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    <title>Research in Progress (RIP)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Managing and Sharing Traffic Management Systems Video

</title>
      <link>https://rip.trb.org/View/2558409</link>
      <description><![CDATA[Traffic management systems (TMSs), which integrate advanced technologies, software, and data, are essential tools for enhancing the safety, efficiency, and reliability of surface transportation. These systems play a vital role in helping agencies meet the growing and evolving mobility needs of travelers, service providers, partner agencies, and the general public.

Traditionally, TMSs provided only static images of roadway conditions, but technological advancements have transformed this practice into 24/7 live-streaming video feeds of traffic conditions. Increasingly, individuals and private companies are capturing, scraping, or archiving these video feeds, and often repackaging and selling the data to public or private customers, raising legal, technical, and operational challenges for transportation agencies.

Most TMSs do not record or archive video feeds due to concerns over legal obligations and public information requests, risks of releasing sensitive or personally identifiable information (PII), potential liability from unintended uses, and technical burdens of video management. The rising expenses of data storage and telecommunications add complexity to video management.

Research is needed to help agencies evaluate the implications, benefits, and risks of sharing TMS video.

The objective of this research is to develop a guide for transportation agencies on managing and sharing access to TMS video. The research will identify current practices, challenges, unintended consequences, and opportunities for improvement.]]></description>
      <pubDate>Tue, 27 May 2025 20:58:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2558409</guid>
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    <item>
      <title>Data Analysis for Security and Privacy in Advanced Traffic Management Systems (ATMs)</title>
      <link>https://rip.trb.org/View/2549186</link>
      <description><![CDATA[The project is driven by two main objectives: 1) Real-Time Intrusion Detection Algorithm (DL_TraSec): The first objective involves the creation of a cutting-edge intrusion detection algorithm that harnesses the power of deep learning. This algorithm, named DL_TraSec, is purpose-built to cater to the intricacies of advanced traffic management systems (ATMS). By analyzing real-time data streams, it will identify anomalies and patterns indicative of impending DDoS attacks. The aim is to proactively prevent such attacks before they disrupt the system. 2) Intrusion Detection System (ID_TraSec): Building upon the DL_TraSec algorithm, the second objective is to develop an integrated intrusion detection system, ID_TraSec. This system will not only accurately analyze the ongoing behaviors of potential DDoS attackers but also predict their actions. The ID_TraSec system will act as a comprehensive shield against a variety of DDoS flooding attacks targeting different components of the ATMS infrastructure.

The proposed system's efficacy spans multiple levels of the ATMS architecture: 1) Central Control System: The system will bolster the Central Control System, enhancing its resistance to DDoS attacks and ensuring continuous operation. 2) ATMS Sub-systems (Corridors or Areas): It will secure ATMS sub-systems by vigilant monitoring and prompt response to DDoS threats specific to these zones. 3) Intersection Advanced Transportation Controllers: The system will safeguard the critical intersection controllers, a key component of efficient traffic management. The project's methodology is dynamic and interdisciplinary, combining several elements to form a comprehensive solution. It integrates: 1) Literature Synthesis: A thorough review of existing literature on DDoS attacks, deep learning algorithms, and traffic management systems will inform the project's direction. 2) Conceptual Models: Building upon the literature, conceptual models will be developed to design and structure the DL_TraSec algorithm and the subsequent ID_TraSec system. 3) Real-Time Big Data: The collection and analysis of real-time big data from the ATMS environment will provide the foundation for refining and validating intrusion detection algorithms. 4) Algorithm Development: Novel algorithms will be crafted, leveraging deep learning techniques, to enable the accurate detection and prediction of DDoS attacks in real-time scenarios.]]></description>
      <pubDate>Tue, 06 May 2025 16:44:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2549186</guid>
    </item>
    <item>
      <title>Creating Security Approaches for Advanced Traffic Management Systems (ATMS) against Intersection Signal Attacks (ISA)</title>
      <link>https://rip.trb.org/View/2549187</link>
      <description><![CDATA[This project is an innovative effort to advance the most recent understanding of the security of advanced traffic management systems (ATMS). The project is organized along four unique axes, each painstakingly designed to improve comprehension and actual use of security measures inside the ATMS framework:

