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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>
    <image>
      <title>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Game-Theoretical Approach for Cyberattack Modeling and Deep Learning-Based Resilience of Connected Automated Vehicles</title>
      <link>https://rip.trb.org/View/2696944</link>
      <description><![CDATA[The current state of practice in security research on connected automated vehicles (CAVs) does not consider how adversaries may evolve over time and adapt to defense strategies. This proposed research takes the state of practice well beyond the current focus on anomaly detection towards strategic response in defense strategies by developing attack defender models with game-theoretical models. Game theory provides a framework to study the strategic interactions between defenders and adversaries with conflicting objectives. Given the above background, this study will design strategic games to study attacker and defender strategies for cyber deception, as well as algorithms to compute equilibrium or optimal defense strategies in a CAV environment. Real-world data from CAV experiments conducted by PIs will be used to design game theory models in a CAV environment. The study will design two strategic games, namely a zero-sum game and a Stackelberg security game, to formalize the interactions between attackers and defenders by devising a strategic comparison between a zero-sum game and a Stackelberg security game. The proposed game models define payoff functions that capture the trade-offs between model accuracy and the success rates of attacker and defender. The dynamic attacker-defender strategies mimic real-world applications and provide the ability to provide alerts to traffic management center operators for performing cyber incident response in a timely manner, which has attracted the Virginia Department of Transportation’s (VDOT’s) interest. VDOT will serve as a partner to help with real-world implementation. ]]></description>
      <pubDate>Wed, 29 Apr 2026 11:17:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696944</guid>
    </item>
    <item>
      <title>Quantum Computing and Quantum-Inspired Algorithms for Transportation Network Design</title>
      <link>https://rip.trb.org/View/2625857</link>
      <description><![CDATA[Quantum computing has the potential to transform the classical computing paradigm with improved efficiency for solving NP-hard combinatorial optimization problems modeled as a quadratic unconstrained binary optimization (QUBO) model, including those in transportation and supply chains. Although the quantum annealing (QA) algorithm is theoretically attractive, there is a lack of computational experience showing its superior performance over the traditional algorithms. The purpose of this project is to explore quantum computing and quantum-inspired algorithms on a class of transportation network design problems. We will develop and implement custom-designed algorithms to solve large-scale QUBO models for transportation network design, and evaluate their performance compared to that of QA. The model and algorithms are expected to provide optimal large-scale network design solutions efficiently. This project aligns with the DOT’s strategic goal of economic strength and global competitiveness, and supports MATC-TSE’s theme on transportation systems of the future.
]]></description>
      <pubDate>Tue, 18 Nov 2025 13:58:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625857</guid>
    </item>
    <item>
      <title>Integration of a Real-time Traffic State Estimation and a Decentralized Game-Theoretic Traffic Signal Controller</title>
      <link>https://rip.trb.org/View/2447006</link>
      <description><![CDATA[This project proposes to enhance a decentralized traffic signal controller based on a Nash Bargaining game-theoretic framework, integrating a Kalman filtering (KF) algorithm for real-time traffic state estimation. By combining KF with the traffic signal controller, the project aims to optimize signal phasing sequences at intersections based on turning movements and traffic density, thereby reducing queue lengths and delays. The approach involves traffic data collection through loop detectors and probe vehicle data, and it will address saturation flow rates for shared lanes. This integration intends to achieve efficient system performance across varying probe vehicle market penetration levels, ultimately providing a robust solution for improved traffic flow and reduced environmental impact at intersections.]]></description>
      <pubDate>Wed, 30 Oct 2024 14:41:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2447006</guid>
    </item>
    <item>
      <title>Competitiveness, User Preference, and Willingness-to-Pay for Peer-to-Peer Ridesharing Service</title>
      <link>https://rip.trb.org/View/2343858</link>
