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    <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" />
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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>Smart Drop-Shipping and Stocking Decision Support System</title>
      <link>https://rip.trb.org/View/2703794</link>
      <description><![CDATA[Drop-shipping is an increasingly important order fulfillment strategy in modern supply chains, allowing firms to reduce inventory holding costs by shipping products directly from suppliers to customers. However, because inventory is not directly controlled by the firm, drop-shipping can introduce uncertainty in product availability, delivery lead times, and service reliability. To compensate, firms often rely on expedited transportation, which increases costs and may negatively affect safety and efficiency in freight operations. These trade-offs create a challenging decision problem: determining which products should be stocked internally, fulfilled through drop-shipping, or managed under a mixed fulfillment strategy.
Industry interviews with a major U.S. wholesaler indicate that firms tend to rely on drop-shipping for slow-moving products due to limited warehouse space and capital constraints, yet lack systematic, data-driven methods to guide these decisions Existing research largely focuses on single-product settings or coordination issues between retailers and suppliers and does not address multi-product decisions under warehouse capacity constraints.
This project aims to fill this gap by developing an optimization-based decision support framework for drop-shipping and inventory planning across multiple stock-keeping units (SKUs). The proposed approach integrates mixed-integer programming with meta-heuristic methods to support large-scale, real-world applications. The model incorporates demand patterns, inventory holding costs, transportation costs, service level requirements, and cash flow constraints. A complementary simulation framework will be developed to evaluate system performance under uncertainty in demand, supplier inventory availability, and delivery times.
The project supports Mid-America Transportation Center (MATC) themes of Safety and Transportation Systems of the Future by enabling more predictable and efficient freight movements, reducing reliance on expedited shipping, and promoting data-driven planning in distributed fulfillment networks. Expected outcomes include an implementable decision support tool, analytical insights for industry stakeholders, and dissemination through publications and conference presentations.]]></description>
      <pubDate>Sat, 16 May 2026 11:49:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703794</guid>
    </item>
    <item>
      <title>New Models and Solutions to Vehicle Routing with Cardinality and Distance Constraints</title>
      <link>https://rip.trb.org/View/2703788</link>
      <description><![CDATA[Many emerging transportation and logistics operations are constrained by both the maximum distance a vehicle can travel and the number of customers it can serve before requiring replenishment, recharging, or maintenance. These operational realities motivate the need for new routing optimization models that explicitly integrate distance and cardinality constraints. This project proposes the first comprehensive study of a novel Black-and-White Vehicle Routing Problem (BWVRP), where customer nodes and replenishment nodes are jointly routed across a fleet of vehicles, with replenishment nodes allowed to be visited multiple times. The project will develop new mixed-integer linear programming models and exact branch-and-cut methods to obtain optimal solutions for small and medium-sized instances. To address large-scale instances, efficient heuristic and metaheuristic algorithms will be designed and implemented. In addition to methodological advances, the project will develop a data-driven optimization decision-support tool integrating models, algorithms, and user-friendly interface. 
]]></description>
      <pubDate>Sat, 16 May 2026 11:45:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703788</guid>
    </item>
    <item>
      <title>Optimization-Based Framework and Decision-Support Tool for Bridge Toll Implementation Under Behavioral and Operational Constraints

