was used to develop Safety Performance Functions for signalized intersections in Alberta, Canada. Overall, participating in the challenges gradually improved driver behavior.]]></description>
      <pubDate>Tue, 05 May 2026 13:15:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643340</guid>
    </item>
    <item>
      <title>Visualising Blackspot Improvement at Nagpur</title>
      <link>https://trid.trb.org/View/2581525</link>
      <description><![CDATA[Nagpur, the “Orange City” of the country is situated in the state of Maharashtra. Nagpur is one of the fastest economically growing cities and part of the “Smart City Mission”. In this paper, the city of Nagpur has been identified as the test bed to understand and demonstrate the before and after scenarios of black spot improvement through 3-D modelling. In this regard, road crash data obtained from Nagpur Police in the form of First Information Reports (FIRs) was analysed to identify the black spots conforming to the protocol of the Ministry of Road Transport and Highways (MoRT&H). The geometric countermeasures deduced for two out of the 37 locations are illustrated by presenting ‘before’ and ‘after’ scenarios through 3-D Modelling with the primary objective of pictorially depicting the efficacy of the proposed intervention for two out of the 37 locations in Nagpur. The above form of pictorial illustration of the detailed Geometric Design Plan (GDP) for the above black spots will serve as an eye-opener for the relevant stakeholders in terms of undertaking steps towards the implementation of the suggested black spot remedial measures. It is clarified that the overall project termed Intelligent Solutions for Road Safety through Technology and Engineering (iRASTE) includes redesigning the road geometry as well as applying Artificial Intelligence (AI) technology to achieve its aim of reducing road fatalities by at least 50%, safety of pedestrians, and ease in traffic flow. However, this paper aims to highlight the importance of 3-D visualization for the representation of design interventions through which the policymakers, stakeholders and the general public alike can easily understand.]]></description>
      <pubDate>Wed, 29 Apr 2026 16:47:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581525</guid>
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    <item>
      <title>Economic Benefit Assessment of Black Spot Improvements</title>
      <link>https://trid.trb.org/View/2579832</link>
      <description><![CDATA[Road transport remains the most favoured mode of transport for both freight and passenger movement in India. The fast-growing population, exceptional rate of motorization coupled with the ever-growing urbanization have made people vulnerable to frequent road accidents resulting in fatalities, injuries/disabilities. The number of fatalities related to road accidents in India has been on an upward trend for the last two decades. Black spots account for a major portion of the total road accident fatalities for the past 3 years. Hence, it is important to undertake appropriate countermeasures to improve the geometric aspects of such black spot locations in a systematic and cost-effective manner. In this paper, the city of Nagpur has been identified as the test bed to understand the efficacy of the economic benefit assessment of black spot improvement by estimating the impact of the proposed countermeasures for four identified black spots in the Nagpur rad network. In this regard, road crash data obtained from Nagpur Police in the form of First Information Reports (FIRs) were analysed to identify the black spots conforming to the protocol of the Ministry of Road Transport and Highways. The geometric countermeasures deduced for the four most severe black spots are discussed followed by the economic benefit assessment with the primary objective to demonstrate the estimated reduction in fatalities as well as the total number of crashes which can happen due to the proposed intervention at the identified black spots. The methods adopted in this study are benefit-cost ratio method, net present value method and economic internal rate of return method as prescribed by the Indian Roads Congress. As a result of the proposed countermeasures, it is estimated that there is a significant decrease in both fatal and serious injury crashes. It is hoped that the above study will serve as an eye-opener for the relevant stakeholders in terms of undertaking steps towards the implementation of the suggested black spot remedial measures. Apart from the above engineering measures, proper enforcement and road safety campaigns that educate the road users about the potential dangers need to be implemented.]]></description>
      <pubDate>Tue, 28 Apr 2026 16:55:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579832</guid>
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    <item>
      <title>Identifying Road Segments that Bisect Predicted Movement Corridors for Small Priority Species in Virginia</title>
      <link>https://trid.trb.org/View/2696023</link>
