<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=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzc5IiAvPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSIyeWVhcnMiIC8+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>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>
    </item>
    <item>
      <title>Adaptive Cyber Threat Detection for Rail SCADA Systems: A Hybrid Machine Learning and Statistical Approach</title>
      <link>https://rip.trb.org/View/2655702</link>
      <description><![CDATA[Supervisory Control and Data Acquisition (SCADA) systems form the digital backbone of modern railway operations, enabling real-time monitoring of critical track geometry parameters including gage, cross-level, alignment, and warp that are essential for preventing derailments and ensuring passenger safety. While SCADA-driven sensing has advanced continuous condition monitoring, it has also introduced new cyber-physical vulnerabilities, particularly stealthy False Data Injection Attacks (FDIAs) capable of masking real defects or fabricating false positives without detection.

Existing rule-based and signature-based detection systems fail to identify subtle or novel attacks in high-dimensional, noisy rail geometry data, and most current models require labeled attack datasets that are rarely available. Although unsupervised methods such as autoencoders and Variational Autoencoders (VAEs) can detect deviations from learned normal behavior, they remain limited by non-stationary data characteristics and static detection thresholds.

This research proposes a Hybrid VAE with Median Absolute Deviation (MAD) scoring to enable robust, adaptive anomaly detection based on the statistical significance of reconstruction errors. The study investigates whether this approach enhances detection of both subtle and overt FDIAs compared to Isolation Forest and static-threshold VAE baselines, evaluates the effectiveness of MAD-based adaptive thresholding against fixed percentile methods, and examines trade-offs in interpretability, computational load, and reliability across attack intensities.

Using an operational track geometry dataset (18,290 samples, 87 features) from Colorado rail testing, the methodology simulates FDIAs through additive spikes, multiplicative distortion, and high-variance noise injection on safety-critical features. Model performance is evaluated using precision, recall, F1-score, and accuracy, with PCA and t-SNE visualization for validation. Findings will provide actionable deployment guidelines for enhancing cyber-physical resilience in railway SCADA systems.]]></description>
      <pubDate>Mon, 19 Jan 2026 16:16:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655702</guid>
    </item>
    <item>
      <title>Improving Traveler Experience Via Alternatives to Roadway/Railway Grade Crossings  </title>
      <link>https://rip.trb.org/View/2646961</link>
      <description><![CDATA[There are more than 240,000 at-grade crossings between railroads and roadways in the U.S. and as the number of freight trains increases, the times of interface and blocked crossings also increases. USDOT reports numerous driver complaints about delays and frequent disruptions, and in some cases, there are delays to emergency vehicles due to excessive numbers of blocked trains. Work is underway to continue documentation and to consider strategies and address the frequent and repeated delays caused by long trains. The most requested remedy is grade separation. Grade separations are extremely expensive, and planning and construction lead times are long, so there is a need to identify other more short-term strategies that will offer travelers and emergency responders options to waiting on the long trains.  