</title>
      <link>https://rip.trb.org/View/2696158</link>
      <description><![CDATA[This project aims to develop a novel theoretical framework and a practical decision-support tool to guide strategic bridge toll implementation under real-world behavioral and operational constraints. Traditional toll optimization and project evaluation models focus on market uncertainties but neglect critical human behavioral factors, such as present bias, that significantly influence decision outcomes. To bridge this gap, the proposed research introduces an optimization framework that integrates behavioral dynamics into infrastructure decision-making, enabling the identification of strategies that maximize long-term social welfare while addressing short-term user response and implementation pressures. The accompanying decision-support tool will translate this framework into an interactive, user-friendly platform for transportation agencies and policymakers. It will allow users to simulate and compare alternative tolling strategies, assess implementation timelines, and visualize trade-offs between system efficiency, user response, and long-term performance outcomes. By empowering decision-makers to make data-driven, welfare-maximizing choices, this project supports more effective, publicly acceptable, and operationally robust tolling practices. The research will generate theoretical advances, peer-reviewed publications, and an actionable tool ready for integration with Florida Department of Transportation's (FDOT’s) planning processes, ultimately contributing to more resilient, safe and efficient transportation infrastructure systems.]]></description>
      <pubDate>Mon, 27 Apr 2026 19:59:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696158</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>Efficient Mobility for Rural Communities</title>
      <link>https://rip.trb.org/View/2683240</link>
      <description><![CDATA[Rural communities face unique transportation challenges due to low population density, dispersed destinations, and limited resources. Efficient mobility requires strategies that both optimize existing transit systems and embrace innovative service models. This project integrates two lines of research that can support efficient mobility and access to active modes and destinations in rural areas. The first focuses on right-sizing rural transit fleets and the second, on analyzing modal shifts from shared-use mobility services.  

On the vehicle procurement side, rural agencies must balance capital and operating costs, service quality, and reliability while meeting the capacity needs of their riders. When procuring vehicles, rural transit operators choose between transit buses, cutaways, vans, and minivans of various sizes. Each has its own advantages and disadvantages regarding capital costs, operating costs, performance, service quality, and ability to meet the needs of their users. This study builds upon previous research to develop spreadsheet-based user tools that can help rural agencies and state departments of transportation (DOTs) make these decisions. This research improves upon the decision tools developed in a previous study by incorporating new models for the capacity needs of rural transit. Previous decision tools provide guidance on the types and sizes of vehicles to procure but they lack the ability to estimate capacity needs for individual agencies. This study develops a vehicle procurement decision model that incorporates a more sophisticated capacity needs analysis. The result will be a tool that estimates capacity needs of an agency and provides guidance on the number, types, and sizes of vehicles that can best meet that capacity need while improving efficiency and meeting the unique needs of the transit agency.  

A second tool to be developed by the study is an optimization tool. The study will improve upon a previously developed optimization model by incorporating more detailed estimates for lifecycle costs of different types and sizes of vehicles. The study will consider transit buses, cutaways, vans, and minivans of different sizes and seating capacities and include more detailed analysis of the operating costs for each vehicle type and the overall lifecycle costs. This will be incorporated into an optimization model that will minimize total costs, including capital and operating costs, for an agency while meeting capacity needs and service requirements and considering the impact of the fleet configuration on service quality.  

At the same time, emerging technology-enabled shared-use services—such as ridesourcing, microtransit, bikesharing, and carsharing—are transforming mobility in rural areas. By analyzing National Household Travel Survey data and documenting real-world deployments, the study will evaluate adoption patterns, service models, and performance outcomes of rural shared-use systems. This analysis will identify factors contributing to the success or failure of shared-use mobility initiatives and provide actionable strategies for rural agencies and policymakers . ]]></description>
      <pubDate>Tue, 24 Mar 2026 14:22:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2683240</guid>
    </item>
    <item>
      <title>Potential Impact of Autonomous Vehicles on Reducing Congestion</title>
      <link>https://rip.trb.org/View/2676004</link>
      <description><![CDATA[Traffic congestion is a major problem in large metropolitan areas in the United States. In 2022, on average, a commuter lost about $1,259 in monetary terms annually due to congestion nationwide, which amounts to 8.7 billion lost hours in total. The lack of coordination among individual users, who make routing decisions independently based on current traffic information without anticipating that others may follow similar decision-making patterns, contributes significantly to the high cost of congestion.

