<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=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSJhbGwiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMTYiIC8+PC9wYXJhbXM+PGZpbHRlcnM+PGZpbHRlciBmaWVsZD0iaW5kZXh0ZXJtcyIgdmFsdWU9IiZxdW90O1Byb3RvdHlwZXMmcXVvdDsiIG9yaWdpbmFsX3ZhbHVlPSImcXVvdDtQcm90b3R5cGVzJnF1b3Q7IiAvPjwvZmlsdGVycz48cmFuZ2VzIC8+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>Innovations Deserving Exploratory Analysis--The Transit IDEA Program. IDEA 106. Sustainable Tire Tread Nanotechnology for Battery Electric Buses</title>
      <link>https://rip.trb.org/View/2572330</link>
      <description><![CDATA[Battery Electric Buses (BEBs) place severe mechanical stress on tires. A long-term study of BEB fleets by the National Renewable Energy Laboratory (NREL) has shown a 45% reduction that the average BEB tire life. Excessive tire wear has been reported to contribute over 140% increase in tire costs in BEB maintenance budgets. Further, tire maintenance costs have been estimated to be almost 143% higher than for compressed natural gas (CNG) bus fleets.

This project proposes a new material modification and processing in the manufacture of BEB tires. The method involves preparation of siloxane oligomers, adding a compatibilizer to avoid premature coagulation and phase separation, mixing the compatibilized siloxane oligomer to the natural rubber (NR) latex, followed by controlled shear blending and drying. The process creates a reinforced network of silica and NR within the tire tread and is essentially a drop-in technology to the current tire tread material  manufacturing process. By controlling silica dispersion, the process breaks the tradeoffs between high tire wear and low rolling resistance, allowing for performance gains not attainable with current methods and materials.

The work plan will involve preparing a new tire formulation of NR and compatibilized siloxane  to make prototype tires. The prepared material will be characterized using standard test methods for rubber materials. Prototype tires will be manufactured, and their performance compared with EPA SmartWay low rolling resistance tires and the tires of the collaborating transit partners. A fleet test plan and data collection strategy will be developed in collaboration with the transit partners. A transit impact analysis will also be conducted to evaluate the impact and adoption strategy. The material and tire manufacturing process will be scaled beyond laboratory to the pilot production stage along with the compound manufacturing process. Two transit agencies have agreed to run the tests on their fleets. 

Benefits to the transit agencies of this new BEB tire technology will include lower maintenance costs, increased vehicle range and energy efficiency, increased use of low rolling resistance tires, less frequent tire replacements, and reduced labor for tire maintenance. Taking into account their longer life, these tires are also estimated to reduces the total BEB tire costs by 44%.]]></description>
      <pubDate>Tue, 08 Jul 2025 16:59:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2572330</guid>
    </item>
    <item>
      <title>Development of a Prototype Turnkey Artificial Intelligence Aided Automated Trespassing Detection Solution Based on Stationary Cameras



</title>
      <link>https://rip.trb.org/View/2572329</link>
      <description><![CDATA[This Type II IDEA project will develop and test a prototype turnkey artificial intelligence aided trespassing detection system.  The system consists of integrated hardware (solar security trailer, networking equipment, etc.) and software that was proven in an earlier project funded by the Federal Transit Administration and Federal Railroad Administration. This system will be developed and tested in collaboration with the industry partner, SunRail, a commuter rail system in the greater Orlando, Florida area. The system hardware will be assembled and installed at selected locations. Data will be collected in those locations for 12 months, and the information will be analyzed to provide actionable safety data to SunRail, the industry collaborator. SunRail will install fencing along their right-of-way. This system could be used to gather trespassing data before and after the fencing installation to evaluate the effectiveness of the solution. At grade crossings, violation data could be used to justify upgrades like the installation of quad gates, gate skirts, or dynamic envelopes based on the types of violations observed. This data can improve trespassing mitigation decision making and support grant applications for further actions. Following this task, sample video data will be collected and analyzed to ensure system accuracy and data quality. The developed system will benefit railroad industry by enabling the collection of previously unavailable trespassing and grade crossing violation information.  It is rather unfeasible to have railroad staff manually annotate video feeds to acquire trespassing data.  This system, on the other hand,  will automatically watch and understand trespass behavior from video feeds at remote locations. Trespass and grade crossing violation information will be aggregated in a trespasser database, presenting users with a video clip of the trespassing event and corresponding metadata (time, weather, type: person, car, motorcycle etc.). Trends and common behaviors can be determined once enough of these events are aggregated.]]></description>
