<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzkyIiAvPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSI3MzAiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMTYiIC8+PC9wYXJhbXM+PGZpbHRlcnMgLz48cmFuZ2VzIC8+PHNvcnRzPjxzb3J0IGZpZWxkPSJwdWJsaXNoZWQiIG9yZGVyPSJkZXNjIiAvPjwvc29ydHM+PHBlcnNpc3RzPjxwZXJzaXN0IG5hbWU9InJhbmdldHlwZSIgdmFsdWU9InB1Ymxpc2hlZGRhdGUiIC8+PC9wZXJzaXN0cz48L3NlYXJjaD4=" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
    </image>
    <item>
      <title>Near Real-time Construction Quality Monitoring and Inspection Protocols
Using Uncrewed Aerial Vehicles</title>
      <link>https://rip.trb.org/View/2691499</link>
      <description><![CDATA[Innovative quality control technologies are used for asphalt materials to ensure smooth and long-lasting pavements. Paver Mounted Thermal Profilers (PMTPs) and Intelligent Compactors (ICs) are among the technologies used for freshly paved mat quality monitoring. However, these technologies are expensive and have some limitations in retrofitting the existing pavers and rollers from different equipment manufacturers. In the first phase of our research, aerial thermal images captured from an Uncrewed Aerial Vehicle (UAV) were used to develop a framework for hot-mix asphalt (HMA) construction quality monitoring. Thermal segregation risk was quantified using three different metrics. An object detection machine learning algorithm was implemented to develop compaction efficiency metrics that includes roller pass counts and roller speed. Various construction sites were visited in 2023 and 2024 to develop models and framework. The main goal of the second phase of the study is to extend capabilities of the models and algorithms of the aerial pavement quality monitoring protocol and improve the readiness level for implementation in real-life scenarios. The focus of the proposed work in the second phase is to develop the probabilistic density models and extend the limits of the surveyed mat. The ultimate vision is to commercialize the proposed technology as a contractor tool that can provide near real-time quality data from the ongoing construction with actionable feedback. When such data is made available to the paving crew, likelihood of achieving uniform and target in-place densities will increase and overall pavement quality will be improved. ]]></description>
      <pubDate>Thu, 10 Sep 2026 08:58:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691499</guid>
    </item>
    <item>
      <title>A Regional Approach to Pavement Design for Low-Volume Roads</title>
      <link>https://rip.trb.org/View/2691525</link>
      <description><![CDATA[Low-volume roads represent a significant portion of the U.S. transportation network, yet their design methods are not adequately calibrated to reflect the environmental, material, and policy conditions of the regions they serve. This project aims to enhance durability and extend the life of low-volume transportation infrastructure by refining pavement design methodologies to better account for regional variations in climate, soil behavior, and construction practices. Building on the Mechanistic-Empirical Pavement Design Guide (MEPDG), which is primarily calibrated for high-volume state routes, this research will address its limitations for rural applications where environmental stresses can dominate performance outcomes. 

The project will integrate insights from the NCHRP 01-59 mechanistic procedure to evaluate volume change effects and their impact on pavement roughness. By comparing existing local construction practices and material use in Arizona against MEPDG predictions, researchers will identify calibration needs and propose region-specific enhancements. These findings will directly support NCIT’s core focus of Improving Durability and Extending the Life of Transportation Infrastructure and align with the topical pillars of Infrastructure Durability & Resilience and Technology. 

