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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
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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>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Measuring the Economic, Mobility, and Health Benefits of Multimodal Projects</title>
      <link>https://rip.trb.org/View/2784594</link>
      <description><![CDATA[The previous study, RP 2024-014: Measuring the Economic, Mobility, and Health Benefits of Multimodal Projects, established a strong foundation for evaluating multimodal transportation projects in North Carolina. This research produced a Multimodal Benefits Analysis Tool, designed to evaluate the economic, physical health, and mobility benefits that accrue when co-locating a bicycle and pedestrian facility with existing transit or ferry networks. By using a data-driven geospatial approach, the study demonstrated how multimodal connections can improve access to employment and medical points of interest and improve overall mobility.
The next step is to obtain a deeper understanding of the planning needs of the North Carolina Department of Transportation's (NCDOT’s) Integrated Mobility Division (IMD) and provide guidance on how the tool can be used to help advance these needs. Specifically, the proposed work will focus on translating the technical research into a user-friendly guidebook, a training presentation, and a webinar. These products will provide step-by-step guidance on using the Multimodal Benefits Analysis Tool, highlight relevant case applications, and ensure that IMD staff can apply the tool consistently to align with departmental planning objectives.]]></description>
      <pubDate>Tue, 29 Sep 2026 15:46:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2784594</guid>
    </item>
    <item>
      <title>Physics-Informed AI-Enhanced Multimodal Modeling and Governance: Improving Safety and Resilience for Data-Limited Transit Corridors</title>
      <link>https://rip.trb.org/View/2739297</link>
      <description><![CDATA[Limited sensor coverage and fragmented, mode-specific modeling infrastructure hinder the holistic monitoring of modern transportation networks. The resulting data blind spots prevent current models from capturing dynamic, cross-modal dependencies, where a disruption in one mode, such as a metro closure, triggers cascading surges in others, forcing planners and Traffic Management Centers to rely on reactive, siloed strategies.

To improve the state of the art, this project proposes a Virtual Sensor paradigm driven by physics-informed generative artificial intelligence (AI). By integrating fundamental transportation physics with generative deep learning, the framework synthesizes high-fidelity data for sensor-sparse regions by inferring correlations from existing sensing infrastructure, creating cost-effective virtual data streams that simulate physical sensors and provide more complete multimodal network data for real-time operations and long-term planning. The project also evaluates the policy and governance dimensions of integrating emerging AI use cases, such as AI-generated data, into the Delaware Department of Transportation (DelDOT)’s planning, design, and operations.]]></description>
      <pubDate>Thu, 30 Jul 2026 16:41:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2739297</guid>
    </item>
    <item>
      <title>Statewide Multimodal Destination Access Methods and Demographic Analysis</title>
      <link>https://rip.trb.org/View/2725639</link>
      <description><![CDATA[The Oregon Department of Transportation (ODOT) does not currently have a consistent, statewide method to evaluate destination access—whether people can reliably and affordably reach essential destinations such as employment, education, health care, and key services. While agency performance measures and analyses focus primarily on infrastructure conditions and system mobility, they do not answer whether investments are improving people’s ability to access what they need for daily life. Without a standardized destination access methodology, ODOT lacks a clear, data-driven basis for monitoring progress, understanding structural access gaps, or using access outcomes to inform investment decisions. 
OBJECTIVES: (1) Establish a standardized, agency-wide methodology for multimodal destination access analysis. ODOT currently performs destination access analysis on an ad hoc basis and does not have a consistent, documented method for statewide or cross-program use. This project will develop and test a unified approach that can be used across business lines for performance reporting, planning, and investment decision-making. (2) Integrate user profile analysis to identify which populations face transportation access gaps—and to what extent. This research will move beyond single-variable demographic assumptions and instead use data-informed definitions of at-risk populations to better understand who experiences structural access barriers and why. (3) Develop a tool for viewing destination accessibility metrics. The tool can be used to view accessibility by mode and destination type by region. The combination of transportation and land use data will enable planners to understand existing accessibility conditions and needs in specific areas.  This tool would be usable for the ODOT Capital Investment Plan (CIP), local transportation system plans, and other programs where access measures offer utility. 
This research fulfills a need for a destination access methodology that supports ODOT policy, planning, and prioritization. With the results of this research, ODOT will be able to answer critical questions about how the transportation system is serving residents. Access metrics can play a critical role in vehicle miles of travel (VMT) per capita and emissions reduction strategies by informing staff on which areas have feasible multimodal access. ]]></description>
      <pubDate>Wed, 08 Jul 2026 16:48:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2725639</guid>
    </item>
    <item>
      <title>From Region to Relationships: Toolkit for Developing Multimodal Corridor Systems</title>
      <link>https://rip.trb.org/View/2709251</link>
      <description><![CDATA[State departments of transportation (DOTs) and metropolitan planning organizations (MPOs) utilize extensive resources and time to develop robust long-range transportation plans and asset management plans that are very policy based. In addition, they develop their very location-specific programs and program projects. However, apart from an area that may require National Environmental Policy Act (NEPA) analysis, there is an overall lack of tools to tie the statewide/regional/local policy plans to specific projects in (state) transportation improvement programs.