The behavior of drivers optimizing their individual routes leads to a state known as the User Equilibrium (UE), leading to travel times that can be significantly higher than travel times from the System Optimal (SO), particularly in congested urban networks where the effects of individual decisions cascade throughout the system. With the future emergence of autonomous vehicles, it is possible that organizations may now own more of the fleet of vehicles and control their routing, providing the organization more options for balancing route selections and thus making it possible to find routing solutions closer to the system optimal. Driverless ride-hailing companies such as Waymo have already begun their service in five major cities across the United States and Tesla has started to test their Robotaxi service in Austin, Texas. The centralized routing capabilities of these autonomous services have the potential to reduce congestion. This first phase of this research project will develop centralized optimization models to quantify the impact of using autonomous vehicles on ride-hailing platforms in reducing congestion.]]></description>
      <pubDate>Tue, 03 Mar 2026 16:17:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676004</guid>
    </item>
    <item>
      <title>Optimizing Signal Timing Through New Technologies</title>
      <link>https://rip.trb.org/View/2640688</link>
      <description><![CDATA[Traditional signal timing optimization is time consuming and requires engineering expertise, often resulting in long delays between optimization cycles. New technologies could provide an opportunity to make the process more efficient by early identification of locations where reoccurring congestion is occurring.  The objectives of this research project are to do a detailed feasibility study of technologies that can aid in identifying locations where current signal timing is causing delays and a process document for implementation of the technology.]]></description>
      <pubDate>Tue, 16 Dec 2025 09:06:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2640688</guid>
    </item>
    <item>
      <title>Empowering Para-Transit through Automation: A Behaviorally Informed Optimization Approach
</title>
      <link>https://rip.trb.org/View/2625307</link>
      <description><![CDATA[The key innovation of this project is the promotion of behaviorally informed paratransit automation design. The team bridges the gap between behavioral and operational research by integrating insights from both fields to leverage Connected Autonomous Vehicles (CAVs) to enhance mobility and independence for people with disabilities.

Behavioral research provides a deep understanding of how individuals with disabilities interact with transportation systems, highlighting their unique needs and preferences. On the operational side, the project employs advanced technology and optimization algorithms to develop efficient and reliable paratransit systems.

By combining behavioral research with cutting-edge technology and optimization techniques, the Northwestern team aims to set a new standard for transit automation, ensuring that individuals with disabilities have the mobility and independence they deserve.

This research aligns with the Center for Connected and Automated Transportation (CCAT) theme of Thrust 4: Fairness, as behaviorally informed paratransit automation design aids in achieving the equitable allocation of transportation services for users with special considerations, such as disabilities.