The project will generate actionable, data-driven recommendations for improving pavement performance and optimizing maintenance strategies. Results will be disseminated through targeted outreach and education efforts for local governments, equipping them with tools to make informed, cost-effective infrastructure decisions. This work offers the potential to significantly reduce life cycle costs, minimize disruptions, and improve mobility across diverse environments and roadway systems. ]]></description>
      <pubDate>Thu, 10 Sep 2026 08:56:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691525</guid>
    </item>
    <item>
      <title>Repurposed Markings: A New Tool for Curve Safety</title>
      <link>https://rip.trb.org/View/2696938</link>
      <description><![CDATA[Horizontal curves pose a significant roadway safety challenge, with crash rates nearly three times higher than on other geometries. Traditional measures, such as roadside warning signs, often have limited visibility and effectiveness. This project will conduct a quasi-experimental evaluation of horizontal signage—durable pavement markings installed within the driver’s line of sight—as a countermeasure for curve safety. Using a difference-in-differences (DID) approach with treatment and control sites the research team will assess safety effectiveness, durability, and cost-effectiveness of horizontal signage. The study will also incorporate Propensity Score Matching (PSM) to ensure balanced site comparisons. Outcomes will include evidence-based recommendations for potential inclusion in traffic control guidance, public datasets, and technical reports.]]></description>
      <pubDate>Thu, 10 Sep 2026 08:21:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696938</guid>
    </item>
    <item>
      <title>Advanced Air Mobility (AAM) Strategic Plan for New Hampshire</title>
      <link>https://rip.trb.org/View/2775489</link>
      <description><![CDATA[This research will position New Hamphshire to quickly integrate Advanced Air Mobility (AAM) safely and expeditiously into the state’s transportation system.  AAM will provide alternative transportation means for the movement of goods and passengers thereby reducing congestion in other transportation modes. This project will identify strategies for incorporating Advanced Air Mobility (AAM) operations that provide service through scheduled, on-demand, or as part of intermodal transportation links throughout NH’s communities and across state boundaries including how to accommodate a wide range of AAM operations, infrastructure, and legislative needs. A Strategic Plan detailing the current and expected trajectory of AAM in New Hampshire and throughout the industry will provide a roadmap for stakeholders to use in developing flexible solutions to integrating AAM technologies into NH’s existing transportation system.]]></description>
      <pubDate>Wed, 09 Sep 2026 10:41:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775489</guid>
    </item>
    <item>
      <title>Accessibility-Based Transportation Network Resilience Planning: Extending Vermont's TRPT</title>
      <link>https://rip.trb.org/View/2775484</link>
      <description><![CDATA[Vermont’s Transportation Resilience Planning Tool (TRPT) assesses the criticality of roadway links by measuring how much vehicle hours of travel (VHT) increases across the network when a link fails. This metric, and similar metrics used in other network resilience applications, are practical as a measure of mobility, but they obscure the question that matters most to communities and planners: which places lose their ability to reach needed destinations? Accessibility — the ease with which people can reach destinations such as jobs, healthcare, grocery stores, and emergency services — is the true purpose of transportation infrastructure, and a road network criticality measure grounded in accessibility can reveal both which links are critical and which communities bear the burden when those links fail. This project will develop an Accessibility Criticality Index (ACI) for Vermont's statewide roadway network and evaluate it against the existing mobility-oriented Network Criticality Index (NCI) used in Vermont's TRPT. The accessibility metric will be developed using publicly available destination data and the Vermont statewide Travel Demand Model (VTDM). The core research questions are: (1) How do link criticality rankings differ when measured using accessibility rather than mobility, and what does that reveal about the current TRPT's blind spots, and (2) what Vermont towns and census tracts stand to see the greatest access loss when links fail, and how does community vulnerability differ when measured using accessibility rather than mobility? Findings will directly inform a proposed Phase 2 project to integrate the ACI into the TRPT web application.]]></description>
      <pubDate>Tue, 08 Sep 2026 16:11:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775484</guid>
    </item>
    <item>
      <title>Generative AI-based Framework for Modeling Longitudinal Travel Behavior Adaptation Under Transportation Interventions</title>
      <link>https://rip.trb.org/View/2775063</link>
      <description><![CDATA[Transportation agencies deploy a wide range of interventions to influence travel behavior and manage demand. While a broad range of approaches have been used to model travel behavior dynamics, modeling how behavioral responses manifest, strengthen, or decay over repeated exposure to transportation interventions remains relatively underexplored. This project will address this gap by developing a novel framework that integrates a Generative Artificial Intelligence (AI)-powered behavioral simulation engine with a longitudinal stated preference (SP) study to model, calibrate, and validate the temporal evolution of travel behavior in response to transportation interventions.