Corridor plans at either a statewide or regional level can provide states and MPOs with a tool to bridge this gap (especially among local jurisdictions) and to allow for a corridor-specific vision and plan. This corridor plan and vision will take the statewide/regional vision and goals, tailor them to the corridor, and then provide guidance and a framework when state and local agencies develop projects along that corridor. The primary function of these corridors (activity, multimodal, main street, freeways, arterials, etc.), their connections between surrounding land uses, and safety, mobility, and accessibility needs should be factored in.

OBJECTIVE: The objective of this research is to identify strategies for state DOTs to encourage local agencies to proactively generate multimodal master plans based on the long-term vision for the corridor and its surrounding land uses.]]></description>
      <pubDate>Tue, 02 Jun 2026 11:29:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709251</guid>
    </item>
    <item>
      <title>A Performance- and Cost-Based Framework to Evaluate the Value of Multimodal Logistics Infrastructure</title>
      <link>https://rip.trb.org/View/2703796</link>
      <description><![CDATA[This project develops a practical, data-driven framework to evaluate the value of logistics infrastructure in a multimodal freight region. Focusing on the St. Louis metropolitan area, the framework integrates freight performance measurement with generalized logistics cost modeling to translate travel time, reliability, and terminal access improvements into economic outcomes. Methods include assembling a regional freight network representation, computing corridor-level travel time and variability metrics, and applying scenario-based valuation to estimate marginal benefits of targeted investments. The project also includes a private-sector truck–rail–barge use case to quantify multimodal tradeoffs and assess the competitiveness of inland waterway transportation under alternative infrastructure scenarios. The resulting workflow provides agencies and regional partners with transparent, repeatable methods to support freight investment prioritization and decision-making.]]></description>
      <pubDate>Sat, 16 May 2026 11:52:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703796</guid>
    </item>
    <item>
      <title>Evaluating Behavioral Responses to Mobility Credits and Ridehailing Integration in a Digital Mobility System</title>
      <link>https://rip.trb.org/View/2702725</link>
      <description><![CDATA[Digital mobility platforms are increasingly adopted by public agencies to coordinate multimodal travel, streamline fare payment, and improve efficiency. However, there is limited empirical evidence on how users respond to platform-based incentives and integrated services in real-world settings, as most studies rely on stated preference data or simulations. This project analyzes user behavior on Vamos-EZHub, a public digital mobility platform that integrates trip planning, fare payment, and access to services including local transit and ridehailing. It evaluates behavioral responses to two sequential interventions on Vamos-EZHub: (1) the introduction of prepaid mobility credits and (2) the integration of a transit-triggered ridehailing credit. 

Using longitudinal platform telemetry, ridehailing trip records, transit fare activation data, and General Transit Feed Specification (GTFS) data, the project examines how mobility and ridehailing credits affect platform engagement, transit and ridehailing use, first/last-mile connectivity, and spatial and temporal patterns of linked travel. Two-way fixed effects and event-study models are used to identify behavioral changes associated with each intervention. A geospatial-temporal algorithm classifies ridehailing trips connecting to transit, and stop- level regression models identify transit service and network characteristics associated with demand for linked trips. 

Expected outcomes include quantitative estimates of the influence of mobility credits and ridehailing integration on multimodal coordination, identification of service characteristics associated with higher demand for linked trips, and a reproducible analytical framework. The results will inform data-driven platform design, operational planning, and integration strategies for public agencies managing digital mobility platforms, while providing evidence to guide coordination with private ridehailing partners to improve system efficiency and reliability.]]></description>
      <pubDate>Thu, 14 May 2026 16:36:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/2702725</guid>
    </item>
    <item>
      <title>TraCR Foundational Project: TraCR Collective Transportation Cybersecurity Testbeds</title>
      <link>https://rip.trb.org/View/2697454</link>
      <description><![CDATA[The National Center for Transportation Cybersecurity and Resiliency's (TraCR's)
foundational project aims to develop technological tools, prototypes, testing platforms, and facilities to ensure the cybersecurity and cyber-resilience of multimodal transportation systems and related infrastructure. The project is led by Clemson University (Clemson) under the strategic direction of Dr. Ronnie Chowdhury (Lead PI), with coordination support from Dr. Sabbir Salek (Co-PI), and involves all eight other TraCR partner institutions organized into four subgroups. A structured project governance framework, including biweekly subgroup meetings, monthly full-team coordination meetings, quarterly progress reporting and advisory board engagement, ensures alignment with project milestones, integration across teams, and effective monitoring of technical progress and deliverables. 