1. The creation of a thorough framework that intricately describes the range of potential intersection signal attacks (ISA) while also offering a compendium of efficient mitigation techniques forms the basis of this undertaking. With the use of this framework, practitioners and other interested parties can gain a systematic understanding of new dangers. This framework gives security professionals the tools they need to proactively handle changing security concerns by encapsulating different attack routes and demonstrating techniques for their mitigation.
2. The creation of a strong historical database that is solely dedicated to recording and archiving incidents connected to ISAs is a crucial component of the project. This archive of historical events is more than just a list of things that have happened; it also offers a variety of information about how attacks have changed over time, how they've been conducted, and what has happened as a result. Stakeholders can undertake predictive analysis, foresee prospective trends, and develop preventative security measures with the use of this historical view.
3. The creation of a sophisticated Distributed Access Control Model (AC_TraSec) is a pillar of the project's innovation. The integrity and confidentiality of data across multiple types of nodes inside the highly distributed ATMS environment are ensured by this model's thorough design, which is in line with trusted computing principles. The persistent focus on formal compliance verification, a quality that confirms the reliability of access control protocols, is a distinguishing characteristic of AC_TraSec. In order to protect critical ATMS resources, AC_TraSec makes a crucial step by integrating security and compliance.
4. The finished product of the project is a ground-breaking ATMS security framework. This framework includes the cutting-edge Smart Traffic Security System (STS_TraSec). In addition to smoothly adjusting to the wide variety of heterogeneous Information and Communication Technologies (ICTs) inside the ATMS domain, STS_TraSec is a robust cyber-physical security system that also orchestrates a cogent and harmonious security posture. The project's innovative approach is demonstrated by STS_TraSec, which bridges the gap between cutting-edge technology and the urgent security requirements of a transportation landscape that is becoming more linked.
In short, this project has the ability to completely redraw the lines around ATMS security. The project sets out to improve the robustness, effectiveness, and safety of transportation infrastructures by providing a thorough framework, a historical database, a complex access control model, and a cutting-edge security system. The initiative is in line with the overarching objectives of secure and sustainable urban mobility by proactively addressing the increasing dangers that surround ATMS and by encouraging a culture of continuous development.]]></description>
      <pubDate>Tue, 06 May 2025 16:29:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2549187</guid>
    </item>
    <item>
      <title>Increasing the Resilience of Transportation Systems under a Combination of
Cybersecurity Attacks and Extreme Events</title>
      <link>https://rip.trb.org/View/2548628</link>
      <description><![CDATA[This project is focused on measuring the resilience of transportation systems with respect to cyberattacks and extreme events
(hurricanes and power outages). This will require developing an Advanced Traffic Management System (ATMS) simulator for a given road network system to simulate
potential cyberattacks and their impact on the traffic. The research team will propose combinatorial optimization algorithms for optimally attacking
the ATMS and measure the impact of such attacks to assess the resilience of the system. The team will also evaluate the impact of
concurrent extreme events on the transportation system, especially hurricanes and power outages. These extreme events are
expected to become more likely in the upcoming years due to climate change and are particularly relevant to the city of Houston, Texas, where the PI’s institution is located. The proposed approaches will be evaluated on publicly available datasets in collaboration
with other members of the center. Main findings will be summarized in at least one research paper and the final project report.
Software, datasets, and metadata produced through the project will be made publicly available.]]></description>
      <pubDate>Tue, 29 Apr 2025 16:56:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2548628</guid>
    </item>
    <item>
      <title>Innovative Traffic Management Methods</title>
      <link>https://rip.trb.org/View/2431575</link>
      <description><![CDATA[Traffic management operational strategies and methods are fundamental elements agencies use to manage traffic and operate roadways and highways safely and efficiently. The Federal Highway Administration (FHWA), working in partnership with state partners, has led research for several decades to develop or use new or improved traffic management operational strategies, methods, technologies, and tools. Opportunities exist to research and develop new or enhanced traffic management operational strategies and methods to consider, collect, compile, and use new data sources, computing capabilities (e.g., artificial intelligence), technologies, communications methods, and decision support tools (e.g., automated use of operational strategies). The initial objectives of the Innovative Traffic Management Methods Pooled Fund Study (PFS) are to assemble and partner with state agencies, the Federal Highway Administration, and other countries (e.g., Europe) to: (1) identify research needs and develop a plan or roadmap of future research; (2) coordinate and collaborate with agencies conducting or considering related research; (3) develop, consider, select, initiate, and manage selected PFS projects; and (4) inform industry and potential partners on perspective research plan or roadmap, status of selected projects and PFS activities, and share research results.]]></description>
      <pubDate>Tue, 17 Sep 2024 16:15:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2431575</guid>
    </item>
    <item>
      <title>Semi-Automation of Interstate-90 Variable Speed Limit (I-90 VSL) Corridor in Ohio's Advanced Traffic Management System (ATMS)
</title>
      <link>https://rip.trb.org/View/2431342</link>
      <description><![CDATA[The current process to adjust speeds on the corridor includes utilizing various data/tools such as speed sensors, CCTV cameras, road weather information systems (RWIS), and communications from local authorities.  A Statewide Traffic Management Center (TMC) Specialist has to manually monitor the information from the sources then make a decision as to whether speeds should be adjusted in the corridor.  Ohio Department of Transportation (ODOT) would like to remove the subjectivity of this process by creating a set of thresholds/criteria to change speeds based on data-driven research.