Leveraging large language models' capability to reason through open-ended behavioral scenarios, the engine will construct heterogeneous traveler agents with persona profiles that vary across sociodemographic characteristics, mode preference, trip purpose, risk tolerance, and sensitivity to intervention type. These agents will simulate the full range of behavioral adaptation over time, capturing dynamics such as habit formation, inertia, and resistance to change. A longitudinal stated preference study, deploying scenario-based stimuli via Qualtrics to a panel recruited through Prolific, will provide the empirical basis for calibrating, fine-tuning where appropriate, and validating the engine against observed traveler responses. Safety-focused interventions will serve as the instantiation domain. The resulting framework will be intervention-agnostic and transferable, providing transportation planners an evidence-based tool for evaluating long-term behavioral implications of interventions prior to their deployment in the real world.]]></description>
      <pubDate>Fri, 04 Sep 2026 15:40:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775063</guid>
    </item>
    <item>
      <title>Investigating the Stability and Evolution of Transportation-related Attitudes: Evidence from Two Longitudinal Survey Samples</title>
      <link>https://rip.trb.org/View/2775062</link>
      <description><![CDATA[Despite extensive evidence linking attitudes to behavior in the academic literature, challenges in measuring and forecasting attitudes remain key barriers to incorporating travelers' attitudes into practice-oriented regional travel demand models. Complementing prior studies on measurement challenges, this project addresses forecasting challenges, by examining temporal changes in transportation-related attitudes. Two panel samples are employed: overlapping respondents from two surveys administered in Georgia in 2017 and 2022 (N=142) and the 2024 and 2025 waves of the Transportation Heartbeat of America Survey (N=701).

After identifying attitudinal factors using exploratory factor analysis, this project will compare attitudinal changes in subsamples with varying socio-economic and demographic characteristics, focusing on whether raw attitudinal variables or attitudinal factor scores are more stable, which attitude types (e.g., travel, residential preferences, technology) exhibit greater stability, and for whom. Additionally, disaggregate-level statistical models of attitudinal changes are developed to provide a more comprehensive understanding of the observed changes. Overall, this project will provide foundational insights that can help analyze travel demand under scenarios involving changes in key input variables — including attitudes — with plausible and empirically-grounded assumptions about how attitudes may evolve and how they relate to other factors, which ultimately will advance the incorporation of attitudes into demand modeling and planning practice.]]></description>
      <pubDate>Fri, 04 Sep 2026 15:38:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775062</guid>
    </item>
    <item>
      <title>Towards Building-Level Modeling: Human-Driven Agentic Workflows for Multi-Source Data Synthesis</title>
      <link>https://rip.trb.org/View/2775061</link>
      <description><![CDATA[Travel demand modeling is limited by the aggregation problem: data are often synthesized at census-tract or traffic-zone scales that obscure building-level mobility patterns. This project develops a human-directed agentic workflow in which a transportation planner orchestrates specialized artificial intelligence (AI) agents that discover, evaluate, and integrate open geospatial data from sources such as OpenStreetMap, building footprint databases, census repositories, municipal portals, and selected sensor feeds. To address risks from plausible but incorrect agent outputs, the pilot will use a bounded proof-of-concept geography, source-provenance tracking, human review, conflict checks, and explicit accuracy metrics against benchmark data.