      <description><![CDATA[The Peer-to-Peer (P2P) ridesharing model is a cost effective system where transit service is not available. This research explores the competitiveness, user preference, and willingness-to-pay (WTP) for P2P ridesharing services as a sustainable mode of transportation. The study aims to understand the factors influencing users' choices and WTP for P2P ridesharing platforms. The research methodology includes a suggested stable price structure for P2P ridesharing for drivers and users using a game theory and a comprehensive analysis of the competitiveness of P2P ridesharing compared to traditional transportation modes and other alternatives. Moreover, a survey will be conducted to identify user preferences and the key attributes influencing their decision to opt for P2P ridesharing. To estimate users' WTP, the adaptive choice-based conjoint (ACBC) analysis will be employed using Sawtooth Software's SSI Web. The findings of this study will contribute to a deeper understanding of the viability and user acceptance of P2 ridesharing, enabling policymakers and ridesharing platforms to optimize their offerings and pricing strategies for improving P2P ridesharing system.]]></description>
      <pubDate>Thu, 22 Feb 2024 16:11:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2343858</guid>
    </item>
    <item>
      <title>A New Optimization Approach to Distributed Manufacturing System Design</title>
      <link>https://rip.trb.org/View/2342037</link>
      <description><![CDATA[Distributed manufacturing is gaining traction in various industries with fast growth of sensor, Internet of Things (IoT) and advanced manufacturing technologies. It is a promising new business paradigm to achieve mass customization and facilitate the shared and circular economy. The goal of this proposed project is to develop a new optimization approach for the strategic design of distributed manufacturing system (DMS) in terms of facility locations and dynamic sharing of manufacturing resources to meet time-varying demand, while considering autonomous/distributed planning decisions of supply-production networks (SPNs) in the DMS. The main challenge and technical advancement of the project is the design and implementation of a decision-game-theoretic model to capture the autonomous decision-making feature of each local SPN and to satisfy time-varying customer demand in a dynamic way. Advanced computational algorithms will also be developed to obtain quality solutions efficiently. This project aligns with DOT’s strategic goals of economic strength and global competitiveness, safety and equity, and supports MATS-TSE’s themes on resilient supply chains and transportation systems of the future.]]></description>
      <pubDate>Mon, 19 Feb 2024 18:24:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2342037</guid>
    </item>
    <item>
      <title>Reinforcement Learning-Assisted Virtualized Security Framework for CAVs</title>
      <link>https://rip.trb.org/View/2335052</link>
      <description><![CDATA[Connected and autonomous vehicle (CAV) technology has brought a major transformation in the transportation sector by significantly improving the mobility of people and goods through advanced communication, sensing, and computing capabilities. However, CAVs can be hacked due to vulnerabilities in the in-vehicle software, resulting in physical damage and jeopardizing the safety of drivers and passengers. By exploiting the vulnerabilities, hackers can perform malicious actions ranging from draining batteries and taking control of the steering wheel to disabling the alarm system. The existing security solutions implemented in CAVs are static and cannot withstand evolving security threats such as Advanced persistent threats (APT) and ransomware attacks. Moreover, costly update procedures leave the CAV software unpatched for a long time, making the CAVs vulnerable to new exploits.
This project aims to develop a virtualized security framework to improve the resiliency of CAV software. The framework will allow the execution of different code variants of CAV software to introduce uncertainty in the attack surface. The proposed framework will integrate the Network Functions Virtualization paradigm to implement the code variants of CAV software as virtual network functions. The proposed framework will offer the ability to optimally deploy the appropriate virtual network functions using a reinforcement learning agent. The reinforcement learning agent perceives the threat environment of CAVs and provides the optimal code variant that maximizes the resiliency of CAV software while ensuring their Quality of Service (QoS) requirements. This project aims to accomplish the following goals: (1) develop a virtualized security framework that allows fast and dynamic provisioning of different code variants of CAV software, (2) design novel and efficient algorithms designed based on game theory and Artificial Intelligence (AI) techniques including Deep Learning and Generative Adversarial Networks (GANs) to determine the optimal code variant, (3) evaluate the performance of reinforcement learning algorithm using simulations, and (4) build a proof-of-concept of the proposed security framework and evaluate its performance using real-world experiments.
]]></description>
      <pubDate>Fri, 09 Feb 2024 19:36:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2335052</guid>
    </item>
    <item>
      <title>R-PNT Virtual Conflict Simulation</title>
      <link>https://rip.trb.org/View/2329759</link>
      <description><![CDATA[This project will entail developing a virtual testbed for modeling various cyber and cyber-physical attacks and designing defense mechanisms to mitigate the effects of these attacks. As part of this effort, simulations will be conducted to evaluate the network-wide effect of such attacks and to evaluate the adequacy of various defense mechanisms in resolving and recovering from these attacks.

