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    <title>Research in Progress (RIP)</title>
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    <atom:link href="https://rip.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzkwIiAvPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSI3MzAiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMTYiIC8+PC9wYXJhbXM+PGZpbHRlcnMgLz48cmFuZ2VzIC8+PHNvcnRzPjxzb3J0IGZpZWxkPSJwdWJsaXNoZWQiIG9yZGVyPSJkZXNjIiAvPjwvc29ydHM+PHBlcnNpc3RzPjxwZXJzaXN0IG5hbWU9InJhbmdldHlwZSIgdmFsdWU9InB1Ymxpc2hlZGRhdGUiIC8+PC9wZXJzaXN0cz48L3NlYXJjaD4=" 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>
    <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>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>Assessing the Reliability and Usability of Mobile Ticketing App Data for Transit Analytics: A Case Study of Unitrans in Davis, California</title>
      <link>https://rip.trb.org/View/2702581</link>
      <description><![CDATA[Mobile ticketing apps have become increasingly popular among transit agencies due to their cost efficiency and ability to streamline payments. Beyond operational efficiencies, these apps also generate vast travel data with the potential to support transit agencies in decision-making. However, this data contains incomplete trip information and suffers from representation bias. Several questions remain unanswered: Is this data representative of all transit riders? If so, what are the potential applications? 

This project will address this gap by evaluating the potential applications and representativeness of app data. The research will focus on ZipPass, a mobile ticketing app used by Unitrans in Davis, California. To date, ZipPass has already generated over one million spatial activation records. The project team devised a strategy to integrate ZipPass data with the onboard transit survey and the UC Davis campus travel survey. The team will also conduct a targeted survey of active ZipPass users to supplement rider-specific and trip-level information. The project will explore how ZipPass data, along with support from supplementary data sources, can be used for two potential applications to support the agency: (1) estimating transit ridership and (2) understanding riders' origin-destinations. 

The research will provide valuable insights to transit agencies looking to harness mobile ticketing data for operational purposes. Since periodic onboard transit surveys are required for federal funding, both mobile ticketing data and transit survey data are available to agencies at no extra expense. Small agencies can leverage our findings to integrate at least these two datasets and effectively utilize them for operational improvement.]]></description>
      <pubDate>Thu, 14 May 2026 16:51:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2702581</guid>
    </item>
    <item>
      <title>Exploring the Relationships Among Perceived Safety, Perceived Accessibility, and Travel Behavior</title>
      <link>https://rip.trb.org/View/2702636</link>
      <description><![CDATA[Accessibility, the potential to reach various opportunities that are spatially dispersed, is an important concept in transportation that has garnered an extensive amount of research. The methods of measurement of accessibility are numerous, and there has been no consensus on a singular best practice. Typical methods of measuring accessibility do not take into account individual differences. Perceived accessibility measures offer another approach to understanding individual differences inaccessibility. This project will contribute to perceived accessibility literature by conducting a study in the United States (U.S.) context in the state of California, as there have not been many studies conducted in the U.S. First, semi-structured interviews with adults residing in the Sacramento Area Council of Government (SACOG) region will be conducted. Next, a cross-sectional survey will be conducted using a sample of SACOG region residents. It is important to understand how perceptions of safety and accessibility may influence mode choice and ability to access economic opportunities.]]></description>
      <pubDate>Thu, 14 May 2026 16:47:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2702636</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>Traveler Information for Rural Maryland</title>
      <link>https://rip.trb.org/View/2701236</link>
      <description><![CDATA[Disseminating traveler information in rural Maryland has long been challenging due to the limited deployment of Intelligent Transportation System (ITS) devices, such as dynamic message signs (DMS) and highway advisory radios (HARs). This challenge becomes particularly acute during major events, such as hurricanes or large-scale evacuations, when clear and accessible communication is critical. HARs, which operate on AM radio frequencies, have been a key tool for disseminating detailed information, but their reliance on outdated technology has made maintenance costly and increasingly unfeasible as spare parts become unavailable. The Maryland Department of Transportation State Highway Administration (MDOT SHA) has already retired half of its HARs and faces difficulty maintaining the remaining units, which are still vital in certain areas. ]]></description>
      <pubDate>Wed, 13 May 2026 09:12:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2701236</guid>
    </item>
    <item>
      <title>Evaluate the Safety Effects of Multiple Vehicle Synchronized Warning Lights in ODOT Work Zones
</title>
      <link>https://rip.trb.org/View/2701274</link>
      <description><![CDATA[In 2024, 56 Ohio Department of Transportation (ODOT) crews were struck while working on the highway system. As of March 2025, 43 ODOT crews have been struck. With safety being of the upmost importance to ODOT's Executive Leadership, protecting road crews and individuals working on ODOT jobsites remains a common theme when investigating new technologies and techniques to help reduce and minimize these accidents. Currently ODOT has a variety of light-emitting diode (LED) warning light systems in use on its fleet of maintenance vehicles. When these vehicles are concentrated in a work zone, there has been concern that these lights, while flashing independently, can lead to confusion among the motoring public as they enter the work zone. Added to this, ODOT operates work zones during all times of the day and in all weather conditions further exacerbates the situation.  This can result in unsafe driving practices and increased accidents. 