The work will demonstrate the workflow through an open Digital Forum where practitioners and students can access tools, data, and examples for building-level demand modeling. Expected outputs include a Human-AI Agentic Workflow methodology; an open-source software pipeline that converts enriched OpenStreetMap and related geospatial data into building-level travel demand inputs; a curated, version-controlled database of discoverable open-data sources with source-provenance records; validation procedures and accuracy metrics for handling conflicting sources; a pilot Digital Forum that serves as an educational resource and practitioner-facing decision-support tool; at least one peer-reviewed publication; and training or curriculum materials for students learning to manage artificial intelligence ecosystems in transportation planning.]]></description>
      <pubDate>Fri, 04 Sep 2026 15:25:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775061</guid>
    </item>
    <item>
      <title>The Infrastructure-Perception Gap: Examining the Role of Transportation Infrastructure in Mediating Safety Perceptions for Active Travel</title>
      <link>https://rip.trb.org/View/2775060</link>
      <description><![CDATA[While extensive research demonstrates that perceived safety influences active travel behavior, the relationship between safety perceptions and transportation infrastructure characteristics remains inadequately understood. Traditional safety studies rely primarily on crash data, which often fails to align with how travelers perceive safe environments. This research addresses a critical gap by investigating how transportation infrastructure mediates safety perceptions among active travelers and subsequently affects their travel behavior and route choices.

The study poses three key questions: (1) How does transportation infrastructure shape safety perceptions of people walking, biking, and rolling? (2) How, and to what extent, does infrastructure affect travelers' mode and route choice decisions? (3) Do more complete streets enhance safety perceptions and actual travel safety? It builds on the team's existing computer vision analyses of streetscape factors and travel behavior studies across Atlanta, Berlin, Boston, and Chicago, to develop person-centric measures of perceived safety. The approach leverages established databases and tools for characterizing transportation infrastructure and built environments, along with discrete choice models representing travel behavior decisions. Unlike previous studies that included safety-related variables without isolating perceived safety measures, this research will explicitly examine the relative importance of transportation infrastructure versus broader built environment factors.]]></description>
      <pubDate>Fri, 04 Sep 2026 15:22:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775060</guid>
    </item>
    <item>
      <title>Beyond Pedestrian Flow: Evaluating Urban Sidewalk Friction and Accessibility via Efficient State Space Models</title>
      <link>https://rip.trb.org/View/2775059</link>
      <description><![CDATA[Traditional sidewalk performance evaluation has primarily relied on flow metrics that treat pedestrians as uniform units. These methods typically rely on basic pedestrian counts which capture pedestrians at a single point location. Walkability metrics typically assess the suitability of a street environment for pedestrian activity based on the presence or absence of specific neighborhood or street characteristics. While useful to capture high-level neighborhood pedestrian activity, these approaches fail to capture the complex, micro-level dynamics that occur in pedestrian spaces.

To address this gap, this project introduces a comprehensive framework for evaluating sidewalk performance by developing novel friction metrics. By moving beyond simple counting, the research team aims to quantify how different pedestrian groups and infrastructural elements interact in real-world scenarios. The architecture leverages the combined strengths of Visual Language Models (VLMs) and highly efficient State Space Models (SSMs). The project is structured around three primary objectives: (1) Semantic Categorization via Visual Language Models — processing CCTV camera feeds to categorize pedestrians into distinct behavioral groups and identify infrastructural hurdles; (2) Development of SSM-Based Friction Metrics — using the semantic data generated by the VLMs, utilizing SSMs operating on anomaly detection principles to analyze specific pedestrian interactions over time; and (3) Walkability and Accessibility Evaluation — synthesizing the outputs of the pipeline to evaluate large-scale urban walkability and accessibility by creating and utilizing benchmark data.]]></description>
      <pubDate>Fri, 04 Sep 2026 15:18:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775059</guid>
    </item>
    <item>
      <title>Decoding Road-User Intent: Advancing Surrogate Safety Measures through Behavior-Aware Generative World Models</title>
      <link>https://rip.trb.org/View/2775058</link>
      <description><![CDATA[This project investigates how roadside LiDAR sensing and generative scene prediction models can support more informative and uncertainty-aware traffic safety assessment at urban intersections. Conventional surrogate safety measures, such as time-to-collision and post-encroachment time, are widely used to identify potential traffic conflicts. However, these measures are primarily based on observed kinematic relationships and may not fully capture road-user behavior, interaction context, or uncertainty caused by sensing noise, occlusion, and tracking errors.