The goal of this research is to provide data-based decision support in a semi-automated fashion to improve the efficiency and effectiveness of managing the I-90 Variable Speed Limit (VSL) Corridor.                ]]></description>
      <pubDate>Tue, 17 Sep 2024 14:37:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2431342</guid>
    </item>
    <item>
      <title>Implementing the ATSPM-In-The-Loop Simulation Solution with the TxDOT State-Wide ATSPM System Deployment Plan</title>
      <link>https://rip.trb.org/View/2420080</link>
      <description><![CDATA[This project will introduce automated traffic signal performance measures or ATSPM systems to all stages of traffic signal projects in Texas. The benefits of ATSPM systems have been broadly recognized by agencies. Texas Department of Transportation (TxDOT) is also in the process of state-wide ATSPM deployment. Nonetheless, the ATSPM system is poised to evaluate the traffic signal data generated by controllers. Therefore, access to the real ATSPM systems is limited to a small portion of traffic signal stakeholders, and these stakeholders may not take advantage of ATSPM during traffic signal planning and design due to a lack of data. In a research project sponsored by TxDOT, the research team demonstrated the use of a microscopic traffic simulation engine to generate the needed traffic signal data for real-world ATSPM systems to generate performance measures. With the developed insights and software tools from that project, the research team will assist and facilitate TxDOT to implement the delivered ATSPM-in-the-loop simulation engine toward a regular task for ATSPM-enhanced traffic signal planning and design. This project will also expand the developed ATSPM-in-the-loop simulation platform to meet all the practical needs for TxDOT's ATSPM deployment effort. This project will increase the TRL from 7 to 9 by assisting TxDOT to develop a practical solution to increase stakeholders' access to and acceptance of the ATSPM concept in various types of traffic signal projects.]]></description>
      <pubDate>Thu, 22 Aug 2024 17:19:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2420080</guid>
    </item>
    <item>
      <title>Data Subsystems and Data Management Plans for Traffic Management Systems</title>
      <link>https://rip.trb.org/View/2286626</link>
      <description><![CDATA[Traffic management systems (TMSs) are deployed in the United States to improve the efficiency, safety, and reliability of travel on designated portions of the surface transportation network. TMSs are typically large, complex systems, that consist of a number of subsystems (e.g., ramp metering, traffic signal control, dynamic message sign, data, traveler information, communication, software, hardware), as well as a range of components (e.g., dynamic message signs, detection devices/sensors, closed-circuit television cameras, signal heads, controllers, communication switches, servers, video wall, phones).