The focus of this research will be Fort Bend County and Harris County, Texas, which include major freight corridors from Port Houston, the 3rd largest container port in the country. Between the two counties, there are at least 11,000 at grade crossings. Specifically, this work will assemble delay time data showing frequency and duration for the identified railroad crossings. The team will conduct literature review and on-line and in-person conversations to determine options and strategies underway by entities (e.g., railroad operators), municipalities, and others to address better traveler information and options to reduce and avoid delay time. Potential options include cameras noting delays and following with notifications to emergency services proximate to locations with frequent delays. The study team will examine whether this information distribution could be expanded to additional users. An additional option to be examined is message signs alerting travelers to blocked crossings in time to adjust their travel route. The expected research outcome is to provide an option to grade separations that will reduce delay time for travelers caused by blocked train crossings. ]]></description>
      <pubDate>Tue, 06 Jan 2026 17:10:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646961</guid>
    </item>
    <item>
      <title>Multi-Sensory System for Railway Track Defect Detection </title>
      <link>https://rip.trb.org/View/2646942</link>
      <description><![CDATA[Railway transportation is essential for moving passengers and freight across the U.S., but accidents continue to pose serious safety and economic risks. In 2022 alone, there were about 950 rail-related fatalities and 6,400 injuries nationwide. While human error and reckless behavior are major contributors, defective track infrastructure is a significant and preventable cause of accidents. Railway tracks are complex systems consisting of steel rails, crossties, fasteners, and ballast, all subject to heavy loads, temperature fluctuations, and environmental impacts. These stresses lead to issues such as broken rails, cracked or spalled crossties, loose or missing fasteners, geometry defects, and cross-level variations. Extreme weather conditions can further cause rail buckling or fracture. Failures in these components can trigger derailments, collisions, hazardous material spills, and major service disruptions. Although manual inspections and specialized vehicles are used, many defects go undetected between inspection cycles. Traditional manual inspections, although reliable for identifying visible rail defects, are labor-intensive and limited in scalability. To improve efficiency, various nondestructive testing (NDT) technologies, such as infrared imaging, acoustic emission, ultrasonic, and electromagnetic techniques, have been used primarily for internal defects. As surface defects become more prevalent, various methods have also been developed for detecting surface-level flaws, which can be broadly categorized into three approaches: static monitoring where sensors at fixed locations provide localized coverage; inspection trolleys which integrate sensors generally in the laboratory setting; and onboard sensing systems which enable real-time detection ahead of moving trains but suffer from high cost with varying imaging quality under different weather and lighting conditions. The primary objective of this project is to develop a comprehensive but low-cost multi-sensory system for railway track defect detection. The system will integrate binocular stereovision cameras, Global Navigation Satellite System / Global Positioning System (GNSS/GPS), and IMU sensors. The scope of this project includes development of a multi-sensory system including controller and field data acquisition, development of real-time data fusion and detection algorithms, and recommendations for system deployment on railway tracks. ]]></description>
      <pubDate>Mon, 05 Jan 2026 23:04:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646942</guid>
    </item>
    <item>
      <title>Quick-Response Research on Long-Term Strategic Issues. Task 55. Impact of Positive Train Control</title>
      <link>https://rip.trb.org/View/2636147</link>
      <description><![CDATA[Positive Train Control (PTC) is a system that increases safety and reduces the risk of accidents by preventing train-to-train collisions, overspeed derailments, protection against improperly lined switches, and unauthorized incursion into work zones. Public transit agencies that operate commuter and intercity passenger rail services have been required to have positive train control (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. Now that the initial implementation has been completed and agencies have been operating with PTC for a significant amount of time, there is a need to better understand the safety benefits of PTC and additional ways PTC infrastructure and staff can be leveraged to improve safety and efficiency.

PTC technology and principles are also being developed for other modes of passenger rail, such as light rail systems to increase safety and reduce the risk of catastrophic accidents. Given the significant cost of implementing and operating PTC systems, there is a great need for examination of how the requirement to install PTC has improved safety and how PTC infrastructure and staff can be leveraged to improve safety and efficiency. Additionally, there is a need to understand how PTC technology can most effectively be deployed in other modes.

The objective of this study is to determine the impact of PTC on safety and how PTC infrastructure and staff can be leveraged.]]></description>
      <pubDate>Mon, 08 Dec 2025 20:06:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2636147</guid>
    </item>
    <item>
      <title>Applying a Safe System Framework to Rail-Related Trespassing Deaths and Injuries in North Carolina</title>
      <link>https://rip.trb.org/View/2604727</link>
      <description><![CDATA[According to the Federal Railroad Administration, in 2023, there were 27 trespassing deaths and injuries on rail corridors in North Carolina, a 23% increase from the number of casualties in 2022. Although various research studies, both outside and within North Carolina, have examined the causes of rail trespassing casualties, preventing these deaths and injuries remains challenging due to the complexities of human behavior and the social and environmental conditions that bring pedestrians into contact with rail lines. Therefore, this research project proposes a new, Safe System-based approach to analyzing and addressing pedestrian rail trespassing incidents. 


