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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
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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>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Evaluation of Safety Elements for Improved Pedestrian Safety at At-Grade Rail Crossings</title>
      <link>https://rip.trb.org/View/2784727</link>
      <description><![CDATA[Provide insight into how other transit/transportation agencies improve safety at at-grade crossings, particularly with helping pedestrians be more aware of train movements before crossing tracks.]]></description>
      <pubDate>Wed, 30 Sep 2026 18:03:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2784727</guid>
    </item>
    <item>
      <title>Rail Economic Contribution Study</title>
      <link>https://rip.trb.org/View/2784542</link>
      <description><![CDATA[North Carolina Department of Transportation (NCDOT) Rail Division’s mission is the “…safe and efficient movement of people and goods on North Carolina’s railroads through freight, passenger and safety programs, supporting job creation and economic growth.” These objectives are integral in serving North Carolina’s citizens, visitors, and industries.

The project will seek to understand the economic contribution relating to the usage of the NC rail system. The final deliverables will include a profile of traffic data (freight and passenger) and economic contribution for each, based on passenger spending/economic benefit derived from their use of rail. ]]></description>
      <pubDate>Tue, 29 Sep 2026 15:13:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2784542</guid>
    </item>
    <item>
      <title>Impact of Positive Train Control</title>
      <link>https://rip.trb.org/View/2780472</link>
      <description><![CDATA[Positive train control (PTC) is an advanced rail safety technology designed to mitigate human error and improve operational safety. It reduces the risk of accidents by preventing or reducing train-to-train collisions, overspeed derailments, improperly lined switches, and unauthorized incursion into work zones. Public transportation agencies that operate commuter and intercity passenger rail services have been required to have PTC installed since 2020. To meet this deadline, agencies had to invest immense resources into new infrastructure and specially trained staff to develop and test the system. 

While PTC implementation has been extensively documented, less is known about its long-term operational impacts, realized safety benefits, and opportunities for leveraging PTC investments beyond regulatory compliance. Now that agencies have been operating with PTC for several years, research is needed to examine and document how PTC has affected passenger rail safety and how PTC-related infrastructure, data, and workforce capabilities can be leveraged to further enhance safety, security, and operational performance.

OBJECTIVE: The objective of this research is to document and quantify the safety, operational, and organizational impacts of PTC on passenger rail operations subject to the Rail Safety Improvement Act of 2008, and to identify lessons learned, current practices, and benefits realized beyond statutory and regulatory minimum requirements.