TMSs capabilities could support different services, functions, tasks, or actions. For example, some TMSs manage only the vehicular traffic on freeways in each region, while others may manage the entire road network, which may include surface streets and freeways. TMSs may also have different roles and responsibilities (e.g., sharing roadway and traveler information) that involve sharing, coordinating, or making information available to other agencies, systems, or service providers (e.g., emergency services, transit).

TMSs range in size (i.e., coverage area), functionality (e.g., incident management, ramp management), services (e.g., traveler information, managing traffic across institutional boundaries), and capabilities (e.g., whether or not the system includes a traffic management center, which can be used for sharing information).

Significant changes have occurred with cloud options available to agencies to store data. TMSs have traditionally been designed with local servers and limited ability to modify or make changes. Technical options are available to support agencies making changes in the design, configuration, and technologies used to support a data subsystem (see Special Note A). 

Limited technical information and resources exist to help agencies assess the capabilities and evolving needs for TMSs data subsystems. There are limited resources to support agencies integrating the needs and requirements of data subsystems into the decisions made in planning and programming processes throughout the life cycle of a TMS (e.g., how to plan, design, or procure needed data storage and management capabilities). Agencies face challenges with systematically managing data as part of their TMSs operation. There are limited resources for agencies to use or to assist with data management (e.g., archiving, use, configuration, monitoring use), and issues with receiving, sharing or using data with third-party sources or within an agency (e.g., licenses, proprietary, sensitive information). Research is needed to help agencies better manage data in TMSs.  