Clemson collaborates with Benedict College (Benedict), South Carolina State University (SCSU), and the University of Texas at Dallas (UTD) to advance a comprehensive, automated threat modeling capability for multimodal transportation systems. Building on the Transportation Cybersecurity and Resiliency Threat Modeling Framework (TraCR-TMF), the team conducts testbed-in-the-loop evaluations within Clemson’s real-world cybersecurity testbed, implementing digital-twin-based cybersecurity analysis of in-vehicle networks, and engaging state transportation agencies to assess operational transferability. Additionally, the team will work to integrate graph-based reasoning models into threat modeling, deploy supervised ModernBERT classifiers, and align with the MITRE Embedded Systems Threat Matrix to strengthen structured system-to-vulnerability mapping and improve threat coverage across transportation cyber-physical systems.

The other partner institutions will develop additional real-world and virtual testing platforms to support cybersecurity experimentation for multimodal transportation. Florida International University (FIU) and the University of Alabama at Tuscaloosa (UA) are jointly advancing the Open-Source Connected and Automated Mobility Co-Simulation (OpenCAMS) environment and related simulation platforms, integrating SUMO, CARLA, and network simulation tools, to evaluate privacy-aware multimodal large language models and post-quantum-secure C-V2X communications. Their efforts further include the development and validation of spoofing attack models targeting Basic Safety Message transmissions and multi-frequency GPS receivers, as well as investigations into backdoor-resilient perception systems and the security of vision-language models for intelligent transportation applications.

Purdue University (Purdue) and the University of California, Santa Cruz (UCSC) are advancing adversarial testing methodologies through integrated physical-virtual experimentation frameworks that combine miniature autonomous vehicle testbeds, CARLA/METS-R simulation coupling, and scenario-based vulnerability discovery. These activities include simulation-to-real validation of perception and traffic signal spoofing attacks, evaluation of V2X safety message vulnerabilities, cybersecurity analysis of shared micromobility Bluetooth pairing protocols, implementation of lightweight post-quantum cryptographic protections for vulnerable road user beacons, and closed-loop security assessments of traffic signal controller infrastructures, along with investigations of secure multimodal AI agents and memory-augmented reasoning architectures for autonomous robotic transportation systems.