While this five-year project will address numerous cyber and cyber-physical attacks, including GNSS jamming and spoofing, the focus of the effort in the first year will be on spoofing of routing attacks, given that these have occurred with Google and Waze. Specifically, in the case of Google, an artist in Berlin tricked Google Maps into creating traffic jam alerts by pulling 99 phones – with their location services on – slowly around the German capital in a handcart1. As part of this effort, the INTEGRATION agent-based traffic simulation model will be modified to allow for the sharing of erroneous real-time travel time information that will impact the dynamic feedback traffic router (similar to the Google maps router). This will be tested on at least one network for different attack locations and intensities to quantify network-wide impacts of such attacks. In addition, various filtering techniques will be devised to try to identify anomalies in the data and rectify data as a means of defense against such attacks.]]></description>
      <pubDate>Wed, 31 Jan 2024 15:58:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2329759</guid>
    </item>
    <item>
      <title>Multiple-vehicle Trajectory Planning Framework Considering Vulnerable Road Users</title>
      <link>https://rip.trb.org/View/2229371</link>
      <description><![CDATA[This study aims to address the challenge of real-time trajectory planning for connected and automated vehicles (CAVs) while considering vulnerable road users (VRUs) in the environment. The problem lies in efficiently planning trajectories for CAVs in the presence of unpredictable VRUs, ensuring safety and avoiding crashes. The solution involves modeling the decision-making processes of CAVs and VRUs through game theory, incorporating the uncertainty of VRUs' motion using confidence intervals, and designing efficient heuristic algorithms for real-time problem solving. The project's expected outcomes include a technical paper describing the developed trajectory planning framework, along with simulation videos showcasing its effectiveness. This proposal seeks to fill a research gap by exploring novel solutions for this complex problem and contribute to the field of CAV operations. The proposed framework's concepts and validation methods are intended for educational purposes and potential practical implementation in future CAV operations.]]></description>
      <pubDate>Thu, 17 Aug 2023 08:40:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2229371</guid>
    </item>
    <item>
      <title>Designing an Electric Transit Bus Network</title>
      <link>https://rip.trb.org/View/1986459</link>
      <description><![CDATA[This study examines different approaches for designing an optimal network for electric transit buses. Mathematical models will be developed that will design an optimal network while minimizing both operator and passenger costs and maximizing ridership. The research will also consider the costs of infrastructure required for electric buses and will design an optimal network for the placement of chargers and electric bus infrastructure.]]></description>
      <pubDate>Tue, 28 Jun 2022 08:36:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/1986459</guid>
    </item>
    <item>
      <title>Alleviating Traffic Congestion: Developing and Evaluating Novel Multi-Agent Reinforcement Learning Traffic Light Coordination Techniques</title>
      <link>https://rip.trb.org/View/1981125</link>
      <description><![CDATA[Traffic congestion costs American cities tens of billions of dollars per year, not to mention its negative impact on the environment or people’s mental health. Novel Markov game models and advanced reinforcement learning algorithms hold the promise of drastically alleviating congestion through dynamic coordination of traffic signals and adaptive techniques to dynamically re-route traffic. This project involves a collaboration with Econolite, a leading provider of traffic management systems.]]></description>
      <pubDate>Fri, 10 Jun 2022 14:34:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/1981125</guid>
    </item>
    <item>
      <title>Develop and Test Drug Positive Driver Detection Cues</title>
      <link>https://rip.trb.org/View/1889973</link>
      <description><![CDATA[For decades, the National Highway Traffic Safety Administration (NHTSA) has worked with law enforcement to develop strategies to detect impaired drivers on the road. Much of this work has focused on drivers who are impaired by alcohol, but NHTSA is also conducting extensive research on drivers impaired by drugs other than alcohol. This study focuses on examining the feasibility of providing law enforcement officers with specific cues for detecting drivers positive for a potentially-impairing drug, other than alcohol, with a focus on driving under the influence of marijuana. To develop and test a set of cues to detect individuals driving under the influence of marijuana, this project will review previous drugs and driving performance research, the NHTSA Drug Recognition Expert database, and conduct ride-along with law enforcement officers to develop potential cues. The cues will be driving behavior-based but may also include cues related to after an officer has stopped a driver. The cues will be based on decision theory and the probability of cues being related to driving and behavior of marijuana-positive
drivers. Once NHTSA approves a set of cues for field testing, the project will work with law enforcement agencies to use these cues while they are on patrol. Researchers, who are riding along with officers, will approach drivers after an
enforcement stop and invite them to participate in the study, and to provide a biological sample via an on-site oral fluid test device to determine present use of marijuana. The study determine the probability of separate cues and sets of cues to accurately predict marijuana use of drivers. The project will document the study in a final report and develop basic training materials for use by law enforcement.]]></description>
      <pubDate>Wed, 03 Nov 2021 14:31:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/1889973</guid>
    </item>
    <item>
      <title>Campus as a Living Lab: Discovering the Comfort of Wheelchair Users in the Pedestrian Network by Experiential Learning with High School Students</title>
      <link>https://rip.trb.org/View/1874587</link>
      <description><![CDATA[Although the adoption of the Americans with Disabilities Act (ADA) shifted our paradigm for accessibility, the built environment is still not friendly for wheelchair users. The main challenge is that enforcement of ADA guidelines primarily focuses on the design requirements, while actual usability is less emphasized in current practice and evaluation is limited largely to walk-through investigations. In this research project, we will draw on Disability Studies and disability-led design to develop and implement an experiential learning-based curriculum to teach students  about disability-related transportation inequities. The curriculum aims to introduce the mobility barriers encountered by wheelchair users—one of the largest minority user groups of transportation infrastructure. The curriculum also covers the educational contents of open source-based data acquisitions (e.g., Raspberry Pi,  sensors) as well as data analytics (e.g., descriptive statistics, data visualizations) with multiple hands-on examples. After completing each module, the research team  will use the University of Texas at Arlington campus as a living lab for high school students via which they will demonstrate their data acquisition tools and present the results. This series of educational activities will provide experiential learning opportunities for upper-level high school students interested in careers in urban planning and engineering and introduce them to basic concepts in Disability Studies and disability-led design. Ultimately, the curriculum will motivate high school students to develop citizen science-based  solutions and to be aware of disability-related barriers when they encounter other transportation inequities in the future.]]></description>
      <pubDate>Wed, 25 Aug 2021 17:12:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/1874587</guid>
    </item>
    <item>
      <title>Y3R7 - Identifying Critical and Vulnerable Freight Routes in Roadway Networks: A Game Theory Framework and Application in the State of Florida</title>
      <link>https://rip.trb.org/View/1868907</link>
      <description><![CDATA[Transportation networks are by nature vulnerable to natural and man-made disasters (or incidents).