The Safe System Approach is a public health paradigm of transportation safety management that holds human vulnerability and human fallibility as critical considerations for how to proactively prevent transportation deaths and injuries. The Safe System Approach has been formally adopted by the United States Department of Transportation and is central to the North Carolina Department of Transportation’s (NCDOT's) Strategic Highway Safety Plan. Applications of the Safe System Approach often entail an assessment of three parameters: road users’ exposure to conflicts, the likelihood for those conflicts to become crashes, and the severity of crashes when they occur. In the context of rail trespassing incidents, implementing the Safe System Approach involves understanding pedestrian exposure to rail crossings and the likelihood of trespassing because rail strikes tend to be severe. If the mechanisms behind exposure and likelihood can  be better understood, then countermeasures can be applied.

To accomplish this Safe System assessment, the research team proposes combining multiple data streams to build a  knowledge base of a model rail trespassing incident so that a systems science-based evaluation method, the AcciMap, can be applied to identify the critical risks that lead to fatal and severe trespassing incidents. The team proposes supplementing data collected for previous NCDOT projects with survey data, literature-derived risk factors, desk reviews, and field visits to establish a foundation upon which the team can apply the AcciMap method. Using the causal links identified through AcciMapping, the team can then identify countermeasures to the risks. The team will translate these methods into reproducible, locally relevant guidance for transportation agencies and local governments. 

The team of researchers from the UNC Highway Safety Research Center and North Carolina A&T University are uniquely poised to conduct this research project. They are national leaders in Safe System research and have completed rail safety research upon which this project will build. The team understands the need for novel thinking to address safety risks while also recognizing the hyper-local focus this analysis requires. They are well-equipped to produce useful resources for practitioners, such as a Safe System-based rail safety checklist and guidance for risk identification and countermeasure selection. A final report documenting the project’s findings will be accompanied by presentation materials for sharing results and a more detailed implementation plan to facilitate uptake by State and local transportation agencies.]]></description>
      <pubDate>Tue, 30 Sep 2025 15:38:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2604727</guid>
    </item>
    <item>
      <title>Evaluation of Signs at Highway-Rail Crossings</title>
      <link>https://rip.trb.org/View/2601423</link>
      <description><![CDATA[This project evaluates the safety impacts of replacing STOP signs with YIELD signs at highway–rail grade crossings. The study examines whether this transition has resulted in measurable reductions in crashes and near misses, while also analyzing key crossing characteristics—including traffic volume, train frequency, visibility, and roadway type—that may influence safety outcomes. Findings will be used to provide data-driven recommendations on whether to retain, modify, or reverse the signage change at specific locations, supporting evidence-based decisions for improving transportation safety.]]></description>
      <pubDate>Wed, 17 Sep 2025 16:18:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2601423</guid>
    </item>
    <item>
      <title>Evaluating Ballast Performance with Freeze/Thaw Cycles</title>
      <link>https://rip.trb.org/View/2573189</link>
      <description><![CDATA[In seasonally cold regions, railroad tracks are subjected to ice formation under sub-freezing conditions and ice thawing under above-freezing conditions due to significant seasonal temperature fluctuations, posing challenges for the maintenance of ballasted railway tracks and operation safety. Currently, little attention has been given to the impact of ice formation and thawing on the permanent deformation of railroad ballast and incidents due to track stiffness variation have not been reported. This proposed research project will investigate the effect of ice formation and thawing on the permanent deformation of ballast through large-scale triaxial cyclic testing, utilizing a newly developed freezing system to simulate frozen conditions. The results will demonstrate the potential track support variation when ballast is subject to freeze-thaw cycles, under the same loading cycles. The rate of permanent deformation will be related to track settlement and help predict track geometry degradation and optimize track maintenance for enhanced track safety.]]></description>