      <pubDate>Tue, 08 Jul 2025 16:55:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2572329</guid>
    </item>
    <item>
      <title>Local Resonances-based NDE Technique for Rail Flaw Detection</title>
      <link>https://rip.trb.org/View/2572328</link>
      <description><![CDATA[Rail internal defects have been one of the leading causes of track-related accidents. Rail internal defects can reduce cross-sectional area and introduce stress concentration. Moreover, they can develop with normal, rapid, and sudden growth rates. If left undetected, internal defects can result in broken rails, train accidents, and derailments, where sudden rail rupture can occur without warning. Accurate and reliable rail flaw detection is therefore critically important for improving safety and reliability and minimizing the risks of accidents induced by rail internal defects. Nondestructive evaluation (NDE) techniques, including roller search unit (RSU), ultrasound A-Scan, and phased array, have been employed to detect rail internal defects but their performance or accessibility has been limited. This project will develop a new technology using a newly identified wave propagation phenomenon  (local resonances in rails) for rail defect detection.  These local resonances feature highly localized energy and signature frequencies that are governed by the geometry and material properties of a rail. These local resonances were found to be sensitive to internal defects over the full rail cross-section and are easy to measure. A low-cost contactless acoustic sensing prototype will be developed that would generate local resonances in rails. These resonances will provide flaw detection capability over the full rail section. The prototype’s sensing configuration will be simple and robust and, compared with existing NDE techniques, it will not require sophisticated/expensive sensors or data acquisition systems. It will combine fast data collection with efficient data processing to produce timely critical damage alert. If successful, the project is expected to have a significant impact of the current state of practice for accuracy and practicality with regard to determining the presence and severity of internal rail defects.

 ]]></description>
      <pubDate>Tue, 08 Jul 2025 16:46:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2572328</guid>
    </item>
    <item>
      <title>Innovations Deserving Exploratory Analysis--The Transit IDEA Program. IDEA 108. TrainMate - Let's Make Public Transportation Public</title>
      <link>https://rip.trb.org/View/2572327</link>
      <description><![CDATA[This is a follow-on project to a recently completed Transit IDEA Project T-100 in which the research team  developed the designs and studied the feasibility of a robotic system, TrainMate, to assist passengers with disabilities at non-accessible street level train transit stations. The project successfully produced electromechanical blueprints as well as software components of the proposed robotic system and ran them through end-to-end software simulations to ensure that they could work in real-life scenarios and be used to take the research to  the prototyping phase  In this Type 2 project, a prototype version of the TrainMate will be built and its capabilities demonstrated in enabling individuals using mobility devices to independently board and deboard trains efficiently and conveniently. 

The project work will involve building a physical full-scale prototype unit with focus on meeting the specific requirements of the users and the transit agencies. In the earlier proof-of-concept project, 3-D models of the system were designed, and the integrated system components were tested in various simulation environments. The results validated the proposed prototype design. Several software systems were  surveyed or developed dealing with artificial intelligence, autonomous navigation, machine vision, robotic operating system, and the speech to text and text to speech capability to establish the feasibility of the integrating software components with the electromechanical components.  In this follow-on project, those software systems will be further developed and tested on the prototype platform to ensure their applicability and useability. The fully working prototype unit (robotic base and the wheelchair lift module) including the sensor network and software components, shall pass all technical verifications and tests conducted in laboratory set-up in a mockup train station. The system will be made ready for pilot program testing. New Jersey Transit will allow access to one of its railyards to test the system in a safe and controlled environment using real railcars before moving on to the public train stations. The TrainMate system will be taken to different train stations identified by the NJ Transit and tested over several months in actual public transportation environments to assess its field readiness, useability, and applicability to serve the intended use. A passengers’ survey will be conducted for their feedback on their satisfaction with the TrainMate system. Calibration and enhancement of the system will continue. Finally, the project will be concluded with a field demonstration before invited officials and NJ Transit executives  at the New Jersey’s Hoboken train station.

