<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=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" 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>E-Ticketing Technologies for Efficient Asphalt Ticket Collection and Quantity Calculation</title>
      <link>https://rip.trb.org/View/2596426</link>
      <description><![CDATA[The Federal Highway Administration (FHWA) Every Day Counts program has highlighted e-Construction as a proven innovation to shorten the project delivery process, enhance roadway safety, reduce traffic congestion, and integrate automation. The New Mexico Department of Transportation (NMDOT) has implemented many e-Construction innovations. The use of e-Ticketing software on construction projects will complement existing NMDOT e-Construction technologies.   
The decision was made to use an e-Ticketing platform by Haulhub Technologies on NMDOT projects during the 2024 project season. Biweekly meetings with Haulhub and the NMDOT e-Ticketing implementation team commenced in January 2024. User accounts for the implementation test were created for training and testing purposes.  An NMDOT landing page with a picture and a quote from the technology champion was created as an initiative introduction to contractors and vendors. 
Two Haulhub-led e-Ticketing training sessions were scheduled with construction crews and audit sections throughout the state. The sessions were held virtually and covered how to interact with ticket data on the mobile application online (how to mark tickets as delivered, input notes, and take pictures) and offline, how to submit a missing ticket report, and how to interact with the web interface including flex grid overview and daily and delivery reporting options.
A list of construction and audit users was sent to Haulhub for account creation in preparation for the training sessions. Once the accounts were created, users were able to interact with the e-Ticketing platform in advance of the training sessions. This interaction allowed users to formulate questions to ask during training sessions.   
E-Ticketing was optional for contractors starting on the July 2024 bid letting. When projects were let and subsequently awarded, the contractor was allowed to use e-Ticketing technology on their project. The contractor was reimbursed for the cost of any associated equipment via a bid item in the contract and submitted invoices. Contractors were notified of the potential for e-Ticketing use on projects via the Notice To Contractors (NTC). Information on the use of e-Ticketing was also dispersed through partnership associations with the Associated Contractors of New Mexico (ACNM) and the Asphalt Pavement Association of New Mexico (APANM). Contractor use of e-Ticketing will be mandatory starting in the September 2025 bid letting for all projects over 10,000 tons of asphalt.

]]></description>
      <pubDate>Wed, 03 Sep 2025 12:57:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2596426</guid>
    </item>
    <item>
      <title>A Multi-Sensor System for Identifying Threats from Cyber-Compromised Transport of Hazardous Materials</title>
      <link>https://rip.trb.org/View/2301352</link>
      <description><![CDATA["This proposal extends previous CARMEN+ research on LiDAR detection of anomalous trajectories of potentially cyber-compromised autonomous heavy-duty trucks at Southern California ports. In this research we propose to focus on heavy-duty hazardous material transport vehicles (HMTVs) that have the potential to inﬂict signiﬁcant damage to critical infrastructure and/or injury to urban populations because of their volatile payloads (which can include explosives, toxic chemicals, molten metals, radioactive materials, and weapons). Both autonomous and human-driven HMTVs may be controlled and rerouted for nefarious purposes in cyber-compromised environments.
Currently, monitoring of most HMTVs on US highways relies on manual intervention from law enforcement, which is cumbersome and inedicient, making comprehensive surveillance of these sensitive vehicles tenuous at best and ensuring full compliance with their designated routes all but impossible. We will develop a system based on multi-sensor fusion, independent of GPS, to identify and track such vehicles to determine anomalous behavior, such as a change in routing to sensitive and prohibited routes. The system could be widely deployed to accurately detect and track HMTVs, especially those with elevated security concerns, that deviate from authorized routes. Such detection will provide relevant authorities with critical information for improved oversight and response. This will be accomplished through development of an anomaly detection model based on ﬁeld data that will be capable of distinguishing between non-threatening HMTVs and ones that possess higher risks to infrastructure and populations if they are cyber-compromised, and by leveraging existing federal and state HMTV routing regulations (using California as a case study).