      <pubDate>Mon, 14 Jul 2025 20:12:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2573189</guid>
    </item>
    <item>
      <title>Rapid Detection of Track Changes from In-Motion Data Acquisition Records: Lab Setup and Field Implementation – Year 3
</title>
      <link>https://rip.trb.org/View/2573191</link>
      <description><![CDATA[Track stiffness is a critical parameter influencing infrastructure integrity, safety, and maintenance efficiency. Track stiffness variations over time and space lead to uneven load distribution, track degradation, and increased risk of failure, necessitating continuous monitoring and timely intervention. Current technologies determine stiffness under loaded or unloaded conditions at discrete locations, or through continuous measurements. They are either costly, labor-intensive, or limited in spatial and temporal resolution. The proposed work is a four-year effort to develop an in-motion system that detects track stiffness and stiffness changes in real-time that is free of the shortcomings of existing techniques. The proposed system is an acceleration-based system that uses hybrid signal processing techniques and machine learning for classification. The system consists of three modules: (1) Data acquisition using onboard vibration sensors; (2) Hybrid signal processing on the edge for feature identification and data compression; and (3) Classification and decision support, utilizing machine learning algorithms for characterization of track conditions in predictive maintenance. This proposal is for Year 3 of the research team's current University Transportation Center for Railway Safety (UTCRS) sponsored effort. Year 1 focused on the development of a track stiffness monitoring concept and produced a feasibility study that led to Year 2 work on method development, and validation through simulations and laboratory small-scale testing. Spurred by the findings of Years 1&2, this proposal focuses on the development of an experimental prototype system and its validation through high-fidelity laboratory testing. In addition, the team proposes to develop a digital twin of the experimental prototype to facilitate extensive validation, calibration, and sensitivity studies to enhance accuracy and scalability. The project will enhance track safety, reduce maintenance costs, and improve railway infrastructure reliability by enabling continuous, cost-effective, and scalable monitoring. The research directly aligns with UTCRS’s strategic goals by advancing infrastructure monitoring technologies and contributes to the United States Department of Transportation (USDOT)’s objectives in safety and economic competitiveness.]]></description>
      <pubDate>Mon, 14 Jul 2025 20:04:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2573191</guid>
    </item>
    <item>
      <title>Modeling Special Cases of Longitudinal Resistance in Continuously Welded Rail (CWR)</title>
      <link>https://rip.trb.org/View/2573194</link>
      <description><![CDATA[Continuously welded rail (CWR) is the standard for North American freight railroads due to its advantages in ride quality, fatigue life, and reduced maintenance costs, despite concerns about rail buckling and breaks. Longitudinal rail resistance is a critical parameter for re-establishing rail neutral temperature (RNT) after rail breaks and for mitigating potential rail failures caused by vehicle loading, temperature changes, and maintenance activities. This proposed research builds upon a previous year project and continues the effort to refine and enhance the Finite Element (FE) modeling of rail longitudinal resistance. Specifically, it aims to improve the representation of realistic rail and anchor conditions by integrating new experimental data into the FE models. The research will develop efficient 2D and 3D FE models in ABAQUS that incorporate rail-to-tie friction, anchor slip forces, and tie-to-ballast restraint, using both experimental results (e.g., anchor slip behavior under varying load conditions) and historical data (e.g., rail-sleeper friction and sleeper-ballast resistance). The models will accommodate various rail profiles, tie materials, and geometric configurations, and will be applicable to a wide range of track conditions including frozen ballast, frozen structures, turnouts, crossings, and loading scenarios from vehicles and maintenance activities. The proposed project will be executed through four key interconnected areas of research: (1) Effects of sleeper-ballast on models larger than 4-ft in length using FE modeling in ABAQUS, (2) experimental testing in the laboratory for anchor slippage with various anchor types, (3) sensitivity analysis, and (4) model analysis with various track conditions. ]]></description>
      <pubDate>Mon, 14 Jul 2025 19:49:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2573194</guid>
    </item>
    <item>