The benefits of the robotic TrainMate system are significant, particularly for disabled passengers who require accessible transportation. With TrainMate, this disadvantaged section of the public will no longer have to rely on assistance from others or deal with limited mobility when using public transportation. It will be a safe, reliable, and convenient way for them to travel with confidence, providing them with a greater sense of independence and autonomy. ]]></description>
      <pubDate>Tue, 08 Jul 2025 16:41:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2572327</guid>
    </item>
    <item>
      <title>Prototyping a Low-cost Roadside Device System for Cooperative Automated Driving</title>
      <link>https://rip.trb.org/View/2425404</link>
      <description><![CDATA[Although significant progress has been made in automated driving technologies, technical challenges still exist, especially for complex Operational Design Domains (ODDs). A low-cost roadside device system, the Connected Reference Marker (CRM) System, has been developed to support CAVs in those ODDs. The CRM system can facilitate CAV localization by providing real-time distance measurement and road geometry changes (i.e., work zones). Therefore, the CRM system has the potential to serve as a gateway system for infrastructure-based cooperative driving automation (CDA) due to its low cost and easy deployment. This project will evaluate the performance regarding localization and road geometry data provision in field experiments. Specifically, this project will build a prototype system and evaluate the localization accuracy in various scenarios; in addition, the prototype system will be used to detect the boundaries of work zones, as improving access to work zone data is one of the top needs identified through the USDOT Data for Automated Vehicle Integration (DAVI) effort. The detected boundaries of work zones will be later translated into a data feed following the Work Zone Data Exchange (WZDx) specification, a national work zone data standard pioneered by USDOT to meet the DAVI requirement and a critical part of the Roadway Digital Infrastructure (RDI) strategy.]]></description>
      <pubDate>Thu, 05 Sep 2024 16:58:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2425404</guid>
    </item>
    <item>
      <title>Towards a Safe, Healthy and Efficient Gig Transportation Workforce</title>
      <link>https://rip.trb.org/View/2325390</link>
      <description><![CDATA["Towards a Safe, Healthy and Efficient Gig Transportation Workforce" is a research initiative that addresses the heightened safety and health risks faced by the gig transportation workforce, particularly in Massachusetts. The gig economy, characterized by independent contractor roles and piece-rate payments, poses unique challenges to drivers/riders, including higher anxiety, fatigue, risk-taking behaviors, and lack of federal regulation protections. This project aims to enhance the overall well-being of gig drivers/riders by focusing on three main objectives: understanding their economic, safety, and health status; developing a decision support system (DSS) prototype to aid in making informed scheduling decisions; and engaging disadvantaged communities in the design and testing of this DSS.

The research involves a collaborative effort between faculty members from Civil and Environmental Engineering and the School of Public Policy, combining expertise in driver behavior analysis, operation optimization, community outreach, and socio-technical ecosystem studies. The research plan includes conducting a literature review, focus groups/interviews with gig drivers/riders, designing and testing a DSS prototype, and final reporting.