Vulnerabilities of transportation networks have been widely studied in recent years and are gaining even
more attention with the growing number of threats (e.g., climate change, man-made attacks). In the US the
transportation network is one of the largest and oldest in the world making also one of the most vulnerable.
As traffic demand increases (despite the decrease in vehicle miles traveled) decision-makers are faced with
the important task of identifying the vulnerable and critical links and routes in the transportation network
and make decisions on investment that will protect and fortify the network against attacks. Addressing
network vulnerabilities of transportation assets, in general, will minimize impacts of disruption, reduce
recovery time and improve on the region’s resilience. In this project, the project team will improve and implement on a
testbed in Florida mathematical models and tools developed by Golias et al. (2018) to identify critical and
vulnerable links and/or paths with a focus on freight movements]]></description>
      <pubDate>Tue, 27 Jul 2021 15:56:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/1868907</guid>
    </item>
    <item>
      <title>Commercial Package Delivery through Public Transportation Systems in Rural States</title>
      <link>https://rip.trb.org/View/1714623</link>
      <description><![CDATA[The purpose of this project is to provide the Small Urban, Rural and Tribal Center on Mobility with additional information and greater understanding of the feasibility of last mile package delivery for commercial entities via public transportation in rural areas.
This project will investigate innovative “last mile” package delivery systems and how rural public transportation systems may have a role in the process.  It will include a synthesis of current last mile package delivery practices in public transportation systems in rural states; an analysis of state policies regarding the use of public transportation for package delivery; and an estimate of demand, capacity need, and revenue generation for rural transit systems in regard to last mile package delivery.  This feasibility study will also include recommendations for policy and planning.]]></description>
      <pubDate>Wed, 10 Feb 2021 14:00:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/1714623</guid>
    </item>
    <item>
      <title>Impact of Utility Delays on Project Delivery</title>
      <link>https://rip.trb.org/View/1742784</link>
      <description><![CDATA[This research project will evaluate the impacts of utility delays on construction project delivery for transportation projects and recommends best practices for minimizing those impacts and improving project delivery. Specifically, this project will: (1) Explore the inclusion of utility relocation in the prime contract with the goals of saving time and money, and expediting state, county, and municipal infrastructure improvement projects. One of the efforts to expedite project delivery is the new law enacted on May 13, 2019, S. C. Code Ann. § 57-5-880. This new law enables SCDOT to bear 100% of the relocation costs (less the value of betterment) of small public water and sewer utilities within right of way and up to 4% of the original construction bid amount of the transportation project in the case of large public water and sewer utilities if the utilities go in-contract and place the relocation work under the control of the general contract for the transportation project. If a transportation project involves relocating both small and large public (water or sewer) utilities, this new law allows SCDOT to bear 100% of the relocation costs for small public utilities and up to 4.5% of the original transportation project bid cost minus the relocation costs of small public utilities in the case of large public utilities. This law is effective until July 1st, 2026 and any evidence to show this benefits SCDOT in expediting project delivery will be helpful in having this law extended beyond its current expiry data; (2) Provide recommended solutions for overcoming most common utility related delays in order to improve project delivery efficiency; (3) Review Memorandums of Agreement (MOA) between utility owners and SCDOT with the goal of offering suggestions based on research findings and best practices from other state DOTS; and (4) Develop best practices on identifying utilities early in the transportation project and minimizing delays through effective management of utility relocations.
]]></description>
      <pubDate>Mon, 05 Oct 2020 09:18:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/1742784</guid>
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