This project will integrate data from multiple existing sensors, such as roadway inductive loop detectors, automated license plate recognition (ALPR) systems, and video surveillance cameras. The system design will also include interfaces for future LiDAR sensor integration. Such a fusion framework will process data from combinations of some or all of these sensors at multiple freeway locations to achieve more accurate vehicle identiﬁcation. Unlike traditional vehicle classiﬁcation methods, the system will learn to recognize ""normal"" HMTV conﬁgurations and operational
patterns, and trigger an alert based upon detection of deviations. This method will not only identify"

"HMTVs such as fuel and liquid nitrogen tankers, but will be particularly edective in detecting vehicles designed to haul rare but potentially highly destructive commodities such as explosives, toxic chemicals and molten metals."]]></description>
      <pubDate>Mon, 04 Dec 2023 17:21:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2301352</guid>
    </item>
    <item>
      <title>Active Traffic Monitoring through Camera Networks with Automatic Camera Calibration for Pan – Tilt – Zoom Cameras</title>
      <link>https://rip.trb.org/View/2196804</link>
      <description><![CDATA[This research focuses on automated processing of video from a persistent surveillance camera array to extract traffic data from a 25 square mile area.  In previous research, the research team developed an automated traffic surveillance system capable of processing aerial camera array imagery to extract valid and useful traffic data for diverse applications. In this research, the team continues to improve the system’s capability by adding a novel multiple hypothesis tracking capabilities to improve vehicle tracking in congested traffic and adding a location identification algorithm to map vehicles throughout a network.  This evaluation has shown that the proposed system is capable of collecting speed, density, and volume data with an acceptable level of accuracy for many applications. The mapping of vehicles for a sample area was also successful.  With further research, improved video preprocessing, enhanced resolution, and a higher frame rate, the accuracy of tracking vehicles can be improved significantly which will eventually allow the envisioned system to be able to accurately map the location of all vehicles throughout a camera array image sequence. A digital real-time “traffic map” created by the envisioned system will provide a robust data set where data mining methods could be applied to enhance traffic management and provide data for a variety of traffic studies.   A connected vehicle camera array application can open up plenty of possibilities in real-time traffic surveillance where erratic drivers can be identified automatically and warnings or even shut down commands can be sent to the erratic vehicles.  The active sensing capability of such a system can potentially prevent some incidents from occurring thereby increasing safety and reducing incident induced traffic congestion. ]]></description>
      <pubDate>Wed, 14 Jun 2023 11:51:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2196804</guid>
    </item>
    <item>
      <title>Installation of On-Bus Mobile Ticket Validators and Development of an Origin-Destination-Transfer (ODX) Model</title>
      <link>https://rip.trb.org/View/2062446</link>
      <description><![CDATA[The Pioneer Valley Transit Authority in western Massachusetts will receive funding to install on-bus technology to modernize fare payment and data collection. In addition to improving the rider experience, the new fare system will include interactive dashboards to track rider travel patterns to support service planning and other modeling efforts.]]></description>
      <pubDate>Tue, 15 Nov 2022 16:17:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2062446</guid>
    </item>
    <item>
      <title>Documenting and Verifying Environmental Commitments</title>
      <link>https://rip.trb.org/View/1957109</link>
      <description><![CDATA[State departments of transportation (DOTs) and other transportation agencies routinely establish commitments to complete specific environmental impact avoidance or mitigation as part of project planning and design under the National Environmental Policy Act (NEPA) and related federal and state laws and regulations. Federal agencies and state DOTs are responsible for ensuring these legally binding commitments are implemented throughout the life of a project and ultimately fulfilled. Proper implementation of environmental commitments affects all phases of project delivery including planning, design, construction, operations, and maintenance.

Tracking environmental commitments is essential to ensuring that specific commitments are implemented. DOTs face challenges in the successful documentation and verification of such commitments. Common challenges include inconsistencies with terminology and language and failure to ensure that commitments are incorporated into design, construction, operations, and maintenance. Lost and unfulfilled commitments can lead to legal issues, violations and fines from regulatory agencies, loss of public trust, and ultimately poor environmental outcomes.

Research is needed to provide a comprehensive approach to documenting and verifying environmental commitments. 

OBJECTIVE: The objective of this research is to produce a documentation and verification process workflow with tools to facilitate the definition, implementation, monitoring, verification, and maintenance of environmental commitments established through the life of a project. 