The Regional Transportation Authority (RTA) is a project partner and will provide key data for the analysis. The research promises to support the mission of RTA by preparing transit operators to develop more widely accessible and cost-effective operational strategies in an era of increasing automation.]]></description>
      <pubDate>Thu, 13 Nov 2025 14:58:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625307</guid>
    </item>
    <item>
      <title>A Reinforcement Learning Framework for Dynamic Inland Waterway Maintenance Under Stochastic Shoaling and Annual Budget Allocation</title>
      <link>https://rip.trb.org/View/2620600</link>
      <description><![CDATA[This research proposes a dynamic, data-driven framework for long-term inland waterway maintenance planning that integrates reinforcement learning (RL), and stochastic modeling. Unlike traditional models that assume deterministic sedimentation, known multi-year budgets, and static decision horizons, the research team models shoaling as a stochastic process, budgets as annually realized random variables, and infrastructure deterioration as a gradual, condition-dependent process. The core of the methodology is an infinite-horizon sequential decision model that makes year-by-year dredging and lock maintenance decisions using RL. Dredging is modeled as a continuous decision variable, and policy learning is guided by a custom-designed simulation environment that reflects realistic physical and institutional constraints. The team trains RL agents using Proximal Policy Optimization (PPO). This work addresses the curse of dimensionality that limits conventional optimization techniques by learning generalizable policies rather than enumerating all possible scenarios. By finding the solution across various uncertainty regimes, the team provides both methodological insights and practical guidance for agencies such as the U.S. Army Corps of Engineers. The resulting framework offers a robust and adaptive tool for managing long-term infrastructure investment under uncertainty]]></description>
      <pubDate>Mon, 10 Nov 2025 09:33:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2620600</guid>
    </item>
    <item>
      <title>Urban Network Speed Optimization for Connected Automated Vehicles: Development and Testing</title>
      <link>https://rip.trb.org/View/2606410</link>
      <description><![CDATA[This research develops and evaluates optimal speed control strategies for Connected and Automated Vehicles (CAVs) at the network level, addressing critical gaps in existing research by incorporating multiple powertrain technologies including internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), and hydrogen fuel cell vehicles (HFCVs). The study addresses real-world challenges such as communication delays, data transmission errors, and vehicle actuation complexities that are often overlooked in idealized research conditions. Using the INTEGRATION microscopic traffic simulation software, the research will implement advanced communication modules for vehicle-to-vehicle and vehicle-to-infrastructure interactions alongside vehicle speed control modules. The methodology involves formulating speed trajectory optimization as a constrained problem incorporating vehicle dynamics, fuel consumption models for different powertrains, and signal phase and timing data. Dynamic programming methods including A-star search algorithms will ensure real-time computational efficiency. The research includes extensive testing across varied traffic networks with different congestion levels and CAV market penetration rates, culminating in a scalable framework for generalizing results to large-scale networks including the entire U.S. roadway system through collaboration with Saudi Aramco.]]></description>
      <pubDate>Thu, 02 Oct 2025 15:21:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2606410</guid>
    </item>
    <item>
      <title>Field Deployment and Testing of Enhanced Fixed- and Actuated-Traffic Signal Control Systems</title>
      <link>https://rip.trb.org/View/2606409</link>
      <description><![CDATA[This research conducts field deployment and testing of enhanced traffic signal control systems using the Laguna-Du-Rakha formulation to optimize signal timings for reduced vehicle delays and fuel consumption at signalized intersections. Building on previous work demonstrating that traditional Webster formulation methods produce cycle lengths nearly three times longer than optimal under congested conditions, the study implements and validates improved signal timing approaches through real-world field testing in collaboration with Virginia Department of Transportation. The methodology involves identifying candidate intersections in the Blacksburg and Salem area, with primary focus on the Beamer Way and Southgate Drive intersection equipped with LiDAR surveillance instrumentation tracking objects within 150 meters. Optimized cycle lengths will be calculated using multi-objective optimization balancing delay minimization and fuel consumption reduction through adjustable weighting factors. Field implementation includes one-week deployment of optimized signal timing plans with LiDAR-based trajectory data collection for performance quantification including queue lengths, vehicle delays, stops, and fuel consumption measurements. VISSIM microsimulation modeling creates digital twins of selected intersections for validation against field data and sensitivity testing across various traffic demand levels and cycle length weight combinations, enabling assessment beyond observed field conditions and identification of optimal control strategies.]]></description>
      <pubDate>Thu, 02 Oct 2025 15:18:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2606409</guid>
    </item>
    <item>
      <title>Clay and Shale LRFD Design Criteria for Drilled Shaft Foundations</title>
      <link>https://rip.trb.org/View/2604567</link>
      <description><![CDATA[The Texas Department of Transportation's (TxDOT) geotechnical design guidance has transitioned from using Texas Cone Penetrometer (TCP) boring logs and capacity correlations to design drilled shaft foundation elements with American Association of State Highway and Transportation Officials' Load and Resistance Factor Design (AASHTO LRFD) investigations and resistance-based design methods. The research team will optimize TxDOT's drilled shaft design methods and resistance factors for clay and shale in accordance with TxDOT's specific soil and construction conditions. The research team will develop a likelihood map of shale for a district selected by TxDOT.]]></description>
      <pubDate>Mon, 29 Sep 2025 16:35:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2604567</guid>
    </item>
    <item>
      <title>Develop Performance Models for Different Preventive Maintenance Treatments</title>
      <link>https://rip.trb.org/View/2604521</link>
      <description><![CDATA[The Texas Department of Transportation's (TxDOT) Pavement Management Information System (PMIS), recently implemented as Pavement Analyst (PA), stores and analyses network information and pavement performance data to select projects for maintenance and rehabilitation. After the network is analysed, an optimization algorithm produces one of the following recommendations: do nothing, preventive maintenance (PM), light, medium or heavy rehabilitation. PM includes several treatment options associated with very different cost and performance, e.g., seal coat, thin overlay, microsurfacing, etc. Since the performance and cost of these options are different, research is needed to quantify the difference and to incorporate this information into PMIS. Previous TxDOT-sponsored projects have developed performance models and decision trees that were last updated in 2021 as part of Project 0-6988, "Quantification of the Performance of Preventive Maintenance and Rehabilitation Strategies." This update was based on data available at that time, which was a combination of visual distress surveys and automated data. Now, more accurate automated data are available. Therefore, the research team will develop pavement performance models for different PM treatments, update the current decision trees and performance models, validate the models with new data, and develop an implementation plan to incorporate the findings into PMIS.]]></description>
      <pubDate>Mon, 29 Sep 2025 16:08:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2604521</guid>
    </item>
    <item>
      <title>Innovations Deserving Exploratory Analysis--The Transit IDEA Program. IDEA 109. Simulation-based Decision-Making Tool for Microtransit Service Evaluation and Optimization</title>
      <link>https://rip.trb.org/View/2572333</link>
      <description><![CDATA[Microtransit refers to an on-demand, dynamically routed, mobile-app-powered shuttle service with rider walking exchange. Microtransit is an emerging transit service that seems to improve riders’ experience by operating small-sized shuttles that can offer flexible routes and on-demand scheduling services. It is essentially a “smart bus service,” similar to the service of transportation network companies such as Uber and Lyft in which riders are required to book a trip using a smartphone app and get picked up in minutes at a corner by exchanging a short walk distance (walking exchange) or just waiting at the original address (door-to-door). The user may be matched with other passengers heading in the same direction to share their trips, defined as “ridesharing.” An increasing number of places are deploying microtransit to serve their residents and visitors, such as L.A. Metro in Los Angeles, California and King County Metro in Seattle, Washington. Most microtransit services integrate into existing public transit systems, such as city buses, to enhance transit service where running fixed-route buses is rather challenging.