      <title>Temperature-Induced Cyclic Loading Effects on Rail Anchor Slip Force</title>
      <link>https://rip.trb.org/View/2573195</link>
      <description><![CDATA[Recently, continuous welded rail (CWR) systems have been widely adopted due to their enhanced ride quality, reduced maintenance requirements, and extended service life for both rails and rolling stock. However, the elimination of joints in CWR introduces challenges, particularly in managing thermal expansion, which can lead to track buckling. A critical factor in maintaining track stability is the Rail Neutral Temperature (RNT) — the temperature at which rails are free of thermal stress. Anchors, which resist longitudinal rail movement, play a key role in managing RNT and ensuring track integrity. While previous studies have largely focused on the static behavior of rail anchors, this research emphasizes the importance of cyclic longitudinal loading, which can simulate daily and seasonal temperature fluctuations. Unlike static loading, cyclic longitudinal loading on the rail-anchor under different temperatures can potentially lead to gradual degradation in anchor performance, slip initiation, or cumulative displacement over time. These effects may be more critical to track stability than static forces alone, especially under service operating conditions. This study will conduct full-scale laboratory testing to investigate the impact of cyclic temperature-induced longitudinal loading on slip force performance for various rail anchor types. By simulating temperature cycles and measuring anchor slip under controlled conditions — including different anchor geometries, installation tightness, and environmental parameters — this research aims to provide an understanding of the long-term reliability of rail anchoring systems under thermal cycling. Also, this study addresses the need to construct a 15-foot full-scale track segment on ballast and wood ties to replicate in-field conditions for the future studies to be performed for this project.]]></description>
      <pubDate>Mon, 14 Jul 2025 19:42:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2573195</guid>
    </item>
    <item>
      <title>Multi-modal AI Agents for Railway Safety</title>
      <link>https://rip.trb.org/View/2573196</link>
      <description><![CDATA[Artificial Intelligent (AI) agents, powered by foundation models, such as ChatGPT, have transformed every aspect of everyday life, in personal and professional settings, and have also started making substantial progress in specializing and producing results in various scientific and engineering domains. In this project, continuing the effort the research team started in Year 2 which entailed the development of a prototype for a large language foundation model for railway safety, the team will work towards developing a multi-modal AI agent for railway safety, which will be able to seamlessly integrate structured and unstructured text (such as accident reports and policy documents) with image data pertaining to a railway crossing and perform a number of tasks such as analyzing, comparing, and contrasting different railway crossings with respect to their risk factors and/or accident history, and come up with safety recommendations specifically tailored to a crossing. The proposed AI agent will combine rich domain expertise and the ability to sift through and analyze vast amounts of data that no human operator or policy maker may realistically be able to, thus empowering large scale data-driven railway safety. ]]></description>
      <pubDate>Mon, 14 Jul 2025 19:24:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2573196</guid>
    </item>
    <item>
      <title>Non-Motorist Safety at Highway-Rail Grade Crossings: Developing a Crash Prediction Model with Integrated Non-Motorist Exposure – Year 3</title>
      <link>https://rip.trb.org/View/2573721</link>
      <description><![CDATA[The proposed research objective is to address the safety of non-motorists at highway-rail grade crossings (HRGCs). In Phase III (Year 3), the research team will focus on a survey of pedestrians and bicyclists in Federal Region VII (NE, IA, KS, and MO) to investigate their characteristics, perceptions of safety at crossings, understanding and comprehension of traffic signs/signals at crossings, and their self-reported unsafe movements at HRGCs. The focus on this region ensures a representation of the Midwest, where the extensive rail network creates safety issues for non-motorists. Incidents involving non-motorist users at HRGCs are often underreported or overlooked, yet statistics reveal that they significantly contribute to overall fatalities and injuries at these locations. Pedestrians and bicyclists at HRGCs are particularly vulnerable due to the lack of adequate protective barriers or warning devices. In 2022, the Federal Railroad Administration (FRA) recorded 2,202 crashes at HRGCs, leading to 269 fatalities and 827 injuries nationwide. Furthermore, during the same year, there were 1,157 reported incidents of pedestrian rail trespassing, resulting in 606 fatalities and 551 injuries. These numbers emphasize the need for a comprehensive understanding of the risks associated with non-motorized users at HRGCs and identification of crossings where non-motorized users may be susceptible to crashes. This research will contribute to a deeper understanding of safety hazards associated with HRGCs and lead to the development of autonomous mitigation technologies for rail crossings. ]]></description>