There is a growing opinion among transportation professionals that synchronizing warning lights and/or customizing patterns to evolve situationally could alleviate, if not resolve, these dangerous work zone crashes. ODOT is looking to evaluate the effectiveness of a system that synchronizes the warning systems of all vehicles present in a work zone.   A system that could increase driver awareness and reduce safety related incidents would be useful not only to ODOT but to local public agencies, emergency responders, and other state departments of transportation (DOTs).

OBJECTIVES: The goal of this research is to identify the effectiveness of using synchronized warning light systems versus non-synchronized warning light systems on work zone vehicles.
             ]]></description>
      <pubDate>Tue, 12 May 2026 10:43:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2701274</guid>
    </item>
    <item>
      <title>Which Way Forward? Learning from Global Informal Transport Networks to Inform Microtransit Services in California</title>
      <link>https://rip.trb.org/View/2695811</link>
      <description><![CDATA[This proposed 12-month study seeks to draw upon lessons learned from informal transit systems, particularly from the developing world, to inform the development and implementation of demand-responsive transit (often referred to microtransit) strategies in California. Through a comprehensive review of existing literature, case studies (n= up to 5), and expert interviews (n=15-20), this study aims to identify lessons learned, challenges, and opportunities associated with informal transit operations. Leveraging this understanding, the research will assess how such lessons can be applied to the design, deployment, and evaluation of microtransit and other demand-responsive services in California communities, including transportation network companies (TNC) and taxi models. Key areas of focus include business and operational models, fare affordability and financial sustainability (including operational costs), and potential policy frameworks. By synthesizing insights from informal transit experiences internationally, this proposed study seeks to contribute to the development of efficient and sustainable microtransit and demand-responsive strategies tailored to the diverse needs of all travelers.]]></description>
      <pubDate>Thu, 23 Apr 2026 18:05:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2695811</guid>
    </item>
    <item>
      <title>Corridor Speed Management Strategies Toolbox</title>
      <link>https://rip.trb.org/View/2693730</link>
      <description><![CDATA[This project will develop a repository of speed management countermeasures that are applicable for use in Virginia based on existing evidence-based research of traffic calming and speed management practices in the United States. The scope will be limited to applications for state and regional highways. The toolbox will include countermeasure definitions and a description of the appropriate context for application. The countermeasure details may include contexts with evidence for speed management effectiveness, contexts where countermeasures may be appropriate, and contexts where further research is needed to justify their use. This contextual guidance will provide useful information for practitioners in Virginia.     ]]></description>
      <pubDate>Thu, 16 Apr 2026 10:46:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2693730</guid>
    </item>
    <item>
      <title>An Efficient Algorithm for Solving Collaborative Truck-Drone Parcel Delivery System Considering En-Route Launching and Recovery Points</title>
      <link>https://rip.trb.org/View/2692315</link>
      <description><![CDATA[The logistics industry faces significant challenges in keeping up with evolving demand and supply conditions, especially in urban areas. Traffic congestion during peak hours makes on-time delivery hard. Moreover, time-sensitive products, such as emergency blood and medicine, must be delivered to the customer at the desired time. Drones are a viable solution to urban logistics problems, as they offer several benefits for package delivery. Drones are resilient to traffic delays since they function independently of road infrastructure, unlike conventional vehicles. However, drones have capacity and other constraints; therefore, collaborating with a drone and a truck can make the delivery system more efficient. Although there has been significant research interest in developing truck-drone routing algorithms, a gap remains in developing models that allow for en-route drone launching points and recovery points. The prior research on truck-drone routing assumes that the truck can only reconnect with a drone at a customer location. This project will expand on the prior work to develop optimization models and algorithms to allow with en-route meet points. This added dimension has the potential to reduce truck vehicle miles and subsequently congestion. The solution framework will employ a dynamic programming-based algorithm for the initial solution and a synchronized drone dispatch algorithm to determine the launching and recovery points along the truck route. The proposed algorithm will be able to provide solutions for real-world large instances.]]></description>