The project methodology combines qualitative and quantitative research methods, including focus groups and surveys. The DSS prototype will be designed to help gig workers balance economic gains with safety and health considerations, potentially transforming how gig work is conducted and perceived. The community engagement aspect ensures that the DSS is user-friendly and effective for the target audience.]]></description>
      <pubDate>Fri, 19 Jan 2024 10:38:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2325390</guid>
    </item>
    <item>
      <title>AV4EV - Open-source Autonomous Vehicle software for Open-standard Electric Vehicle platforms</title>
      <link>https://rip.trb.org/View/2292653</link>
      <description><![CDATA[Over the past decade, self-driving capability for all variants of on-street vehicles have promised safer and more efficient transportation. This remains “work in progress” with large unfilled gaps in addressing user-acceptance, safety, ethics, regulation, technology and the business model. The goal is to develop the Open-source Autonomous Vehicle (AV) software for Open-standard Electric Vehicle (EV) platforms, ie. AV4EV paradigm, to help realize safe, reliable, and efficient autonomy for off-street use cases. In particular, the research team focuses on developing the AV4EV Autonomy Essentials Kit (AV4EV-Kit) for known controlled application  domains: logistics (in-warehouse mobile robots), material handling (autonomous forklifts) and airside cargo (autonomous ground support equipment). The AV4EV business model addresses these many smaller domains through simplification and modularity. The EV ‘skateboard’ chassis is orders of magnitude simpler than on-street vehicles (~20 moving parts compared to nearly 2,000 in contemporary vehicle architectures) - supporting standardization of  interfaces for autonomous driving. Modularity allows AV4EV to address autonomous vehicle market sizes of 50K-250K vehicles/year for each use case by enabling component re-use and efficient customizability to meet specific segment needs. If successful, the AV4EV Kit will create a new business category for Autonomy-as-a-Service with plug-n-play hardware and software for rapid prototyping and deployment. Autonomous machines have a serviceable market of $2.9B with a 15.5% growth rate.  The AV4EV Autonomy Essentials Kit enables logistics customers to kickstart their journey of autonomous machines for safe and efficient movement of people and goods, even if their companies have little prior autonomous system development experience. Using the AV4EV-Kit, customers can rapidly prototype EV platforms into autonomous machines in 10 days for brownfield deployments.  The AV4EV Autonomy Essentials Kit is dedicated to lowering the entry barrier of autonomous driving development and deployment. AV4EV-Kit consists of (1) a plug-in-play hardware platform with sensors and compute, (2) an autonomy software stack to achieve essential autonomous driving functions of perception, sensor fusion, mapping, localization, path planning, obstacle avoidance, traffic light recognition and safe control; and (3) a new Software Defined Vehicle approach for autonomous machine software development and testing in the cloud to lower cost of mixed-criticality software and over-the-air upgrades to enhance safety across the vehicle lifecycle and customize for different deployment scenarios. The AV4EV-Kit conforms to the open-source Autoware autonomous vehicle software standard to interface with the EV’s drive-by-wire system for users to easily integrate navigation functions with vehicle control. The AV4EV-Kit incorporates energy-efficient machine learning-based perception, planning and control algorithms developed by the PI’s and Co-PI’s labs and will be tested by commercialization partners on a variety of EV platforms. ]]></description>
      <pubDate>Tue, 21 Nov 2023 21:23:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2292653</guid>
    </item>
    <item>
      <title>Improving Subsurface Non-metallic Utility Locating Using Self-aligning Robotic Ground-penetrating Radar</title>
      <link>https://rip.trb.org/View/2093163</link>
      <description><![CDATA[The project will develop a pre-commercial prototype robotic locating system. This system will use GPS and adaptive ground probing radar sensors to improve the quality of image and location data.]]></description>
      <pubDate>Tue, 03 Jan 2023 13:53:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2093163</guid>
    </item>
    <item>
      <title>Development of a Prototype Smart Hy-Rail Wheel</title>
      <link>https://rip.trb.org/View/2071678</link>