      <description><![CDATA[The Virginia General Assembly passed legislation in 2020 requiring the preparation of the Virginia Wildlife Corridor Action Plan. It directed the Virginia Department of Wildlife Resources in collaboration with the Virginia Department of Transportation, the Virginia Department of Conservation and Recreation, and the Virginia Department of Forestry to identify wildlife corridors, identify areas with a high risk of wildlife-vehicle collisions, and recommend wildlife crossing projects intended to promote driver safety and wildlife connectivity. The first version of the Wildlife Corridor Action Plan was released in 2023 and listed several “recommendations for future actions” for its next iteration. Four of these future actions include (1) identifying at-risk terrestrial species, aquatic species, and other species of interest whose corridor needs are not sufficiently addressed by the Wildlife Biodiversity Resilience Corridors; (2) identifying important habitat corridors for these species; (3) identifying Wildlife Crossing Concern Areas (e.g., high-risk road segments) for these at-risk species; and (4) identifying and analyzing non-road human barriers (e.g., land uses) affecting corridor connectivity for the Wildlife Biodiversity Resilience Corridors. The purpose of this study was to advance the objectives of the legislated Wildlife Corridor Action Plan by developing species-specific road risk models and identifying road segments that pose a high risk to small priority species. To identify high-risk road segments, the authors collaborated with 29 species experts to develop maps of “landscape resistance” for 12 state species on the Species of Greatest Conservation Need list. These maps represent how different landscape features are expected to affect species movement—by increasing mortality risk, influencing movement speed, acting as physical barriers, or causing behavioral avoidance. Species-specific connectivity analyses were used to generate maps of predicted movement intensity for each species and to identify areas where roads were expected to cause the greatest reductions in movement. The 12 priority species considered in this analysis varied widely in distribution, resulting in large differences in the spatial extent and configuration of predicted movement corridors. The Mabee’s salamander was unique in having compact movement zones predicted around a small number of extant source populations. For this species, a focused set of priority road segments was identified that may be suitable for site-level assessment and mitigation—such as improving existing culvert passability or adding targeted small-animal passages. Several species, including the bog turtle, wood turtle, diamondback terrapin, mud snake, rainbow snake, and fisher, had much broader but regionally concentrated movement corridors that intersected a modest number of important roads. The prioritization of road segments for these species reflects both proximity to key source habitats and road characteristics associated with high resistance to movement (e.g., high traffic volume and greater road width). For several wide-ranging species (spotted skunk, Alleghany woodrat, spotted turtle, and box turtle), predicted corridors were diffuse and covered large portions of the state. Mitigation efforts for such species should likely focus on roadway design standards (e.g., use of fencing or curbing to funnel animals to existing structures and ensuring underpasses are compatible with small-animal use) rather than prioritizing individual crossing sites. This project provides connectivity map outputs for all 12 priority species as geographic information system raster data. It develops a consistent, repeatable workflow for assessing road impacts and connectivity across multiple taxa in Virginia.]]></description>
      <pubDate>Tue, 28 Apr 2026 12:18:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696023</guid>
    </item>
    <item>
      <title>Geospatial patterns and risk analysis of maritime accidents using GIS and association rules mining</title>
      <link>https://trid.trb.org/View/2638293</link>
      <description><![CDATA[The complexity of coastal shipping networks makes maritime accidents a major challenge. A comprehensive analytical framework is constructed, with Chinese waters selected as the case study. This study utilizes a dataset of maritime accidents reported by the China Maritime Safety Administration (CMSA) from 2016 to 2024. First, a descriptive analysis is conducted to generate the spatial distribution patterns of maritime accidents based on GIS, with the range visualized through coastline buffer analysis. Next, kernel density estimation is applied to examine the dynamic distribution of accident frequency by overall layout, vessel type, and accident type. The Global Moran's I index, combined with hot spot analysis, is innovatively used to introduce a three-dimensional spatio-temporal model of “season, day-night, accident severity.” This model reveals the dynamic spatio-temporal migration and evolution of accidents, and spatio-temporal coupling is used to analyze the spatial correlation between accident frequency and severity. Finally, the FP-Growth algorithm is employed to explore the risk factors of collision accidents from multiple dimensions. This study aims to reveal the multi-scale spatial and temporal variability of coastal accidents and to analyze the high-risk combinations of typical collision accidents, providing methodological support and empirical evidence for maritime safety management strategies.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2638293</guid>