The objective of this research is to develop two technical reports to support agencies’ decision-making processes and frame the opportunities for agencies to consider when contemplating improvements to data subsystems and data management plans of their TMSs: (1) Report No.1, Data Subsystems for TMSs, and (2) Report No.2, DMP for TMSs.]]></description>
      <pubDate>Tue, 07 Nov 2023 12:01:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2286626</guid>
    </item>
    <item>
      <title>Improving Traffic Signal System Planning, Design and Management with Big-data-enhanced Automated Traffic Signal Performance Metrics (ATSPM) System</title>
      <link>https://rip.trb.org/View/2256333</link>
      <description><![CDATA[This project provides a guideline accompanied with the necessary software tools for the 
Texas Department of Transportation (TxDOT) and local agencies to better use the Automated Traffic Signal Performance Metrics System (ATSPM) in arterial traffic management. The ATSPM system came to traffic signal operations years ago and it can help agencies better understand arterial traffic signal performance. Many agencies are considering adopting the ATSPM systems because the ATSPM system(s) focuses on monitoring the traffic signal performance in the field. However, most traffic signal planning and design activities at present still rely on the traditional methods, such as Synchro, Highway Capacity Manual, etc. If agencies adopt different criteria between the planning/design stage and implementation stage, confusion will form and grow with the increase of ATSPM adoptions. To fill this gap, the research team plans to take a systematic approach to introduce the ATSPM concepts into all stages of traffic signal management. The research team will develop a series of software tools to establish a new "ATSPM-In-The-Loop" traffic signal simulation framework, accompanied by case studies that public agents or consultants can use to evaluate their future traffic signal timing plans in simulation before deployment. The outcomes of this project will nationally be the first of its kind.]]></description>
      <pubDate>Wed, 27 Sep 2023 17:15:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2256333</guid>
    </item>
    <item>
      <title>Design and Demonstration of an Arterial-Friendly Local Ramp Metering Control System</title>
      <link>https://rip.trb.org/View/2071697</link>
      <description><![CDATA[Highways and arterials are highly inter-dependent, but may have their own operational strategies and systems that do not necessarily synchronize. As a result, traffic queues can spillover from highway to arterials, or the other way around, leading to substantial congestion that worsens the system performance. Coordinating the signal control system on arterials and ramp metering control on ramps are key to mitigating such congestion. Most signal or ramp metering systems can alleviate queues locally to some extent under non-recurrent congestion (being responsive or reactive), but are not designed to prevent queuing from the occurrence of incidents (being predictive) nor mitigate congestion for the joint network. Managing traffic predictively (or proactively) and coordinating ramp metering and street signals among all relevant highway on-ramps/off-ramps can effectively improve the joint network performance. This research project addresses two problems for an integrated Transportation Systems Management and Operations (TSMO) system: ahead-of-curve prediction and system-level signal and ramp metering coordination, and quantify the network benefits of operational strategies to improve mobility/safety.]]></description>
      <pubDate>Thu, 01 Dec 2022 10:56:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2071697</guid>
    </item>
    <item>
      <title>Next Generation Traffic Management Systems</title>
      <link>https://rip.trb.org/View/2062800</link>
      <description><![CDATA[Work will continue on a “toolbox” of materials for use by Infrastructure Owners and Operators (IOOs) to plan for the modernization of their Traffic Management System (TMS)  including insights into decision support subsystems, developing concepts of operation, and preparing for transitions from legacy systems.  This work will be advanced in partnership with the TMC Transportation Pooled Fund Study.]]></description>
      <pubDate>Thu, 17 Nov 2022 11:56:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2062800</guid>
    </item>
    <item>
      <title>Research on Artificial-Intelligence for Data Integration with State Highway</title>
      <link>https://rip.trb.org/View/1982055</link>
      <description><![CDATA[Active traffic management implies the dynamic management of recurrent and non-recurrent congestion based on current and predicted traffic conditions. The goal of this project is to improve mobility and safety using data analytics and artificial intelligence applied in active traffic management for arterial freeway interactions. The project team will develop an integrated system that uses data collected from videos and loop detectors and other sensors for this purpose. Using machine learning and artificial intelligence, the team will develop techniques that will support real-time traffic management]]></description>
      <pubDate>Wed, 15 Jun 2022 15:05:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/1982055</guid>
    </item>
    <item>
      <title>Preparing for Virtual Operation of Traffic Management Systems</title>
      <link>https://rip.trb.org/View/1957082</link>
      <description><![CDATA[The ability to virtually manage and operate traffic management systems (TMSs) is no longer a luxury; it needs to be a capability of the system and core capacity of an agency’s operations program. It has become a necessity for agencies to work toward developing and sustaining these capabilities and having the resources necessary to remotely manage and operate their own or another agency’s TMS. Agencies continue to explore what organizational policies, procedures, capacity, resources, and capabilities may be needed to virtually manage and operate their TMSs to support day-to-day traffic management, planned (e.g., concerts, festivals) and unplanned (e.g., during COVID-19 pandemic, weather emergency) special events.

Agencies are looking for resources to explore what planning, development, and training may be needed to successfully position or prepare TMSs with the capabilities and resources needed to allow agencies to transition the operation of a TMS involving highly technical traffic management centers to a virtual operating environment with minimal service disruptions. 

There is a need to develop technical resources to assist agencies in planning, developing, or improving their TMSs to enable virtual operation, and to assist agencies in preparing for, training, testing, and transitioning to remote or virtual TMS operation. 