In addition, Morgan State University (MSU) is enhancing its connected vehicle cybersecurity experimentation capabilities by developing replay-attack models targeting C-V2X onboard units and evaluating mitigation strategies in its real-world testbed environment, in collaboration with Clemson. These efforts quantify communication-level impacts on safety-critical applications and support the development of deployable countermeasures to strengthen resilience against wireless attack vectors affecting connected transportation infrastructure.
]]></description>
      <pubDate>Thu, 30 Apr 2026 12:19:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2697454</guid>
    </item>
    <item>
      <title>Secure Multi-Modal Transportation Artificial Intelligence (AI) at Run-Time</title>
      <link>https://rip.trb.org/View/2689389</link>
      <description><![CDATA[This research develops robust and secure artificial intelligence (AI) systems for smart transportation that could defend against novel adversarial attacks with high performance. The project addresses critical vulnerabilities in Test-Time Adaptation (TTA) mechanisms and Multimodal Large Language Models deployed in transportation systems. Through new attack discovery, effective defense framework development, and deployable prototype systems, this work ensures that AI technologies can be safely deployed in safety-critical transportation applications. The research delivers practical solutions including natural scene adversarial attack frameworks, sharpness-aware minimization based TTA defenses, event-conditioned representation compression for efficient multimodal AI, and adversarially robust multimodal fusion architectures.]]></description>
      <pubDate>Tue, 28 Apr 2026 15:43:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2689389</guid>
    </item>
    <item>
      <title>Lessons Learned on Mobility as a Service (MaaS): Exploring Opportunities and Barriers for the U.S. Context</title>
      <link>https://rip.trb.org/View/2696846</link>
      <description><![CDATA[Mobility as a Service (MaaS) packages can increase the popularity of alternatives to owning (and using) a personal vehicle, through the integration of multiple transportation services and options on the same platform. Several MaaS solutions have been proposed in Europe and other regions of the world. However, there is a dearth of research on MaaS in the US context. This study will be a starting point to fill that gap. In this study, the researchers will review MaaS experience from abroad and investigate the lessons learned on the way MaaS works, the various levels of integration possible on the MaaS platform, the type of transportation services that are offered, and the way (bundle) payments and fare integration are handled. The study will then build US-specific knowledge on the potential attractiveness of MaaS-type mobility packages through hosting focus group discussions with groups of travelers, to identify their potential openness to adopt MaaS services, the perceived benefits that would be derived from their use, and the characteristics that MaaS solutions should have to (eventually) be attractive among selected groups of US travelers. The findings from this study will help understand what realistic paths may exist to integrate public transit and shared mobility solutions to expand travel options in the US, and how effectively these options might encourage travelers to increase travel multimodality and reduce their reliance on the use of private vehicles. This study will serve as a starting point for developing future larger studies on MaaS in the US context.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:03:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696846</guid>
    </item>
    <item>
      <title>Governance of Multimodal Planning: Examining Transit Corridor Planning Strategies in California</title>
      <link>https://rip.trb.org/View/2696845</link>
      <description><![CDATA[This project will evaluate sub-regional corridor-scale multimodal planning processes in California that aim to improve multi-modal access and mobility for reaching desired destinations (e.g. homes and residences). Research has demonstrated that transit and active transport use can be enhanced through coordinated planning of facilities and services on a corridor and/or travel network basis, especially when also coordinated with land use strategies. With some new and ambitious corridor/mobility-hub planning efforts currently underway in California, now is a good time to evaluate what makes them succeed or fail. Although the potential benefits from such strategies have been shown to be substantial, corridor-plus-station area planning faces considerable challenges, many of which concern the challenges of institutional coordination. Yet scant research has examined governance challenges of multimodal planning in California. This research project will address this neglected yet critical topic through case study analysis of selected current and past sub-regional corridor planning efforts in California. Through public documents analysis and interviews with stakeholders, the project will examine and compare the selected planning processes in relation to: stakeholders involved; goals, objectives, and operational measures established; planning and analytical processes/procedures pursued; stakeholder interests and concerns; policies, programs, and projects adopted and funded for implementing the objectives; and outcomes achieved (including measurable impacts for mode choice, VMT (Vehicle Miles Traveled), and access and mobility to desired destinations by mode). The project will identify best practices and obstacles and pitfalls for effective corridor planning and consider how the state government can support multimodal corridor planning, as these plans support the transformation of the transportation networks they cover by improving access to transportation modes and increasing travel mobility to desired destinations.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:01:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696845</guid>
    </item>
    <item>
      <title>A Robust Optimization-Based Approach for an Integrated Truck-Drone Emergency Resource Distribution System</title>
      <link>https://rip.trb.org/View/2684212</link>
      <description><![CDATA[The primary objective of this project is to develop and validate an integrated truck-drone coordination system that enhances emergency resource distribution through advanced optimization modeling and simulation. This system aims to improve delivery speed, service coverage, and operational efficiency during crisis situations. This project seeks to
address the challenges of disrupted transportation networks, uncertainty in demand locations, and inefficiencies in last-mile delivery during natural disasters. The primary stakeholders in this study include disaster relief agencies, emergency response teams, local government bodies, and logistics companies involved in post-disaster supply distribution. Efficient and adaptive delivery systems are crucial for these stakeholders, as traditional transportation methods often become inoperable due to damaged infrastructure limiting accessibility.