      <description><![CDATA[The Federal Railway Administration (FRA) track safety standards require rigorous visual inspections of tracks based on their operating speed (FRA track class). Often these inspections are carried out using hy-rail (highway/rail) vehicles, with the trained inspector using a set of hand tools (track level, string line, gauges, etc.) to further measure locations that appear to be out of compliance. Currently, bolt-on inspection systems for use on inspector’s hy-rail vehicles such as track geometry measurement systems, can be used to assist and supplement the inspector, but are quite expensive. This research developed a prototype, low-cost, “smart” hy-rail wheel (SmartWheel) for deploying on an inspector’s hy-rail vehicle (or any hy-rail vehicle the railway operates) that assists the inspector in identifying locations in track with certain classes of potential defects, in an autonomous and passive manner. It was intended that the SmartWheel be self-contained, autonomous, and provide alerts to the operator. Additionally, the SmartWheel needed to be inexpensive to implement and provide additional information to the inspector to assist in assessing particular elements of the track condition. The innovative approach utilized a low-cost inertial measurement unit (IMU) integrated into the hy-rail gear along with a combined mechanistic and artificial intelligence (AI) approach to analyze the response data from the IMU to identify particular classes of track defects (or issues). These included profile/surface, cross level, curvature, dipped joints, rail surface defects, rail corrugation, mud spots, etc. This approach does not require a sophisticated algorithm for transforming the IMU data to measurable geometry parameters (which requires additional expensive hardware).  Rather, the system evaluates the IMU response data directly using AI algorithms developed as part of this research. The current status of the product addresses a subset of track geometry parameters. The primary benefit of this product is a safer operating environment through the low-cost implementation of a tool that assists inspectors in an autonomous fashion in locating potential track defects. A secondary benefit is identifying locations with habitual problems where revised maintenance practices can increase safety and reduce overall costs. While not all track anomalies are identifiable through this technology, a significant number of safety related anomalies are identifiable.]]></description>
      <pubDate>Tue, 29 Nov 2022 09:17:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2071678</guid>
    </item>
    <item>
      <title>Development of an Immersive Training Platform for Roadway Construction Workers using Virtual and Augmented Reality Technologies</title>
      <link>https://rip.trb.org/View/2008006</link>
      <description><![CDATA[Work zones play a crucial role in maintaining and upgrading the nation’s roadway infrastructure.  Roadway construction workers, as vulnerable roadway users, experience a significant number of fatalities and injuries in work zones. Safety training is critical in conserving a safe and equal working environment for roadway construction workers. Immersive training based on virtual reality (VR) 
technologies has become an attractive solution for safety training in high-risk environments (i.e., work zones). The goal of this study is to develop an immersive training platform, integrating the state-of-the-art VR and Augmented Reality (AR) systems for roadway construction workers in an efficient, safe, and cost-effective training approach. To achieve this goal, study objectives are to (1) identify critical work zone scenarios for workers’ immersive safety training, (2) determine appropriate VR technologies, (3) design and develop a prototype immersive training platform, and (4) conduct a preliminary test to evaluate the effectiveness of the immersive training platform for construction workers. The expected outputs include a prototype of an immersive training platform, successful experiences and lessons learned, and recommendations for implementing the platform. Through demonstrations, webinars, presentations, and publications, the technologies and products developed in this study will be transferred to the stakeholder (Florida Department of Transportation, District 7) and other agencies. The project results can be the basis for further research and development and teaching materials in graduate courses.

]]></description>
      <pubDate>Tue, 16 Aug 2022 18:21:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2008006</guid>
    </item>
    <item>
      <title>Connected Vehicle Security Metrics and Threat Intelligence</title>
      <link>https://rip.trb.org/View/1971174</link>
      <description><![CDATA[The objectives of the proposed project are as follows: 
(1) Design and develop a comprehensive set of security metrics and visualization system for connected vehicles; 
(2) Implement a prototype Machine Learning (ML) system that will process sensor data for intelligent security analytics; and 
(3) Create a web-based threat intelligence portal for connected vehicles. 