      <pubDate>Tue, 14 Apr 2026 12:15:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2692315</guid>
    </item>
    <item>
      <title>Transportation Corridor Fuel Consumption Calculator (TCFCC) Version 5.0</title>
      <link>https://rip.trb.org/View/2692310</link>
      <description><![CDATA[The Transportation Corridor Fuel Consumption Calculator (TCFCC) updates and enhances Georgia Tech’s 2018 spreadsheet-based modeling tool (http://fec.ce.gatech.edu/) that allows users to assess on-road fuel consumption under real-world traffic conditions. The team will incorporate the latest fuel use rates from the MOVES 5.0 model (2025) and extend the capabilities of the previous FEC to allow users to specify any one of more than 60 standard laboratory driving cycles that best represent corridor traffic congestion, and to incorporate any monitored or modeled second-by-second driving trace. Users specify fleet and model year composition, and the tool models corridor-level fuel consumption as a function of congestion. Hence, the tool allows users to assess the consumer fuel savings and cost savings of proposed congestion mitigation strategies that provide smooth traffic flow. The tool is directly applicable to the assessment of traffic signal coordination, ramp metering, express lane operations, etc. The research team will update the model to incorporate MOVES 5.0 model outputs, extend calendar year coverage to 2060, and introduce 40+ new driving cycles that are representative of urban, suburban, and freeway corridors. The project will deliver separate calculator spreadsheets for light-duty passenger cars, heavy-duty trucks, and express buses, each calibrated for mode-specific load factors and driving patterns. A new second-by-second fuel-use worksheet will allow users to input their own driving cycles for detailed vehicle-specific analysis. By focusing on fuel consumption, the project provides a technically neutral and performance-based approach for evaluating corridor operations and fleet technologies. The center will release the TCFCC as open source, encouraging further development and integration with travel demand and simulation models.]]></description>
      <pubDate>Tue, 14 Apr 2026 12:07:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2692310</guid>
    </item>
    <item>
      <title>Enabling Next-Generation Safe, Efficient and Reliable Traffic Signal Management via Advanced Sensing and Foundation Models</title>
      <link>https://rip.trb.org/View/2691670</link>
      <description><![CDATA[Urban traffic signal management systems often rely on outdated techniques and strategies that fail to adapt to dynamic roadway conditions, leading to safety concerns, congestion, and access issues for road users. In addition, current signal optimization approaches rarely consider energy efficiency as the main objective. This research proposes a next-generation safe, efficient and reliable traffic signal control framework powered by advanced roadside sensing and foundation models, specifically Visual Language Models (VLMs) and Multi-Modal Large Language Models (MMLLMs). By integrating high-definition cameras, LiDAR, and real-time data analytics, the system will accurately detect multimodal traffic flows, predict future traffic conditions, and optimize signal phase and timings to enhance mobility while minimizing energy consumption. The framework will be validated through a case study at the Riverside Smart Intersection testbed, leveraging real-world data and co-simulation environments.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:42:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691670</guid>
    </item>
    <item>
      <title>Optimizing Last-Mile Delivery Using Micromobility and Autonomous Technologies: A
Scalable Framework for Future Logistics Solutions</title>
      <link>https://rip.trb.org/View/2684210</link>
      <description><![CDATA[Freight delivery is essential to urban mobility and the economy but contributes to congestion, emissions, and infrastructure wear. With e-commerce growth, it is vital
to improve last-mile delivery (LMD), which can comprise up to 51% of logistics costs. This proposal introduces a framework combining micromobility and autonomous technologies to optimize LMD. These solutions offer flexible and labor-efficient alternatives for dense, high-traffic environments. The goal is to ease congestion and enhance delivery efficiency. Real-world case studies in urban and semi-urban settings will assess the framework’s feasibility, scalability, and overall impact.