The project will develop and evaluate a pilot infrastructure-centric modeling framework that uses roadside LiDAR data to forecast short-term movements of vehicles, pedestrians, cyclists, and other road users from a fixed roadside perspective. Rather than treating observed trajectories as deterministic, the framework will explore probabilistic scene prediction methods that represent multiple plausible future interactions and provide uncertainty estimates. Building on these forecasts, the project will develop prototype intention-aware and uncertainty-aware surrogate safety measures. These measures may include probabilistic conflict indicators, uncertainty-calibrated risk scores, and other metrics that supplement conventional kinematic safety measures.

The proposed metrics will be evaluated using roadside LiDAR data and manually reviewed conflict events, with attention to prediction accuracy, uncertainty calibration, interpretability, and practical relevance for proactive safety analysis. The project will also produce implementation-oriented resources, including a prototype Roadside Safety Forecasting Toolkit, example workflows, evaluation scripts, documentation, and a practitioner-facing brief.]]></description>
      <pubDate>Fri, 04 Sep 2026 15:14:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775058</guid>
    </item>
    <item>
      <title>Integrating Microsimulation in a Multi-scale Local Freight Activity Model</title>
      <link>https://rip.trb.org/View/2775057</link>
      <description><![CDATA[This project builds on results from prior TBD Center projects to advance the development of a multi-scale, synthetic data-based simulation framework for evaluating urban freight policy and infrastructure interventions. While prior phases enabled representation of total activity within a traffic analysis zone (TAZ), they do not yet represent location- or segment-specific activity within the zone. However, many urban freight interventions of interest to local cities — such as street design changes, lane or signal prioritization, parking and loading regulations, and segment-specific access controls — require this more detailed granularity. This Phase 3 study aims to address this gap.

The aim of this project is to define a simulation framework that could integrate TAZ-scale vehicle activity inputs, be implemented within a TAZ at a population scale to simulate segment-level activities, and be adaptable to simulate multiple local decision types. This project phase has three objectives: (1) to identify best-available microsimulation approaches for evaluating micro-scale freight interventions; (2) to assess the utility of machine learning approaches — in particular reinforcement learning — to improve upon static decision-assignment processes within these simulations; and (3) to develop a scalable and adaptable framework for microsimulation of freight interventions that integrates TAZ-scale truck paths with high-resolution segment-level activity. Key project tasks include: a comprehensive synthesis of existing freight microsimulation studies, feasibility analysis of reinforcement learning for freight operator route choice and behavioral modeling, development of a proposed graph structure for simulating varied freight-related interventions within a traffic microsimulation model, and evaluation of common simulation tools (e.g., VISSIM, SUMO) to assess their native capability to represent the proposed graph structure.]]></description>
      <pubDate>Fri, 04 Sep 2026 15:04:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775057</guid>
    </item>
    <item>
      <title>Measuring the Motivators of Home Delivery Decisions</title>
      <link>https://rip.trb.org/View/2775056</link>
      <description><![CDATA[Over the last decade, many shoppers in the U.S. and around the world have quickly become reliant on home-based deliveries of household and retail goods. While growth in e-commerce is easily demonstrated from retail sales records, relevant activity trade-offs and resulting impacts on passenger and freight travel activity remain less well-understood. Some home deliveries are made out of necessity, while others are discretionary. Some deliveries replace in-store shopping, while others are supplementary to in-person shopping activities.