The objectives of this research are to develop two technical reports: Report No. 1, Assessing and Improving TMSs to Enable Virtual Operation, to assist agencies with planning, developing, or improving their TMSs to enable virtual operation, and Report No. 2, Operation and Implementation of TMSs Virtually, to assist agencies to prepare, plan, design, build, and operate their TMS virtually.]]></description>
      <pubDate>Fri, 27 May 2022 11:35:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/1957082</guid>
    </item>
    <item>
      <title>Transportation Management Centers Pooled Fund Study Phase II</title>
      <link>https://rip.trb.org/View/1891336</link>
      <description><![CDATA[Transportation management centers (TMCs) are critical resources that offer agencies the potential to improve the safety and mobility of travel on the surface transportation system. TMCs have also been deployed or are being enhanced to assist agencies in fulfilling the ever-increasing transportation needs of travelers (e.g., travel times), service providers (e.g., transit, emergency services), other agencies, and the public (e.g., incidents). TMCs are comprised of a complex, integrated blend of hardware, software, operational strategies, processes, and people performing a range of functions and actions. TMCs typically consist of multiple subsystems, which may include functionality such as ramp metering, traffic signal control, dynamic message signs, data, software, and communication. The deployment, integration, operation, management, and maintenance required to ensure the operation of traffic management systems (TMSs), TMCs, and subsystems is very complex as the supporting technologies continue to evolve. 


OBJECTIVES: The objectives of the Traffic Management Centers (TMC) Pooled Fund Study (PFS) is to assemble regional, state, and local transportation management agencies and FHWA to: (1) identify key issues and challenges agencies are facing with their traffic management systems (TMSs) or centers (TMCs); (2) suggest approaches to addressing identified issues; (3) initiate and monitor projects intended to address identified issues; (4) develop technical resources and disseminate results; (5) provide leadership and coordinate with others on TMC interests; and (6) promote and facilitate sharing information on TMC issues nationally.]]></description>
      <pubDate>Thu, 11 Nov 2021 08:15:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/1891336</guid>
    </item>
    <item>
      <title>Planning and Evaluating Active Traffic Management Strategies</title>
      <link>https://rip.trb.org/View/1870388</link>
      <description><![CDATA[Recent initiatives in the United States and Europe have pointed to the largely untapped potential of Active Traffic Management (ATM), which is “the ability to dynamically manage recurrent and non-recurrent congestion based on prevailing and predicted traffic conditions” (FHWA Active Traffic Management and Demand Website). Some examples of ATM strategies include dynamic lane use control, adaptive traffic signal control, dynamic speed limits, queue warning, and adaptive ramp metering. One common theme from recent workshops conducted by the FHWA on ATM has been that public agencies are highly interested in ATM strategies; however, a major barrier to deployment is uncertainty about the operational, reliability, and safety impacts and resulting benefits. Key questions that agencies need to answer before funding ATM systems include: (1) What are the impacts seen by agencies that have deployed ATM systems? (2) How do these impacts translate into benefits both to the traveling public and to the deploying agency(ies)? (3) How can we estimate the impacts of alternative ATM systems in our state? (4) What benefits and impacts can be reasonably expected on specific roadways in our state if deployed? Another area of interest is the life-cycle costs and resources required to operate and maintain ATM systems. In this time of budget and staffing uncertainty, agencies need to consider issues such as: (1) operations and maintenance resource and management demands and challenges associated with ATM systems; (2) medium- to long-term sensitivity of ATM effectiveness and benefits to operations and maintenance resource levels and vigilance; (3) integration of life-cycle costs into sustainable financial programming of ATM systems; and (4) acknowledgement of human resource demands (agency or contractor staff) for operations and maintenance functions required for ATM effectiveness. Since ATM strategies are new to many agencies and they differ in some significant ways from traditional capital projects, they can present some difficulties during the planning, programming, budgeting, and staffing phases. A guide is needed to help transportation agencies decide whether and which ATM strategies can help them achieve their objectives.

The objective of this research is to complete the work begun in NCHRP Project 03-114 to develop a guide to planning and evaluating active traffic management for recurrent and nonrecurrent conditions.]]></description>
      <pubDate>Mon, 02 Aug 2021 22:11:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/1870388</guid>
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