]]></description>
      <pubDate>Tue, 24 May 2022 10:57:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/1957109</guid>
    </item>
    <item>
      <title>SPR-4605:  Automated Record Keeping for Maintenance Operations via Tracking of Maintenance Vehicles using Telematics Tracks</title>
      <link>https://rip.trb.org/View/1898908</link>
      <description><![CDATA[INDOT is currently working with Parsons to deploy telematics devices in fleet vehicles. These devices can integrate vehicle sensor data from CANbus and other means with GPS positions and time to create a record of vehicle activity, which may be synchronized to the cloud in real time. With this new capability there is need to use the new data to automate the provision of management insights and to create visualizations and dashboards to serve the results inside of INDOT. This proposal will do this in the context of important summer maintenance activities such as pavement patching.]]></description>
      <pubDate>Mon, 20 Dec 2021 15:45:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/1898908</guid>
    </item>
    <item>
      <title>CubeSat Cluster Deployment Tracking</title>
      <link>https://rip.trb.org/View/1537228</link>
      <description><![CDATA[Clustered CubeSat deployments, where dozens of CubeSats are released over a short time span, represent a relatively new and challenging problem for detection, tracking, and space traffic management. Over the last few years CubeSat missions have reported on the difficulty of relying on Two Line Elements (TLEs) provided by the Joint Space Operations Center (JSpOC) early in the mission. Researchers and industry professionals have begun taking an interest in this scenario. Mainly they are advocating policy changes to push CubeSat developers toward adding navigation aids, in the form of ID beacons or reflectors. We propose to develop a robust solution that leverages, but is not entirely reliant on compliance by CubeSat developers. Our goal is to develop and demonstrate a resilient strategy for the deployment, detection, and tracking of multiple CubeSats. The proposed research for the first year focuses on solving the estimation problem of detecting, tracking and identifying each individual CubeSat in a realistic large scale deployment scenario. Multi-­‐target estimation methods based on random finite set (RFS) statistics, including the cardinalized probability hypothesis density (CPHD) and the labelled multi-­‐Bernoulli (LMB) filter provide the basis for this work. Using the understanding gained from the first year study, the second year will focus on recommending deployment strategies and accelerating the estimation process, using reported data from a subset of cooperative, fully functioning CubeSats in the deployment. The result will be a validated set of algorithms to support and enhance the safety and speed at which future CubeSat deployments can achieve their mission goals. ]]></description>
      <pubDate>Wed, 22 Aug 2018 13:03:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/1537228</guid>
    </item>
    <item>
      <title>Tracking and Monitoring Suborbital Commercial Space Vehicles</title>
      <link>https://rip.trb.org/View/1537219</link>
      <description><![CDATA[Monitoring the launch and on-orbit health of space-based assets will enhance and improve existing capabilities for safe and successful use of the near-Earth environment for scientific, military, and commercial purposes. This task will also help build a body of knowledge to assist in the development of the appropriate regulatory requirements for the commercial space industry.]]></description>
      <pubDate>Wed, 22 Aug 2018 13:03:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/1537219</guid>
    </item>
    <item>
      <title>SPR-4158: Implementation of Continuous Improvements for INDOT Maintenance (Training and Tracking Process Improvements)</title>
      <link>https://rip.trb.org/View/1464278</link>
      <description><![CDATA[This is a study of existing examples within Indiana Department of Transportation (INDOT) and other states’ Departments of Transportation to find examples of where Continuous Improvement tools and methodology have been successfully used to solve problems and improve efficiency. This research will be shared in a series of workshops where the participants will immediately apply the underlying principles to current problems/opportunities within INDOT to make similar improvements.]]></description>
      <pubDate>Tue, 11 Apr 2017 10:22:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/1464278</guid>
    </item>
    <item>
      <title>NextGenEA/EIS Database Tracking 
</title>
      <link>https://rip.trb.org/View/1368629</link>
      <description><![CDATA[No summary provided.]]></description>
      <pubDate>Mon, 14 Sep 2015 09:07:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/1368629</guid>
    </item>
    <item>
      <title>Real-time Estimation of Transit Origin-Destination Patterns and Delays Using Low-Cost Ubiquitous Advanced Technologies</title>
      <link>https://rip.trb.org/View/1357759</link>