    </item>
    <item>
      <title>Time-series risk forecasting of airport conflict hotspot with SA-GRU</title>
      <link>https://trid.trb.org/View/2663678</link>
      <description><![CDATA[Airport surface operations increasingly confront collision risks from intricate layouts, vehicle-aircraft interactions, and dense mixed traffic flows. This study develops a predictive framework for conflict hotspot identification by integrating topological, network vulnerability, and traffic complexity metrics into a composite risk evaluation system. A hybrid method combining composite weighting and improved TOPSIS first identifies latent hotspots through node-level risk assessments. Temporal risk patterns are then extracted via principal component analysis of hotspot features, with future risk trajectories predicted using a GRU network enhanced by self-attention mechanisms. Validated through Shenzhen Bao'an International Airport simulations, the proposed SA-GRU model reduces RMSE by 9.14–11.55 % against benchmark models (HA/ARIMA/SVR/LSTM/GRU). Analysis reveals significant spatiotemporal variations in hotspot risks, where daily trends show similar risk fluctuation patterns across zones but differ substantially in intensity. High-risk areas dynamically shift across operational phases, emphasizing the necessity of time-sensitive predictions. The framework enables proactive identification of critical conflict zones through predictive risk monitoring, demonstrating practical potential for optimizing airport surface management. By translating multidimensional operational data into actionable safety insights, this methodology supports intelligent decision-making for collision prevention and resource allocation in complex aviation environments, while remaining adaptable to diverse airport configurations.]]></description>
      <pubDate>Fri, 24 Apr 2026 08:55:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663678</guid>
    </item>
    <item>
      <title>The Role of Vehicle Kinematic Volatilities in Expressway Crash Prediction and Analysis: A Two-Part Framework from Road Features to Crashes</title>
      <link>https://trid.trb.org/View/2660565</link>
      <description><![CDATA[To identify high-risk road sections and understand their mechanisms, we develop a two-part framework intermediated by vehicle kinematic volatilities. First, we use Generalized Autoregressive Conditional Heteroscedasticity Models to model volatilities of tri-axial accelerations and angular velocities, and Standard Ordered Logit models to examine how alignment and infrastructure features influence these volatilities. Second, we build an ensemble Soft Voting Classifier that combines Gradient Boosting, eXtreme Gradient Boosting, and Multi-layer Perceptron to predict high- and low-risk sections using kinematic volatility levels. The classifier achieves an accuracy of 0.911 with balanced performance across risk levels. The framework clarifies the role of kinematic volatilities in the causal pathway from inherent risk features to crash occurrence. Feature importance analysis identifies lateral acceleration, yaw rate, longitudinal acceleration, and pitch rate volatilities as critical indicators of high-risk sections. Practically, the framework can be implemented with inertial sensors on instrumented vehicles to screen crash risk along the entire expressway and prioritize high-risk sections for targeted improvement according to feature importance. In vehicle-infrastructure cooperation environments, the framework supports real-time risk monitoring and safety management by transmitting kinematic volatilities and risk alert information between connected vehicles and the control center.]]></description>
      <pubDate>Thu, 23 Apr 2026 13:54:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2660565</guid>
    </item>
    <item>
      <title>Hot Spot Identification for the Deployment of Roadside Units: Install Only Where Lives Are Saved</title>
      <link>https://trid.trb.org/View/2652057</link>
      <description><![CDATA[Roadside units (RSUs) are crucial for improving the safety of connected vehicles, yet their effectiveness heavily depends on strategic deployment. Traditional methods prioritize RSU installation in high-collision-risk areas (hot spots), but these locations may not yield the greatest safety improvements. This paper redefines hot spots as road areas where RSU deployment achieves the greatest collision risk reduction and proposes a cost-effective method for identifying these hot spots. The proposed method not only helps save more lives but also minimizes the identification cost. It reduces both data consumption and identification time compared with traditional approaches. It requires only small-sample real-world data, which are augmented to generate the additional data needed for identification. Additionally, an accelerated sampling strategy helps minimize the time required for the identification process. The simulation results show that this approach achieves a 46.7% greater risk reduction, a 43% reduction in data costs, and a 40% faster identification of hot spots compared with the state-of-the-art method.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652057</guid>