OBJECTIVES/GOALS:
• Develop a scalable framework for LMD that integrates various micromobility and
autonomous technologies to optimize routes and identify the best delivery options for
policymakers.
• Design algorithms for efficient route planning that enhance operational efficiency, reduce fuel/electricity costs, and improve delivery speed.
• Evaluate the feasibility of different delivery methods, taking into account constraints and
practical considerations to ensure real-world applicability.
• Improve system resilience by enabling real-time route adjustments to address real-world obstacles, such as road repairs and traffic disruptions.
• Validate the proposed framework through agent-based simulation using Amazon’s last-mile data  to demonstrate its effectiveness.
• Leverage AI-powered technology to analyze historical data and predict demand to enable dynamic adaptation of delivery methods, such as deploying more drones on weekdays and fewer on weekends in specific areas, to optimize operational performance.]]></description>
      <pubDate>Wed, 25 Mar 2026 17:38:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684210</guid>
    </item>
    <item>
      <title>A Data-driven Approach in Improving Truck Parking Efficiency</title>
      <link>https://rip.trb.org/View/2684213</link>
      <description><![CDATA[Freight transportation systems are a critical component of the United States' economy, underscoring the importance of adequate truck parking to ensure safe and efficient operations. However, a significant disparity between truck parking demand and supply has resulted in numerous challenges, including increased road safety risks, regulatory non-compliance, and operational inefficiencies. This study aims to address this knowledge gap by conducting a comprehensive review of current truck parking management approaches, with a focus on data-driven prediction models, and truck parking pattern analysis. In collaboration with the North Carolina Department of Transportation (NCDOT), the study will analyze truck parking patterns along key freight corridors and develop data-driven solutions to enhance parking efficiency and address these pressing challenges.

This project aims to address this gap by conducting a comprehensive review of existing literature and offering a nuanced exploration of potential truck parking solutions. Using NC as a case study, the project will provide data-driven recommendations to improve the efficiency and utilization of existing parking facilities along key freight corridors. By enhancing the safety and efficiency of truck parking, this study will directly benefit truck operators, supply chain stakeholders, regulatory agencies, and local communities. The findings will serve as a foundation for informed policymaking and infrastructure planning, ensuring that North Carolina’s freight transportation network remains resilient, sustainable, and operationally efficient in the face of growing demands.]]></description>
      <pubDate>Wed, 25 Mar 2026 17:16:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684213</guid>
    </item>
    <item>
      <title>Spatial Modeling to support Supply Chain Policymaking across Metropolitan Areas in
Tennessee</title>
      <link>https://rip.trb.org/View/2684214</link>
      <description><![CDATA[Efficient supply chains are critical for economic growth of metropolitan areas. Despite the strong logistics and manufacturing sectors in Tennessee, a comprehensive understanding of supply chain dynamics across its key Metropolitan Statistical Areas (MSAs) remains limited. This research aims to bridge this gap by applying supply chain metrics and advanced modeling techniques to measure centrality, spread, and dispersion of economic activities. The project will systematically map the logistical landscape in key MSAs of Tennessee, providing a data-driven foundation for identifying economic clustering patterns. This research will serve as a decision-support framework to assist policymakers, transportation agencies, and industry stakeholders in designing supply chain strategies. This research will enhance freight efficiency and resiliency, reduce congestion, and improve economic competitiveness.

A deeper understanding of the spatial organization of economic activity is essential to support data-driven planning and policy development (Holguin-Veras et al., 2021). Effectively mapping the spatial dynamics to inform practical decision-making remains a significant challenge. A comprehensive view of supply chain structure across Tennessee’s MSAs would enhance the state’s economic competitiveness and support better coordination of land use and freight infrastructure. This research addresses that need by estimating spatial metrics to identify economic poles and quantify supply chain dispersion. It integrates spatial analysis and supply chain modeling to examine the distribution of economic activity and supply chain echelons, using network-based distances, industry-specific demand functions, and freight-relevant dispersion metrics. Project insights will help planners to assess freight systems and improve urban logistics efficiency.]]></description>
      <pubDate>Wed, 25 Mar 2026 17:11:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684214</guid>
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