      <description><![CDATA[The Polytechnic Institute of New York University (NYU Poly) research team proposes utilizing Bluetooth technology to estimate origin-destination demands and station wait times of users of the Metropolitan Transportation Authority (MTA) New York City Subway system. If the entrance and exit turnstiles at subway stations are equipped with Bluetooth receivers, it is possible to capture Origin-Destination (O-D) information for some percentage of the riders with visible Bluetooth devices. The riders who have electronic devices such as most cell phones, iPods, and computers carry unique information in their devices' Bluetooth media access control (MAC) address. This information can be used scrambled and used anonymously to detect the origin and destination of riders by matching data collected at entrances and exits from the system. Assuming that visible Bluetooth (BT) devices are uniformly distributed among the riders, it is possible to estimate a transit O-D matrix for the entire system not only on a daily basis but also over a time period allowing the agency analyze time-dependent OD demand for different station pairs. Moreover the same BT sensors proposed by the research teams will capture waiting times of the same sample of transit riders at fixed locations in each station. This information will then be converted average hourly, daily, weekly delays that can be used in conjunction with OD matrices. Estimation of daily and hourly OD demands and delays is important for transit agencies because it can help improve their operations, reduce delays, and save money, among other benefits. As a low-cost and easy to implement alternative to surveys or other advanced technologies, the research team proposes tracking anonymous Bluetooth IDs using inexpensive, small and easy to deploy Bluetooth detectors / readers with specialized software developed by the research team. Following a literature review and device testing, a series of one-day pilot tests will be conducted in coordination with the MTA to iron out all of the possible hardware and software issues. Following further consultation with the MTA, a full one week to one months test will be conducted with continuous data collection and monitoring to assess the feasibility and usefulness of long-term data collection using the proposed sensor technology. Two software tools to post process the collected data and to perform self Real-time Estimation of Transit Origin-Destination Patterns &amp; Delays Using Low-Cost Ubiquitous Advanced Technologies Region II UTRC 2012-2013 Faculty-Initiated Research Proposal ii diagnosis and remote data acquisition functions will be developed as part of the overall research project. The results and recommendations will be provided to the MTA and other interested transit agencies.]]></description>
      <pubDate>Wed, 17 Jun 2015 01:00:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/1357759</guid>
    </item>
    <item>
      <title>Inland Waterway Shipment Management System</title>
      <link>https://rip.trb.org/View/1357366</link>
      <description><![CDATA[The inland waterway network is perfectly positioned to increase its prominence in the nation's supply chain system.  Increasing fuel costs, environmental concerns, and changes in the freight distribution network due to the widening of the Panama Canal are pressing shippers to find better ways to move their product from manufacturer to market.  Inland waterways offer several key advantages when compared to traditional modes of transport.  Specifically, the cost per ton of moving freight via inland waterways is significantly lower, and waterway freight movement is more environmentally friendly than other transport options. To capitalize on these opportunities, it is essential that the inland waterway industry move toward increased supply chain management efficiency.  Currently, some data is shared among interested parties with regard to inland waterway freight movements.  However, this data is often not sent in real-time and is seldom processed in a timely manner.  The coordinated use of real-time tracking, electronic manifests, and electronic route plans will allow stakeholders of inland waterways to better utilize the network via the Inland Waterway Shipment Management System (IWSMS).  Furthermore, the creation of the IWSMS will help to greatly increase the efficiency of inland waterways and improve the competitiveness of the United States in the global economy.]]></description>
      <pubDate>Fri, 12 Jun 2015 01:01:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/1357366</guid>
    </item>
    <item>
      <title>Design of a Decision Support Tool for Nutrient Credit Exchange Feasibility in Stormwater Regulatory Compliance</title>
      <link>https://rip.trb.org/View/1326279</link>
      <description><![CDATA[Transportation infrastructure projects require mitigation of water quantity and quality impacts under the Clean Water Act. The traditional approach for accomplishing this mitigation need has been to construct on-site Best Management Practices (BMPs), typically in the form of stormwater detention basins, or increasing through Low Impact Development (LID) tools to collect, store, and treat runoff from roads and bridges. These BMP and LID approaches have the same ultimate goal of onsite reduction of runoff caused by increased impervious surface.  Water Quality Trading (WQT) offers a different approach for mitigating the impacts of transportation infrastructure where the mitigation needs are addressed offsite, potentially miles away, rather than onsite.  WQT is just beginning to be explored and has not yet been widely adopted by departments of transportation (DOTs). While the concept of markets and trading of pollution credits is not new, it has been primarily applied for atmospheric rather than water pollutants. Recent legislation in Virginia allows the Virginia Department of Transportation (VDOT) to use WQT as a means for offsetting the impact of transportation infrastructure projects on water quality and quantity. Given this, there are important questions that must be addressed as to the sustainability of WQT compared to onsite treatment options.  Sustainability is often considered as three pillars: economic, environmental, and societal. Current work at the University of Virginia (UVA) is exploring the economic aspects of WQT vs. on-site BMP construction, but there has been insufficient work to understand the environmental and social impacts of WQT. One of the key environmental impacts of WQT, distinguishing it from other pollutant trading applications, is the need to track the location of loadings and load reductions along river networks. Therefore, the goal of this study will be to create a Geographic Information System (GIS)-based approach for locating BMPs and WQT banks both within the context of existing transportation infrastructure but also within the context of key water and environmental resources.  