]]></description>
      <pubDate>Wed, 23 Sep 2026 18:03:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2780472</guid>
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    <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>
    <item>
      <title>Autonomous Robotic System for Detecting Rail Anchor Failures Using Computer Vision</title>
      <link>https://rip.trb.org/View/2772533</link>
      <description><![CDATA[This project addresses the issue of rail anchor failures that pose significant risks to rail safety. The anchor failure can cause track misalignment, rail buckling, and derailments and adds to the maintenance costs. The proposed innovation is an autonomous robotic system designed to detect and locate rail anchor failures with precision and efficiency. By leveraging computer vision and machine learning, the system will identify problems such as slippage, damage, or displacement of rail anchors in real-time and provide precise location mapping for targeted maintenance interventions. The research approach will involve creating a high-resolution annotated image dataset and training a deployable artificial intelligence (AI) model to enable accurate, consistent, and scalable anchor inspections — capabilities that are not adequately addressed by existing systems. The work will be performed in two phases. The first phase will complete the design and development of the robotic platform equipped with high-resolution cameras and AI-based diagnostic tools. In the second phase, experimental validation of the system will be carried out on a railway track to ensure scalability, reliability, and effectiveness under real-world conditions. The system will automate the inspection process, eliminate human error, and enable proactive maintenance resulting in reduced downtime and enhanced rail safety. The scalable design and integration of advanced diagnostics will make the system suitable for adoption by the railroad industry on a wider scale and contribute to enhanced rail infrastructure reliability and operational efficiency.]]></description>
      <pubDate>Fri, 04 Sep 2026 08:28:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2772533</guid>
    </item>
    <item>
      <title>Preventing Turnout Failures through Advanced Analytics and Real-Time Monitoring</title>
      <link>https://rip.trb.org/View/2772534</link>
      <description><![CDATA[The growing demand for freight and passenger rail transport is putting immense pressure on network availability and reliability. Turnout failures, which account for about 20–30% of all rail infrastructure delays, significantly impact on-time performance. Current labor-intensive and periodic inspection methods lack the granularity and real-time insights needed to track asset degradation under traffic, often resulting in unanticipated failures and reduced network availability. A proactive approach is therefore essential to detect early warning signs, prioritize maintenance, and prevent service-disrupting failures which could potentially impact safe running of the railroad. This Innovations Deserving Exploratory Analysis (IDEA) project proposes to evaluate a new IIoT sensor system to determine turnout conditions through continuous remote monitoring. The goal is to validate whether such a system is deployable in the US railroad network across transit, passenger, and Class One networks. The system comprises IIoT sensors with accelerometers installed on ties at the frog or the switch to measure vibrations caused by the passing train. The level of vibrations, provided as vertical acceleration, is a strong indicator of degradation and is used as the base measurement to provide insights into asset conditions. Tests in Europe have provided further insights into understanding the extent of impact of acceleration on assets. Collaborations with railroad companies, Norfolk Southern and CSX, have been established to evaluate the developed system on shared mainline turnouts. Union Pacific and METRA, the commuter rail system in the Chicago Metropolitan area, have also expressed interest in participation.]]></description>
      <pubDate>Fri, 04 Sep 2026 08:28:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2772534</guid>
    </item>
    <item>
      <title>AI-Enabled In-Motion Assessment of Continuous Welded Rail</title>
      <link>https://rip.trb.org/View/2772546</link>
      <description><![CDATA[Rail neutral temperature (RNT), the temperature at which a section of continuous welded rail (CWR) experiences no internal stress (no compression or tension), is a critical factor in maintaining track stability. Excessive compression can cause buckling and derailment, while excessive tension can lead to rail fractures. In their previous project, the researchers developed and tested a stationary wayside system to determine the RNT of CWR. The system measured low-frequency rail vibrations, triggered with an electrically driven rod, and applied artificial intelligence (AI) trained with finite element modeling to determine RNT. In this new IDEA project, the research team proposes an in-motion inspection system as a better and more desirable alternative to the stationary wayside system. Unlike the stationary system, this in-motion system will allow inspection of several miles of a given track in any given day and will also complement other scanning inspection solutions. The change to in-motion system for monitoring RNT will be accomplished by assembling a moving vehicle on which an impactor will be mounted to generate localized vibrations. A battery-operated laser vibrometer will measure those vibrations. The frequency spectrum of the signals will be extracted and fed to a machine learning algorithm previously trained on finite element data to estimate the RNT and the rail lateral resistance. Despite the development of several techniques in the past, the railroad industry is still looking for a method to provide high level of accuracy with minimum traffic disruption. This non-proprietary and open source IDEA product has a potential to fulfil this need. It will enable measurement of thermal stress or the estimation of RNT without requiring extensive preparation, track closure, or day long observations during which the RNT must be crossed. The proposed system can be installed in moving vehicles anytime of the year to make measurements at discrete locations and is not affected by the environmental effects.