This proposal is about formulating multi-objective optimization models to coordinate multiple trucks and drones for emergence resource allocation. In such a coordination system, trucks can be used as depots, and drones can be used as delivery tools. To use drones beyond the last mile delivery, coordination points will be added between truck and customer locations. At such coordination points, drones may charge or exchange packages with other drones for longer delivery trips. Therefore, the research involves planning coordination points; coordinating delivery schedules; managing hand-offs between trucks and drones and between drones; and coordinating routes, altitudes, and timing for all active drones. The proposed model will improve emergency response efficiency and resilience during adverse conditions. The research team involves faculty members and students working in collaboration with North Carolina Department of Transportation (NCDOT) stakeholders.]]></description>
      <pubDate>Wed, 25 Mar 2026 17:23:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684212</guid>
    </item>
    <item>
      <title>Advancing Urban Co-Modality with Drone Delivery Services</title>
      <link>https://rip.trb.org/View/2684217</link>
      <description><![CDATA[Co-modality in transportation refers to the shared use of infrastructure between freight and passenger systems. While common in air and rail, its use in urban logistics and public transit is limited. This project proposes integrating drone-based last-mile delivery with public transit using fixed-route buses as mobile hubs. Drones would launch from and land on moving buses to enhance delivery flexibility and generate extra revenue for transit operators without raising operational costs. The research is divided into three thrusts: Thrust I assesses the business feasibility of urban co-modality by analyzing trade-offs between transit operators and last-mile logistics carriers. Thrust II examines the cost-effectiveness and reliability of drone delivery using performance modeling and simulations. Thrust III explores the integration between drone delivery and transit service based on the demand settings and drone configuration results from previous thrusts. The project will produce system design models and simulation tools for drone performance that apply to real-world scenarios.]]></description>
      <pubDate>Wed, 25 Mar 2026 16:53:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684217</guid>
    </item>
    <item>
      <title>Understanding Human-centered AI through Data Integration and Analytics</title>
      <link>https://rip.trb.org/View/2643429</link>
      <description><![CDATA[This project intends to develop course modules and tools for students to understand human-centered artificial intelligence (AI) methods and data requirements, conduct specific mobility analysis using AI-empowered technologies, and build multimodal technology solutions to improve mobility. TRB Special Report 275: The Workforce Challenge highlighted data and analysis needs as critical for future transportation professionals. Therefore, this education project will help build the data component currently weak or missing in transportation curricula. Also, the USDOT RD&T Strategic Plan highlights AI as an important technology and skill set for the next-generation transportation workforce. Thus, this project can also help train the future workforce for effective use of AI technologies in their practice.

The project outcome will deliver AI-based data integration and analytics education and workforce development modules that provide the transportation workforce with the knowledge and tools needed to assist them in improving the safety and efficiency of the nation’s transportation system through innovative human-centered AI transportation applications. The project outreach and technology transfer plan will provide greater access to continuing education and workforce development opportunities to meet the evolving needs of the transportation community at the local, tribal, state, regional, and national levels. Additionally, as part of the project tasks, the research team will build coalitions with local, tribal, state, regional, and national collaborators, and AI vendors to ensure that the education activities address emerging issues in Machine Learning and human-centered AI applications.]]></description>
      <pubDate>Mon, 22 Dec 2025 20:02:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2643429</guid>
    </item>
    <item>
      <title>Develop Multi-Modal Maritime-Rail-Roadway Transportation Model for the Texas Inland and Intercoastal Waterways</title>
      <link>https://rip.trb.org/View/2614515</link>
      <description><![CDATA[Texas plays a key role in freight transport nationally and internationally. Freight systems are highly multi-modal (maritime, rail, roadway) and complex, with infrastructure serving different cargo types (bulk goods, containers, hazardous material, etc.). The research team will develop a simulator and decision-support tool for routing and scheduling freight in this system. This work builds on and enhances simulation models and tools the research team has designed, developed, and deployed to successfully represent multi-modal freight operations in the Port of Houston, Houston Ship Channel, and Texas road and rail networks for past projects. This prior experience has shown that the greatest challenges involve data availability, computational speed for statewide modeling, and reflecting the complexities of real-world logistics in routing and scheduling algorithms. The work plan is specifically designed to address these challenges: in terms of data, the research team will use both publicly-available datasets (including the ones used to calibrate its past models) and its existing relationships with port and rail operators and other stakeholders; in terms of computation, the research team will explore a hybrid discrete-event simulator architecture; and in terms of realism, the research team will incorporate uncertainty and reliability in the decision-support tool.]]></description>
      <pubDate>Tue, 28 Oct 2025 11:09:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2614515</guid>
    </item>
    <item>
      <title>Advancing a Statewide Model for Multimodal Transportation Planning and Asset Management Integration in Rural and Small Urban Contexts</title>
      <link>https://rip.trb.org/View/2606412</link>
      <description><![CDATA[This research addresses the critical need for integrating transportation asset management (TAM) with transportation planning practices in rural and small urban contexts. Building on federal guidance from the Moving Ahead for Progress in the 21st Century Act (MAP-21) and subsequent legislation, the project will develop a statewide framework that strengthens the relationship between asset management and multimodal transportation planning across state, regional, and local agencies. The research focuses on three foundational components: meaningful stakeholder collaboration, high-quality integrated asset data, and aligned performance measurement processes. Using West Virginia as a pilot context, the study will evaluate current integration practices, assess multimodal asset data gaps, develop a prototype infrastructure database, and create a practitioner-focused toolkit for co-prioritization and performance-based decision-making. The methodology encompasses assessment of current practices, evaluation of data opportunities, database development, framework creation, and pilot implementation with selected agencies to test real-world applications and gather user feedback for refinement.]]></description>
      <pubDate>Thu, 02 Oct 2025 15:27:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2606412</guid>
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