    </item>
    <item>
      <title>Equitable Estimation of Accurate High Injury Networks (HINs) for Vulnerable Road Users</title>
      <link>https://trid.trb.org/View/2692375</link>
      <description><![CDATA[To improve traffic safety, communities first need to know where serious crashes are actually happening. High-Injury Networks are designed to identify these locations, but they are usually built using only police-reported crash data. This research asks: Are we missing crashes—and injuries—by relying on police data alone? The research team first demonstrates that data from Emergency Medical Services and those from official databases differ substantially, and neither data set captures the full extent of collisions in a community. Following that analysis, the research team concluded that a new data source may help complete this. Toward that end, the research team scraped data from PulsePoint in San Francisco to identify potential traffic collisions reported to 911 operators. The 911 call data for San Francisco, when compared with official city crash data sources, shows that several traffic incidents reported on 911 calls do not appear in the city’s official database. Statistical analysis of data from both sources vs. those found in only one of the two reveals patterns by location. Locations in police districts with lower population density (and larger geographical areas) had more 911 call-reported incidents that did not appear in the official database. The demographics of census tracts of the incident’s reported location, such as income, race, and education levels, did not appear to be statistically significant. Based on the findings, the research team provides a framework for complementing collision data with alternative sources beyond the police records in future Vision Zero efforts. The research project also resulted in a process that allows the team to continuously add to the scraped 911 call data, enabling this analysis to continue beyond what is presented in this report. When serious injuries are invisible in the data, they are invisible in safety planning. Integrating and using all available data is critical to ensuring that Vision Zero strategies reflect real-world injury risk and deliver meaningful, life-saving outcomes.]]></description>
      <pubDate>Tue, 21 Apr 2026 16:23:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692375</guid>
    </item>
    <item>
      <title>Investigating the Extent of Wrong-Way Driving Events and Identifying Wrong-Way Driving Hotspot Roadway Segments and Lone Wolf Exits with High Wrong-Way Driving Risk on Idaho High-Speed Roadways</title>
      <link>https://trid.trb.org/View/2693718</link>
      <description><![CDATA[This project investigated the extent and characteristics of wrong-way driving (WWD) on Idaho limited access roadways. WWD crash (WWC) counts have been increasing in recent years, especially on I-84 and I-90. A model was developed to identify WWD hotspot segments by predicting WWCs based on non-crash WWD citations, exit design characteristics, traffic volumes, and area type. An optimization algorithm was then utilized to identify specific exits within and outside of the hotspots for optimal deployment of WWD countermeasures. The optimization algorithm identified the 81 optimal exit ramps for countermeasure deployment and was shown to improve WWD reduction by 60% compared to only deploying countermeasures in the hotspot segments. Based on the findings from this project, it is recommended for the Idaho Transportation Department to prioritize deployment of WWD countermeasures at these 81 optimal ramps, consider lighting and signage improvements at exit ramp terminals and crossing streets, install treatments at crossing roads to reduce left-turning WWD vehicles where possible, increase law enforcement presence on I-90 and at nighttime if possible to reduce WWCs, standardize the reporting practices for WWCs and WWD citations, collect WWD dispatch reports throughout thestate, and conduct additional research on mainline and construction crossover WWCs.]]></description>
      <pubDate>Fri, 17 Apr 2026 08:55:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2693718</guid>
    </item>
    <item>
      <title>Corridor-level and approach-level features associated with arterial wrong-way driving crashes and hotspots in South Florida</title>
      <link>https://trid.trb.org/View/2686116</link>