]]></description>
      <pubDate>Fri, 04 Sep 2026 08:27:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2772546</guid>
    </item>
    <item>
      <title>Bayesian Network Modeling of Cyber-Physical Risk and System Dependencies in Railway Control Systems</title>
      <link>https://rip.trb.org/View/2762050</link>
      <description><![CDATA[Railway systems increasingly depend on integrated control, communications, and operational technologies, and this growing connectivity exposes them to cyber disruptions that can cascade across interdependent subsystems and compromise operations and safety. Existing cybersecurity assessment methods are largely static or deterministic and poorly suited to representing cascading effects in complex railway environments, motivating a probabilistic framework for understanding disruption propagation, critical vulnerabilities, and the resilience benefits of alternative mitigation strategies.
This project develops a railway-specific Bayesian network model representing major cyber-physical components, dependencies, and disruption pathways in railway control systems. The methodology first identifies and characterizes critical railway subsystems and their cyber-physical interdependencies, then defines the model structure and key variables capturing system states, disruption pathways, and operational consequences. Conditional probabilities are developed from available empirical evidence, published literature, and expert-informed estimates. Bayesian inference is applied to quantify how cyber events propagate through the system and influence safety and operational outcomes, and scenario-based resilience analysis identifies critical bottlenecks, assesses mitigation effectiveness, and compares risk-reduction strategies.
Expected products include a validated probabilistic modeling framework for cyber-physical risk in railway control systems, quantitative risk metrics for operational disruption and safety impact, scenario-based analysis tools, and methods for identifying high-impact components and vulnerability concentrations. Outputs will support railway operators, infrastructure owners, and regulators in cyber risk management, infrastructure hardening, investment prioritization, and operational resilience planning, and may inform future guidance and regulatory discussions on railway cybersecurity.
]]></description>
      <pubDate>Wed, 19 Aug 2026 16:14:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2762050</guid>
    </item>
    <item>
      <title>Application of Machine Learning to Investigate Grinding Parameters of a Novel Railway Grinding Machine</title>
      <link>https://rip.trb.org/View/2762049</link>
      <description><![CDATA[Rail surface defects develop through repeated wheel–rail interactions and require timely grinding intervention to maintain rail integrity, enhance safety, and avoid costly rail replacement. Although prior studies have optimized individual grinding parameters such as rotational speed, stone granularity, and feed rate, the combined influence of multiple interacting parameters on metal removal rate and surface quality remains insufficiently understood, hindering development of optimized grinding patterns.
This project develops an uncertainty-aware machine learning framework tailored to the limited size of laboratory-generated railway grinding datasets. A Gaussian Process Regression (GPR) model will predict key performance indicators — Metal Removal Rate and Surface Roughness- from inputs such as rotational speed and grinding wheel orientation while quantifying predictive uncertainty. Leave-one-out cross-validation maximizes data utilization, GridSearch optimization identifies hyperparameters, and a fixed random state ensures reproducibility; performance is evaluated with the coefficient of determination and Root Mean Squared Error. A complementary Support Vector Classifier is developed to classify grinding burn conditions, with data-imbalance mitigation via class weighting and Random Minority Oversampling, evaluated by accuracy and F1-score.
Interpretability is central to the effort: SHAP analyses will rank feature importance so railway management can identify the most influential grinding parameters, and Partial Dependence Plots will clarify individual parameter effects. Expected outcomes include unified grinding patterns that achieve target rail profiles while maximizing material removal efficiency and surface quality, supporting the industry shift from corrective to preventive grinding, extending rail service life, reducing replacement costs, and strengthening railway operational safety.
]]></description>
      <pubDate>Wed, 19 Aug 2026 16:05:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2762049</guid>
    </item>
    <item>
      <title>Flood-resilient Transport System Through Integrated Modeling, ML &amp; Immersive AR/VR </title>
      <link>https://rip.trb.org/View/2732359</link>
      <description><![CDATA[Extreme rainfall events increasingly disrupt urban transportation systems by overwhelming drainage infrastructure and causing localized road flooding that impedes last-mile freight delivery, delays emergency response, and disrupts the broader multimodal supply chain. These disruptions limit access to essential services, delay emergency response, and threaten public safety. Building on the research team's previously developed framework (F25-26), this project advances a data-driven approach for high-resolution prediction of urban road flooding in Jackson, Mississippi, integrating geospatial databases, process-based H-H modeling (aligning with rigorous U.S. Army ERDC methodologies), and machine learning (ML) and artificial intelligence (AI) techniques to identify flood-prone road and railway segments. The ML-based surrogate models will maintain computational efficiency, enable timely identification of vulnerable transportation networks, and support emergency response by feeding into JSU Water Lab’s broader web-based-visualization interfaces. This project introduces an interactive K–12 STEM module, age-appropriate hands-on activities along with STEM curriculum module designed for upper-level Civil Engineering undergraduate and graduate students at JSU. This initiative transforms research outcomes into the classroom to modernize workforce training using integrated Augmented Reality (AR) and Virtual Reality (VR) and will be tested with summer student exchange programs. Students can explore and 3D print several transportation infrastructure components, such as culverts, bridges, and urban drainage systems, and evaluate their performance under simulated flood conditions, and experience AR/VR based immersive simulators. Using AR/VR tools, including the Meta Quest platform, available in the PI lab, future transportation engineers will visualize flood scenarios in immersive 3D environments built from existing topographical assets in Unity or Unreal Engine. By combining advanced predictive modeling with experiential learning, the project promotes advanced STEM engagement, and high-tech workforce development, aligning with broader goals of improving multimodal transportation system resilience. While the primary focus is on urban road and rail flooding, these transportation corridors serve as critical connectors to Mississippi's inland waterway freight network, including facilities linked to the Pearl River system and regional multimodal freight movements. Roadway disruptions during extreme rainfall events can delay freight access to ports, intermodal terminals, water-dependent industrial facilities, and affect supply-chain resilience. By identifying flood-vulnerable roadway and railway segments, the proposed framework will support more reliable connectivity between surface transportation infrastructure and maritime freight operations]]></description>
      <pubDate>Tue, 21 Jul 2026 16:34:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2732359</guid>
    </item>
    <item>
      <title>Integrated Acoustic and Human-Centered Development and Digital Twin Testing for Rail Noise Abatement Strategies in Ohio
</title>
      <link>https://rip.trb.org/View/2712240</link>
      <description><![CDATA[Rail and highway projects tend to run in tandem. Typically, residential areas along rail lines are located at grade separations. As a result, residents can be adversely impacted by rail noise and track vibration. Ohio Department of Transportation (ODOT) consistently receives complaints from residents related to rail noise. Currently, ODOT does not have any defined noise abatement strategies for rail projects. Research is needed to determine if there are feasible, reasonable, cost-effective ways to dampen rail noise for residential and other noise sensitive areas along rail lines.