This project is aimed at developing a simulation-based decision-making toolkit to help transit agencies evaluate microtransit service performance, optimize its operation, and make evidence-based decisions on resource distribution. Existing simulation models do not adequately represent on-demand microtransit with ride walking exchange, which makes it difficult to optimize the system and make decision with some assurance. The proposed open source microtransit simulation toolkit will be flexible and can be easily modified to apply to any place or community in any scenario. 

The proposed microtransit simulation toolkit will be based on the Simulation of Urban Mobility (SUMO) package. The flexibility and compatibility of the toolkit as a platform will enable its integration with rider walking exchange and vehicle operations, such as pick up and drop off (PUDO) strategy, or as “plugin-and-play” functional modules. A greedy heuristic shuttle routing approach will be developed for efficiently solving large scale shuttle-rider matching and routing problem, which is a major road blocker for simulating real world microtransit systems. Partnering with the city of Wilson, North Carolina, the proposed microtransit simulation toolkit will be calibrated and validated with real-world traffic and microtransit operation data. In addition, a simulation-based recursive framework will be proposed to quantitively describe the interplay between microtransit’s ride demand and behavior, supply (e.g., system operation and design) and service (e.g., waiting time) and provide a complete and systematic solution for optimizing microtransit systems. Finally, a webpage interface will be designed to take the system design inputs and display simulation and optimization results through a graphical user interface (GUI) on a website. The toolkit can be easily applied by transit agencies interested in testing different operation scenarios for their microtransit systems. 

The benefits of deploying on-demand microtransit are expected to be significant in the light of socio-economy and mobility while enhancing transportation equity, mobility convenience, job access, and reducing traffic congestion, energy consumption, and greenhouse gas emissions.]]></description>
      <pubDate>Tue, 08 Jul 2025 17:11:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2572333</guid>
    </item>
    <item>
      <title>SPR-5033: Optimizing field compaction of granular materials: Research and guideline development</title>
      <link>https://rip.trb.org/View/2553994</link>
      <description><![CDATA[This proposal addresses INDOT’s need for material-specific, performance-based compaction guidelines by evaluating alternative lab and field-testing methods. The research will identify optimal compaction procedures and field control strategies tailored to each material, helping INDOT reduce variability, improve long-term pavement performance, and implement more cost-effective, durable, and consistent construction practices.]]></description>
      <pubDate>Thu, 15 May 2025 16:03:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553994</guid>
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