      <description><![CDATA[Wrong-way driving (WWD) crashes often result in fatalities. Understanding these crashes can help agencies reduce traffic fatalities and get closer to Target Zero. Arterial WWD crashes (AWWCs) are more prevalent than limited-access WWD crashes, but few studies have focused specifically on AWWCs. This paper examines AWWCs using a corridor methodology rather than analyzing segments and intersections separately. By understanding the relationships between AWWC frequency, corridor-level features (geometric design, traffic volumes, signage, medians, and lighting), and approach-level features (signage, lighting, and pavement markings on approaches entering the corridor), agencies can implement appropriate treatments. Among various regression models fitted to the data, a negative binomial model with corridor length as an offset variable was found to outperform other models. One-way corridors, more non-crash WWD computer-aided dispatch events, more through lanes, higher corridor left-turn lane densities, lower vegetation median proportions, and lower lighting overlap proportions were estimated to increase mean AWWC frequency. Ten hotspot corridors were examined in detail. These hotspots were characterized by a high through lane count, high left-turn lane density, and low lighting continuity on both sides. This study aids agencies in identifying factors which influence AWWCs and pinpointing hotspot corridors for targeted safety improvements to reach Target Zero.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686116</guid>
    </item>
    <item>
      <title>Spatial-temporal planning of road traffic speed management mobile resources: Enhancing road traffic safety by optimizing resource utilization</title>
      <link>https://trid.trb.org/View/2680648</link>
      <description><![CDATA[Traffic safety on rural roads in various countries, particularly in developing countries, is a pressing concern, with speeding being a major contributing factor to traffic safety issues and crashes. This study introduces a framework for improving rural road traffic safety through the spatial-temporal planning of mobile traffic safety resources with consideration of fixed ones as part of speed management programs. The framework involves converting traffic data into inputs for an optimization model, which serves as a safety tool for traffic safety decision-makers. This tool indicates the time for mobile resources to visit each location. First, potential locations and their relative shares are determined based on a comprehensive analysis of road crash records, road properties, and fixed speed management resource locations, using the location-allocation model in ArcGIS. Then, the optimization model allocates traffic safety resources, considering both distance and time halo effects, which increase the unpredictability of these resources for drivers. A mathematical tool within the proposed framework is introduced to use mobile traffic safety resources in rural areas. This framework is particularly beneficial for developing countries, where resource allocations are planned solely based on the expertise of local professionals, rather than analytical methods. The results demonstrated through a case study on the Arak-Salafchegan Road show effective allocation and relocation of these resources to hotspot locations over time, considering real-world limitations, halo effects, and resource distribution, to improve rural road traffic safety. The framework offers a novel tool to tackle rural road traffic safety challenges. By integrating historical data analysis, integer programming, and real-world insights, the approach provides a robust solution that can be adopted by traffic authorities to make roadways safer.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680648</guid>
    </item>
    <item>
      <title>Traffic crash data augmentation with multi-type variables using hybrid VAE-Diffusion generative neural networks for enhancing crash frequency modeling</title>
      <link>https://trid.trb.org/View/2684517</link>
      <description><![CDATA[Crash frequency modeling aims to analyze influential factors of crashes to enhance road safety. However, as crashes are inherently rare events, excessive zero observations in crash datasets undermine crash frequency models’ ability to identify high-risk road segments. Existing statistical models are often limited by strict distributional assumptions, while resampling and deep generative methods often distort data representation or struggle with multi-type crash data (count, ordinal, nominal, and real-valued) since these heterogeneous variables follow different statistical distributions and require distinct encoding strategies. This study proposes TabSyn, a hybrid VAE-Diffusion model that transforms multi-type crash data into a continuous latent space through a tokenizer-based embedding and VAE encoding, enabling the model to preserve the inter-variable correlations and underlying data structure. To validate its effectiveness, TabSyn is compared with state-of-the-art generative models (CTGAN, TVAE, GReaT, and StaSy) in synthetic data quality, and integrated with XGBoost for crash frequency prediction against two statistical models (ZIP, GAM-Poisson). Results demonstrate that TabSyn outperforms benchmark methods by achieving the best synthetic data distributional and structural fidelity, and the lowest prediction error, particularly for Top-K high-risk segment identification. This study offers valuable insights for improving crash frequency modeling and traffic safety management through imbalanced data augmentation of multi-type crash data.]]></description>
