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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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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>
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    <item>
      <title>Synthesis of Information Related to Highway Practices. Topic 57-09. Practices for Using Crowdsourced Data for Asset Inventory and Condition Assessment</title>
      <link>https://rip.trb.org/View/2630489</link>
      <description><![CDATA[State departments of transportation (DOTs) are increasingly incorporating crowdsourced data into their data collection, management, and analysis activities. Once considered experimental, crowdsourced information has become a mainstream resource, drawing from navigation application user reports, social media, in-house applications, third-party probe services, and active transportation platforms. Federal initiatives have encouraged this shift by providing frameworks for institutionalizing the practice and demonstrating cost savings.
Beyond traffic operations, state DOTs are applying crowdsourced data to maintenance and asset management activities, including identification of potholes, roadway debris, and damaged infrastructure. Crowdsourced data offers the potential to complement traditional sources, expand coverage, improve safety and reliability, and reduce costs.

The objective of this synthesis is to document state DOT practices for using crowdsourced data to support asset inventory and condition assessments and to support maintenance and asset management system operations and planning.

Information to be gathered includes (but is not limited to): (1) Types of crowdsourced data used for maintenance and asset condition, and the applications or platforms through which they are collected; (2) Practices for validating accuracy, ensuring quality, and addressing timeliness of crowdsourced data; (3) Approaches for integrating crowdsourced data into maintenance and asset management systems, including workflows, work-order systems, Maintenance Management System (MMS), and Enterprise Resource Planning (ERP); (4) Use of data fusion, dashboards, and visualization to support decision-making; (5) Organizational policies, governance, and roles for managing crowdsourced data, including standards and retention; and (6) Implementation strategies and challenges, including incentives and disincentives for adoption.

Information will be gathered through a literature review, a survey of state DOTs, and follow-up interviews with selected DOTs for the development of case examples. Information gaps and suggestions for research to address those gaps will be identified.

Information sources (partial): (1) Kelley Klaver Pecheux, Benjamin B. Pecheux, Gene Ledbetter, and Chris Lambert. NCHRP Research Report 952: Guidebook for Managing Data from Emerging Technologies for Transportation (2020). Transportation Research Board. https://doi.org/10.17226/25844. (2) Botshekan, M., Asaadi, E., Roxon, J., Ulm, F. J., Tootkaboni, M., and Louhghalam, A. (2021). Smartphone-Enabled Road Condition Monitoring: From Accelerations to Road Roughness and Excess Energy Dissipation. Proceedings of the Royal Society A, 477(2246), 20200701. (3) Chuang, T. Y., Perng, N. H., and Han, J. Y. (2019). Pavement Performance Monitoring and Anomaly Recognition Based on Crowdsourcing Spatiotemporal Data. Automation in Construction, 106, 102882.
(4) FHWA. Road/Intelligent Transportation System (ITS) Maintenance Crowdsourcing Example. https://ops.fhwa.dot.gov/crowdsourcing/examples/maintenance.cfm.
(5) FHWA. Crowdsourced Data and Safety Performance at State DOTs and Local Partners. (6) Case examples of select transportation agencies. (7) FHWA. Crowdsourcing For Advancing Transportation Operations. (8) FHWA. EDC-5/EDC-6 Crowdsourcing for Operations.]]></description>
      <pubDate>Wed, 26 Nov 2025 15:58:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2630489</guid>
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    <item>
      <title>Agentic Artificial Intelligence Framework for Enabling Automation in Bridge Inventory Database Using Large Language Models</title>
      <link>https://rip.trb.org/View/2570732</link>
      <description><![CDATA[An ideal bridge inventory database is a structured, accessible repository of comprehensive information about bridges, such as their condition, inspection history, load capacities, design types, age, and other relevant attributes. Such database is essential to support data-informed and cost-effective bridge asset management and preservation. However, current practices for retrieving information/insights from and updating the databases lack automation, are slow and extremely expert-demanding. The increasing amount and the heterogeneous (multi-modal) nature of the data make it increasingly challenging to manually synthesize and distill useful insights from and/or updating the databases, calling for smart analytics technologies to automate the management, extraction, and interpretation of bridge inventory data. While large language models (LLMs) have shown the capability of comprehending multi-modal data, they remain significantly underutilized in bridge management. This project will investigate the viability of using LLMs to build artificial intelligence (AI) agents that can extract, memorize bridge condition from inspection records/reports, and enable standardized interpretation and organization of insights to support bridge preservation. The AI agents will convert raw and semi-structured bridge inventory data (e.g., inspection narratives, images, sensor signals) into structured database entries, summaries, and actionable recommendations. Users can interact intuitively with the AI agents via natural language queries, enabling efficient retrieval and interpretation of critical insights for bridge management. The agentic AI framework can achieve specified goals with minimal human/expert intervention.]]></description>
      <pubDate>Wed, 02 Jul 2025 12:02:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2570732</guid>
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      <title>Building and Facility Asset Inventory Assessment and Maintenance for KYTC</title>
      <link>https://rip.trb.org/View/2417067</link>
      <description><![CDATA[The Finance and Administration Cabinet (Division of Real Properties) oversees the Commonwealth’s real property, including land and/or buildings (e.g., offices, maintenance and equipment garages, storage sheds, rest areas, security fences) used by Kentucky Transportation Cabinet (KYTC). Currently, four Cabinet staff track and manage approximately 1,500 staff-occupied buildings. KYTC needs enhanced tracking and management tools to help decision makers balance agency needs and responsibilities (e.g., adequate salt storage facilities) with available funding, Finance and Administration Cabinet policy, and evolving responsibilities.]]></description>
      <pubDate>Mon, 12 Aug 2024 13:26:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2417067</guid>
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