The goal of the project is to enhance and enable the continuous improvement of the cybersecurity posture of the transportation system in Florida through security metrics, intelligent analytics, and threat intelligence.]]></description>
      <pubDate>Thu, 02 Jun 2022 12:33:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/1971174</guid>
    </item>
    <item>
      <title>Mixed Reality for Beyond Visual Line-of-Sight Bridge Inspection Using Robot-Assisted Nondestructive Evaluation (IM-5)</title>
      <link>https://rip.trb.org/View/1969830</link>
      <description><![CDATA[To improve data consistency, work efficiency, inspector safety, and cost 
effectiveness during routine inspections, drones have been increasingly used in recent 
years to support imaging and scanning over the surface of various elements in a bridge 
for surface condition assessment. Most drones are operated manually within a visual 
line of sight and unable to inspect river-crossing bridges completely since not all 
elements can be viewed by a drone operator using a binocular. Even for autonomous 
drones with collision-avoidance features, physical interaction with a bridge for 
nondestructive evaluation (NDE) is currently impossible in practice. An alternative 
solution with robot-assisted remote nondestructive tests in visually blocked areas would 
be desirable during detailed inspection and condition assessment of bridges. 
This project aims to develop a mixed reality (MR) interface that can streamline 
inspection process, analysis, and documentation for seamless data uses from 
inspection to maintenance in bridge asset management by automating access, 
visualization, comparison, and assessment, and to apply the MR interface in a beyond visual-line-of-sight (BVLOS) ultrasonic measurement for the thickness of steel girders 
from a climbing robot.
Scope of Work in Year 1:
(1) Develop a framework of MR-based bridge inspection 
for BVLOS elements
(2) Integrate a NDE device into a climbing robot for remote bridge 
element inspection, and
(3) Evaluate the MR-based inspection for thickness 
measurement of steel plates and lap-spliced joints to understand automated 
measurement precision, accuracy, and statistical variation.]]></description>
      <pubDate>Tue, 31 May 2022 17:10:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/1969830</guid>
    </item>
    <item>
      <title>Deep Reinforcement Learning-based Digital Twin for Risk Improved Decision Making in Transportation Construction</title>
      <link>https://rip.trb.org/View/1948619</link>
      <description><![CDATA[Each state in the U.S. including Louisiana is responsible to oversee an enormous number of
construction and maintenance projects of transportation infrastructure systems such as
highways, bridges, tunnels, and other infrastructure structures. In addition, with the increase in
the need for constructing and repairing transportation systems, the state DOTs are daunted with
the mammoth task of multiple projects and unexpected maintenance caused by recent natural
disasters. This pandemic period has also been a strong driving force for DOTs to consider an
advanced approach to remotely govern and support these multiple transportation projects.
Organizing and overseeing a large number of transportation construction and maintenance
projects that generally entail several miles of a worksite are a critical burden for each DOT. In
addition, it requires manual monitoring of project or construction managers to identify a progress
status, a work activity, and a safety issue in a job site. Because of the projected huge volume,
complexity, significant impacts of future transportation infrastructure projects, it is evident that
we are now facing a critical need to create a means of improving the results of work zone
management and evaluating their impacts on our society. In addition, multiple work zones of a
large-scale highway construction project usually have to be managed and monitored by a
human effort on site, which is slow, inaccurate, and expensive. One primary problem in this
situation is that it has been increasingly challenging for each DOT to consistently monitor
progress of all projects in each state as well as efficiently evaluate work performance. With
limited human resources and time, DOTs in Region 6 States have managed large-scale
transportation construction and maintenance projects by a human inspection and recovered
direct and indirect damages of transportation infrastructure systems caused from the recent
natural disasters. Another critical issue is that this problem has prevented urban-level and
integrated project management. Since it is not feasible to identify the status and the progress of
numerous transportation infrastructure projects in real-time, DOTs cannot flexibly organize
project resources and schedule according to diverse external factors including uncertainties in a
worksite, mobility, natural disaster, and others. In particular, the lack of urban-level project and
progress data is expected to be a critical obstacle for sharing real-time construction worksite
information with autonomous vehicles and self-driving cars.