This study leverages a novel national survey, the second wave of the Transportation Heartbeat of America Survey, that simultaneously captures: (1) motivations for specific types of online purchases and/or reasons for not purchasing goods online; (2) household delivery frequencies by type (carrier-delivered parcels, crowdsourced packages, prepared food, and groceries); (3) personal travel behaviors; and (4) individual and household characteristics. In this study, the research team will directly investigate the underlying motivators for home delivery, and the barriers to home delivery, that influence Americans' home delivery choices. First, leveraging location-specific survey responses, the team will conduct descriptive and spatial analysis of results to identify relevant trends. Next, the team will apply exploratory factor analysis (EFA) to identify latent motivation dimensions and latent class analysis (LCA) to identify distinct behavioral segments based on stated delivery motivations. Third, the team will estimate and compare delivery frequency models, including ordered probit models, zero-inflated negative binomial models, hybrid choice models, or integrated choice and latent variable models. By comparing model performance with and without new motivational variables, this study will test whether controlling for stated motivations improves model fitness and reduces inconsistencies observed in prior work.]]></description>
      <pubDate>Fri, 04 Sep 2026 15:01:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775056</guid>
    </item>
    <item>
      <title>Drone-in-a-Box: Enhancing Public Safety and Environmental Monitoring on Cape Cod</title>
      <link>https://rip.trb.org/View/2775052</link>
      <description><![CDATA[Cape Cod, a popular tourist destination, faces unique challenges due to its geographic isolation, seasonal population fluctuations, and increasing environmental concerns. This research proposes leveraging drone-in-a-box (DiB) technology, an autonomous drone system with automated launch, landing, and charging capabilities, to address these challenges. DiB systems offer persistent aerial surveillance and rapid response capabilities, making them ideal for various applications in remote and dynamic environments.

The research team for this project includes representatives from 
Massachusetts Department of Transportation (MassDOT) Aeronautics, MassAutonomy, and Endicott College.  Endicott College will identify appropriate team members based on the expertise needed for each aspect of the project.

This project is significantly strengthened by the collaboration and support of the Wellfleet Police and Fire Departments, who will provide valuable expertise, operational insights, and access to critical resources.

The objectives of this project are to: (1) Evaluate the technical feasibility of deploying DiB systems in the Cape Cod environment, considering factors such as weather conditions, Federal Aviation Administration (FAA) regulations, and communication infrastructure.
(2) Assess the operational effectiveness of DiB systems in each use case, measuring improvements in traffic flow, emergency response times, and shark detection accuracy.
(3) Analyze the cost-benefit of DiB implementation compared to traditional methods. 
(4) Investigate public perception and acceptance of drone technology for public safety and environmental monitoring.
The project will yield data and associated reports detailing the effectiveness of the use of drone-in-a-box (DiB) technology, an autonomous drone system with automated launch, landing, and charging capabilities, to address these challenges.
]]></description>
      <pubDate>Fri, 04 Sep 2026 14:40:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775052</guid>
    </item>
    <item>
      <title>RailRisk Advisor: A Web-GIS Tool for Assessing Railroad Trespassing Risk and Recommending Countermeasures</title>
      <link>https://rip.trb.org/View/2772532</link>
      <description><![CDATA[Trespassing is the leading cause of rail-related deaths in the United States. According to the Federal Railroad Administration, more than 500 trespass fatalities occur nationally each year (Trespass Prevention, 2025). The number of trespassing occurrences on railroad property each year far exceeds the number of fatalities, which means that there is potential for more trespasser accidents. This proposal aims to develop a RailRisk Advisor, a web geographic information system (GIS)-based system, to enhance railroad safety by identifying and analyzing high-risk railroad segments for trespassing incidents. Using geospatial data, historical trespassing records, and contextual factors (such as train volume, train speed, land use, and population density), a data-driven model will be developed for calculating the risk score of each railroad segment. The calculated risk levels will be validated by comparing historical incident data. Advanced spatiotemporal analytics and predictive modeling will be employed to provide a user-friendly platform for detecting and visualizing risk hotspots, conducting interactive analyses, and developing targeted mitigation strategies. Finally, the platform will be made accessible through an interactive web-GIS based dashboard. The dashboard will feature tools for data querying, visualizing, analyzing, and reporting railroad risk data. The proposed RailRisk Advisor could potentially change the way railroad trespassing risks are managed by shifting from reactive to proactive strategies. By leveraging data-driven insights, transportation agencies can prioritize interventions, optimize resource allocation, and implement targeted countermeasures.


]]></description>
      <pubDate>Fri, 04 Sep 2026 08:29:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2772532</guid>
    </item>
  </channel>
</rss>