      <pubDate>Mon, 14 Jul 2025 19:22:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2573721</guid>
    </item>
    <item>
      <title>Design of Dynamic Train Crossing Estimation and Real-Time Traffic Control Systems for 
Grade Crossing Safety
</title>
      <link>https://rip.trb.org/View/2573792</link>
      <description><![CDATA[This research project aims to enhance safety and traffic flow efficiency at rail grade  crossings by developing a real-time traffic operation control system that dynamically coordinates traffic signals with train movements. A key innovation is the design of dynamically updated train crossing windows for all at-grade crossings within a region. By leveraging available real-time train tracking data, including location, speed, and projected arrival times at crossings, the system will continuously predict and update crossing windows. These predictions will be integrated into adaptive traffic signal control algorithms, ensuring efficient vehicle movement while reducing delays and potential conflicts at crossings. The proposed system will consist of three core components: (1) a real-time train movement prediction model using the Global Positioning System (GPS), trackside sensors, and historical train operation data; (2) a dynamic train crossing window estimation module that calculates and updates available crossing opportunities for all grade crossings in the region based on evolving train movement data; and (3) an intelligent traffic signal coordination system that adjusts traffic lights in real time to optimize vehicle throughput while ensuring safety at rail crossings. This approach will minimize unnecessary traffic stops, reduce congestion, and improve safety by providing a network-wide, adaptive traffic management solution. The findings of this research will contribute to the advancement of intelligent transportation systems, offering practical applications for traffic agencies and rail operators in improving grade crossing safety and efficiency.]]></description>
      <pubDate>Mon, 14 Jul 2025 19:15:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2573792</guid>
    </item>
    <item>
      <title>Intelligent Aerial Drones for Railroad Track Traversability Assessment, Intrusion Detection 
and Integrity Evaluation</title>
      <link>https://rip.trb.org/View/2573854</link>
      <description><![CDATA[Aerial drones have been increasingly used in railroad operations as they offer an effective low-cost solution that can be easily deployed and efficiently support the human efforts in inspection and monitoring activities. This proposal outlines the development of an advanced system leveraging intelligent aerial drones for comprehensive railroad track monitoring and evaluation. The project serves as the integration phase (phase 3) of two University Transportation Center for Railway Safety (UTCRS) projects that in the previous two phases developed related technology: (i) a project on the development of Intelligent Aerial Drones for Traversability Assessment of Railroad Tracks, and (ii) a project on the development of AI-enabled system for Track Intrusion Detection and Track Integrity Evaluation. Through this integration, an intelligent aerial drone will be developed able to carry equipment for the autonomous inspection of railroad tracks with the following capabilities: (i) Visual-based identification and autonomous following of the track; the system will be able to work even in GPS-degraded environments (tunnels, dense forests); (ii) Collision avoidance capability where the drone senses and avoids obstacles; (iii) Track centering capability where the drone follows the same line regardless of the number of tracks in the field of view; (iv) Identification and mapping of any obstacles identified blocking the line; (v)Intrusion\Trespassing detection; and (vi) AI-based Detection, Classification, Tracking, and Situational Evaluation. This innovative solution promises to improve operational efficiency, safety, and cost-effectiveness in the management of railroad networks, while minimizing downtime and enhancing system reliability.]]></description>
      <pubDate>Mon, 14 Jul 2025 19:12:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2573854</guid>
    </item>
  </channel>
</rss>