The primary goal of this project is to identify the characteristics of the digital twin technology that
are applicable to transportation construction and develop a conceptual framework of the
prototype with a participatory sensing concept to improve the construction process monitoring,
performance evaluation, and safety. The digital twin model incorporating the project information
and schedules analyzes on-going activities and conducts thereby urban-level monitoring of all
worksites. Recent research suggests that using only IoT sensors for capturing real-time data
may be insufficient for entirely grasping the real-life situation. Involving participatory sensing
along with IoT sensors for collecting real-time information can be a more efficient approach.
Therefore, the aim of this study is to develop a conceptual framework of a digital prototype for
managing and monitoring transportation construction projects using sound-based real-time data
and participatory sensing along with the IoT sensors. This research involves sound-based data
collection as it was found that audio-based approach can be used for activity identification of
heavy-equipment with relatively high accuracy. Furthermore, sound-data as compared to image
data is lightweight and can be easily processed, does not require a minimum level of
illumination and thus is equally efficient during nighttime construction activities as daytime, and
sound-sensors can capture data from unlimited angles unlike image-sensors.]]></description>
      <pubDate>Fri, 06 May 2022 11:07:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/1948619</guid>
    </item>
    <item>
      <title>Real-time Flood Forecasting for River Crossings – Phase V
</title>
      <link>https://rip.trb.org/View/1945935</link>
      <description><![CDATA[As part of an on-going effort to develop a generic prototype of a flood-forecasting model that is transferable, reliable, and provides actionable information to other locations around the Midwest to provide monitoring and forecasting flood potential at critical infrastructure points, such as bridges, where streamflow gauges are not available, the research team has outlined a series of new activities to achieve their goal.  The team will continue developing a real-time web-based visualization platform to display the model predictions that was initiated under phase I and II Mid-America Transportation Center (MATC) grants.  The efforts will now include a Technology Transfer aspect by interfacing the work with a MATC funded research project at University of Nebraska, Lincoln (UNL) lead by Prof. Tirthankar Roy that aims to implement a snow model for the HLM model. The team will provide a fully functional real-time forecasting system for the Elkhorn river basin in northeast Nebraska, and a corresponding web interface for monitoring an evaluation of forecasts. This work is the first step towards evaluating the feasibility of implementation of the tools outside the state of Iowa. In addition, the team will implement data-assimilation techniques that will help them reduce the difference between model estimates and observations at USGS location and provide metrics of improvement of model predictions. ]]></description>
      <pubDate>Sat, 30 Apr 2022 11:35:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/1945935</guid>
    </item>
    <item>
      <title>Real-Time Emergency Communication System for HazMat Incidents (REaCH) - Phase VI</title>
      <link>https://rip.trb.org/View/1944000</link>
      <description><![CDATA[This research project addresses two issues related to the health of transportation workers, first responders and the public in the presence of hazardous materials: (1) Real-time information on the exposure of hazardous materials to transportation workers and first responders during hazardous material incidents is lacking. (2) Currently, the ability to identify and communicate information regarding hazardous material incidents in real time to all stakeholders is limited. 

At the end of this six-year project, the research team will develop a prototype for a statewide technology system that includes wearable sensor devices, mobile apps and a real-time communication network for stakeholders that can be used during a hazardous materials incident. The new system is called REaCH - Real-Time Emergency Communication System for HazMat Incidents. The REaCH system will include real-time health monitoring of transportation workers and first responders through wearable devices that capture individual health parameters and exposure to hazardous materials. Individual health data and hazmat exposure data will be transmitted to a dashboard that integrates all of the information for the incident commander to monitor.  The incident commander can evaluate if individuals need to be removed from the scene for example, if his or her health status is being compromised, with the goal of minimizing any health-related consequences.  The team is in the third year of their project.  This proposal presents plans for year 6.]]></description>
      <pubDate>Mon, 25 Apr 2022 19:27:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/1944000</guid>
    </item>
  </channel>
</rss>