      <pubDate>Wed, 08 Apr 2026 13:41:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684517</guid>
    </item>
    <item>
      <title>Urban high-risk scenarios for automated vehicle safety testing: A generation and generalization method based on accident data</title>
      <link>https://trid.trb.org/View/2679337</link>
      <description><![CDATA[To improve the effectiveness of automated vehicles (AVs) safety testing and address key issues including distribution bias in road data collection and insufficient coverage of high-risk events, this study adopts a generation and generalization method to establish high-risk scenarios based on the AV Testing – High-Risk City Accident Dataset (AVT-HRCAD). Firstly, Cramer’s V coefficient and eta squared coefficient are employed to identify risk variables that significantly affect accident severity. Secondly, the K-medoids clustering algorithm, iteratively optimized based on Gower distance, generates baseline risk scenarios. Ultimately, a Risk Index (RI) measures risk levels, while the NRPE criterion—assessing Number, Risk, P-value, and Effect—is intended to evaluate and generalize test situations. Principal findings indicate: Nine, seven, and seven key risk variables were identified for expressways, intersections, and road segments, respectively. Ego behavior, target type, collision angle, and lighting conditions consistently emerged as consistently significant risk factors across all three road types. Scenario generalization effectively addressed low-sample/high-severity variables (e.g., three-wheelers), broadening 18 baseline risk scenarios into general-risk, high-frequency-risk, and long-tail high-risk scenarios. A total of 93 urban high-risk test scenarios were established to assess AV capabilities in risk avoidance (across different vehicle types and collision angles), safety distance determination, and distance maintenance. This method provides a more authentic and valuable testing platform for comprehensive AV safety evaluation.]]></description>
      <pubDate>Wed, 08 Apr 2026 13:40:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679337</guid>
    </item>
    <item>
      <title>Runway Incursion Mitigation Fiscal Year 2024 Annual Summary Report</title>
      <link>https://trid.trb.org/View/2685463</link>
      <description><![CDATA[In fiscal year (FY) 2012, the Federal Aviation Administration (FAA) Office of Airports (ARP) initiated a research study to identify and geographically locate areas at airports with nonstandard taxiway geometry. This research was advanced because an earlier study had shown nonstandard taxiway geometries to be associated with a higher prevalence of runway incursions. The FAA defines a runway incursion (RI) as “any occurrence at an aerodrome involving the incorrect presence of an aircraft, vehicle, or person on the protected area of a surface designated for the landing and takeoff of aircraft.” These occurrences include wrong runway landings and takeoffs. This research effort developed a geographic information system (GIS) database of approximately 520 airports with civilian air traffic control towers. For each airport, the location of nonstandard geometries, runway incursions, airfield hot spot areas, airport diagrams, and other airport-related information is identified. This research study identified 140 airfield locations with a high incidence of RIs using data from October 1, 2007, to September 30, 2013. As a result, a long-term improvement program, known as the Runway Incursion Mitigation (RIM) program, launched in FY2015. The goal of the program was to mitigate airfield locations with high incidences of RIs. A subset of the 140 locations identified was then validated for inclusion in the RIM program and prioritized for mitigation. The RIM program is updated annually using the GIS airport database to identify construction-related changes to airfield layout and their impacts on taxiway geometries, the airfield location of new RIs, and the status of airfield locations prioritized for mitigation. This report summarizes the status of the RIM program through FY2024. In FY2024, the program georeferenced 1,417 RIs, removed 122 nonstandard geometry locations, prioritized 27 locations for mitigation, and identified 8 locations as mitigated. Since program initiation, 17,939 RIs have been georeferenced at airports along with 6,573 nonstandard geometry locations. In addition, 238 airfield locations were prioritized for mitigation and 100 of these locations have been mitigated. As of Calendar Year (CY) 2023, the 87 locations that have contributed to the short-term and long-term reduction in RIM Inventory experience, on average, 78% fewer incursions per year after mitigation activity.]]></description>
      <pubDate>Thu, 02 Apr 2026 16:17:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685463</guid>
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