Research Goal: Identify innovative techniques and/or designs that can aid in the mitigation of rail noise. For this study, rail noise is referring to sounds coming from the tracks and subsequent vibrations, not the train horn. Of particular interest is railroad crossing elimination projects, which are subject to National Environmental Policy Act (NEPA)  assignment and typically include an at grade crossing and the potential for road relocation above existing rail lines. Additional items that should be taken into consideration include ownership, requirements, and costs for installation and ongoing maintenance of all proposed solutions.

Potential Benefits: Effective noise abatement strategies for rail noise could extend benefits currently experienced from highway noise abatement strategies to residential and commercial areas located along rail lines. This includes but is not limited to increased quality of life.        ]]></description>
      <pubDate>Tue, 09 Jun 2026 10:55:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712240</guid>
    </item>
    <item>
      <title>Railway Noise Abatement Strategies: State of the Art and Applicability for Ohio Corridors</title>
      <link>https://rip.trb.org/View/2712239</link>
      <description><![CDATA[Rail and highway projects tend to run in tandem. Typically, residential areas along rail lines are located at grade separations. As a result, residents can be adversely impacted by rail noise and track vibration. Ohio Department of Transportation (ODOT) consistently receives complaints from residents related to rail noise. Currently, ODOT does not have any defined noise abatement strategies for rail projects. Research is needed to determine if there are feasible, reasonable, cost-effective ways to dampen rail noise for residential and other noise sensitive areas along rail lines.

Research Goal: Identify innovative techniques and/or designs that can aid in the mitigation of rail noise. For this study, rail noise is referring to sounds coming from the tracks and subsequent vibrations, not the train horn. Of particular interest is railroad crossing elimination projects, which are subject to National Environmental Policy Act (NEPA) assignment and typically include an at grade crossing and the potential for road relocation above existing rail lines. Additional items that should be taken into consideration include ownership, requirements, and costs for installation and ongoing maintenance of all proposed solutions.

Potential Benefits: Effective noise abatement strategies for rail noise could extend benefits currently experienced from highway noise abatement strategies to residential and commercial areas located along rail lines. This includes but is not limited to increased quality of life. 

The goal of this research is to systematically research and evaluate emerging noise abatement strategies for potential implementation in Ohio's railroad projects. The research team will directly address the effectiveness (cost per dB reduced), cost (including construction and maintenance costs, such as cost per mile), reliability, and implementation hurdles for each strategy, as well as ownership considerations. They will also consider combining measures (e.g., dampers, a short barrier, and a track pad) to achieve additive benefits beyond those reported in existing studies. The proposed project advances the state of practice for railroad noise abatement strategies from scattered information worldwide to a more defined, deployable set. It provides an in-depth evaluation of innovative rail noise abatement solutions, moving beyond the conventional highway noise wall paradigm to more adaptable, rail-specific approaches.            ]]></description>
      <pubDate>Tue, 09 Jun 2026 10:26:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712239</guid>
    </item>
    <item>
      <title>Putting a Price on Regional Rail Quality: Evaluating the Value Potential Riders Place on Regional Rail Service Attributes</title>
      <link>https://rip.trb.org/View/2702083</link>
      <description><![CDATA[Public transit has suffered from chronic disinvestment despite its community-wide benefits. Post-pandemic, drastic changes in travel demand have left agencies grappling with financial stress. California’s transit ridership has generally tracked alongside national ridership trends with a substantial dip in ridership and then slow recovery, but commuter rail mode share has remained substantially lower than pre-pandemic shares. Most rail services are geared towards serving commuters; higher frequency is offered during weekdays and peak hours, ticket pricing is tailored to favor people making the same kind of trip on a regular basis, and service hours align with commuter needs. The five days-a-week commuting to work lifestyle is no more, and rail agencies serving commuters are experiencing decimated ridership that is showing no signs of bouncing back. This project uses survey research targeted towards understanding how to tailor rail services to gain new markets for regional rail services. The research team developed a stated preference (SP) experiment to understand evolving needs of commuters and non-commuters, as well as riders and potential riders. The service attributes under study include train schedule, ticket cost, station access, reliability, station amenities, and how the potential user base views rail services. Although the study will focus on the area defined by its research partner, Capitol Corridor, it is widely applicable across the country in locations with intercity, suburban, and small urban regional rail services.]]></description>
      <pubDate>Wed, 13 May 2026 16:58:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2702083</guid>
    </item>
    <item>
      <title>Advancing Rail Infrastructure Asset Management and Hazard Mitigation: Educational Tools and Practitioner Decision Support Systems</title>
      <link>https://rip.trb.org/View/2691664</link>
      <description><![CDATA[As rail infrastructure ages and faces intensifying system stressors (e.g., flooding, icing, and extreme heat), agencies need to identify pathways to enhance the durability and operational reliability of their physical assets. However, there is a significant gap in available training material regarding Rail Infrastructure Asset Management (IAM) for both university students and current practitioners. Building upon the researcher’s ongoing research into adaptive capacity and international rail best practices, this project will translate rigorous research findings into accessible educational and research tools and practical decision-support systems. The project focuses on three primary technology transfer and workforce development initiatives:


(1) Interactive Rail Asset Management Platform: The team will develop a web-based, interactive learning module (utilizing platforms such as Tigyog) targeting students and practitioners. This resource will cover the principles of IAM, condition assessment, and decision-making under uncertainty. It will feature "gamified" scenarios and narrative case studies drawn from the team's research, contrasting infrastructure failures (e.g., the East Palestine, Ohio derailment) with successful engineering adaptations (e.g., the Shinkansen automatic braking systems in Japan). Users will engage with a "build-your-own" asset management framework to apply these concepts in real-time.
(2) University Teaching Packets: To address the lack of specialized rail engineering curricula, the team will create comprehensive teaching modules for instructors. These packets will draw from the team's six-country comparative analysis (U.S., Australia, Spain, Japan, Ghana, Argentina), providing lecture slides, assignment materials, and case-study evaluations. Topics will focus on identifying key asset vulnerabilities, institutional barriers to maintenance, and successful infrastructure hardening strategies.
(3) Practitioner Decision Matrix: The team will develop a "Rail Hazard Mitigation Decision Matrix" for state agencies and rail operators.

This tool will synthesize data on geographic hazards, system ownership models, and cost-benefit ratios to help managers prioritize physical infrastructure improvements.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:25:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691664</guid>
    </item>
    <item>
      <title>A Probabilistic Intelligence-Driven Framework for Predictive Cyber Defense in Railway Systems</title>
      <link>https://rip.trb.org/View/2655703</link>
      <description><![CDATA[The rapid digital transformation of railway systems through automation, system integration, and enhanced connectivity has significantly improved operational efficiency, safety, and reliability. However, this digitalization has simultaneously expanded the cyber-attack surface, introducing new vulnerabilities in signalling, communication, and control systems. As critical national infrastructure, railways require robust protection against cyber threats to maintain operational resilience and public safety.

Railway cyber-physical environments present unique challenges distinct from traditional IT systems, characterized by strong interdependencies between digital and physical components where a single breach can cascade across subsystems, causing widespread disruption, safety hazards, and financial loss. Existing cybersecurity frameworks, often static and rule-based, are inadequate for representing the dynamic, probabilistic nature of modern cyber threats, necessitating data-informed, adaptive approaches capable of modeling complex dependencies and supporting timely decision-making.

This research develops a probabilistic modeling framework for assessing and mitigating cybersecurity risks in railway systems. The core methodology employs Bayesian Networks (BNs) to capture conditional dependencies among key threat variables, integrating both empirical data and expert knowledge to infer system vulnerabilities and potential attack outcomes. To address evolving threats, the framework extends to Dynamic Bayesian Networks (DBNs), incorporating temporal relationships that model cyberattack progression over time, enabling early threat detection and proactive defense strategies.

A central innovation is the integration of MITRE ATT&CK cyber threat intelligence, encoding real-world adversarial tactics, techniques, and procedures (TTPs) into the BN/DBN structures to enhance model realism and predictive accuracy. This research addresses three key questions: how Bayesian and Dynamic Bayesian Networks can model probabilistic relationships and temporal progression of railway cyber threats; how MITRE ATT&CK intelligence can be integrated to capture realistic adversarial behaviors; and how the proposed framework can support proactive cybersecurity risk assessment and decision-making. The resulting framework provides a systematic, interpretable foundation for probabilistic railway cybersecurity analysis, helping operators and policymakers anticipate and respond to emerging threats.]]></description>
      <pubDate>Tue, 20 Jan 2026 14:16:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655703</guid>
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