The GIS-based framework could be implemented in collaboration with the VDOT Information Technology Division (ITD). ITD maintains extensive GIS resources for VDOT including both data and tools that are accessed by the public and across VDOT.  The expected benefits of this work are the ability to geolocate and track estimated loadings from transportation infrastructure to water bodies and explicitly track important upstream/downstream relationships along pollutant sources and nutrient credit banks (pollutant reductions). Using such a framework, it will be possible to better understand the net environmental impact of the decision to either use on-site or off-site approaches to mitigate water quality impacts.]]></description>
      <pubDate>Tue, 07 Oct 2014 01:00:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/1326279</guid>
    </item>
    <item>
      <title>Assessment of Traffic Congestion in Anchorage Utilizing Vehicle-Tracking Devices and Intelligent Transportation System Technology</title>
      <link>https://rip.trb.org/View/1307046</link>
      <description><![CDATA[Traffic is increasing in most urban cities around the world, with Anchorage being no exception. According to the United States Census Bureau, the population of Anchorage has increased by over 9.0% (8000 people per year) since the year 2000 [18]. With an increase in population comes an increase in the number of vehicles on the road, which adds to traffic congestion. The exact impact of this increase is not known because the current means of determining congestion in Anchorage is through vehicle counters and sparsely-placed video cameras (that may or may not be monitored). In addition, the only ways drivers in Anchorage can be informed of current traffic conditions is through radio and television broadcasts and 511 information (which is not always updated in a timely manner). Vehicle-tracking devices utilizing a vehicle-to-infrastructure (V2I) architecture have already been installed in 15 vehicles in the city of Anchorage, and this project will install these devices in an additional 30 vehicles. Further, taxi fleets, emergency response vehicles, transit vehicles, shipping vehicles, navigation system companies, and any other organizations that may be tracking speed, location, and direction of vehicles will be contacted to attempt to leverage the data that is already being collected. From all of the data that is being collected and will be collected, a realistic measurement and understanding of congestion in Anchorage will be determined. As more vehicles are equipped with tracking devices, the data will become more accurate and will include a larger range of the city and state. In addition, the Department of Transportation (DOT) does not currently have much data on the origin and destination of individual vehicles, and this project will provide that information. The overall delay experienced by individual drivers and the extent of cut-through and spill-over traffic due to congestion will be determined. The data gathered in this project will all be anonymously exposed in a public web interface called FreeSim (http://www.freewaysimulator.com, Figure 1) that can be viewed by anyone. The current speeds of the tracked vehicles will be displayed using different colors on the roads, as well as showing exact speeds when hovering the mouse over a road or vehicle. Historical data will be maintained so that algorithms can be executed to determine daily, weekly, monthly, seasonal, and annual changes in traffic. In short, this project will provide the DOT, the population of Anchorage, and anyone in the world with data about the current state of traffic on the roads of Anchorage and Alaska. The novelty of this project is that the data will be gathered from a continuous flow of data rather than at discrete locations, as is the case with many traffic analysis tools currently in use. As additional resources becomes available, drivers will have the ability to view the current traffic conditions during their commutes, and further, the system will be able to send drivers the streets to take to minimize the amount of time spent in traffic.]]></description>
      <pubDate>Thu, 24 Apr 2014 01:01:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/1307046</guid>
    </item>
    <item>
      <title>Gathering of Vehicular Parameters in a Vehicle-to-Infrastructure Intelligent Transportation System</title>
      <link>https://rip.trb.org/View/1307044</link>
      <description><![CDATA[The overall vision of this project will include three different phases. Phase 1 is the Device Integration Phase, Phase 2 is the Device Installation Phase and Phase 3 is the Gathering and Analysis Phase. In the Device Integration Phase the project will obtain a couple On Board Diagnostics (OBD) tracking devices to use for integrating the additional sensors for vehicle emissions and tire slippage. After successfully creating the additional sensors, the Device Installation Phase will commence, allowing installation of up to 30 devices (including the additional sensors) in vehicles. As these vehicles travel during their normal commutes, the Gathering and Analysis Phase will allow us to create the four applications described in previous sections (assessing congestion, air quality impact due to cold weather, fuel consumption &amp; operating cost analysis due to cold weather, and vehicle slippage identification).]]></description>
      <pubDate>Thu, 24 Apr 2014 01:01:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/1307044</guid>
    </item>
  </channel>
</rss>