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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.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>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Uneven skies: long-term global inventory and equity analysis of aviation emissions</title>
      <link>https://trid.trb.org/View/2676418</link>
      <description><![CDATA[Civil aviation is now central to global climate discussions, yet cross-national disparities in emissions and carbon reduction burdens remain underexplored. This study constructs the first harmonized, multi-pollutant global aviation emissions inventory from 1996 to 2023, integrating all scheduled flights worldwide. Systematic comparisons reveal persistent inequalities: while aggregate emissions have surged, especially in emerging economies, per capita emissions remain concentrated in high-income countries. Although efficiency gains from rapid fleet modernization in developing countries are notable, they lag behind soaring demand. Decomposition shows domestic aviation growth is the chief driver in emerging markets, while international flights dominate new emissions in advanced economies. Carbon reduction costs under the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA) indicate mounting burdens for developing countries, highlighting critical challenges for climate justice. Our findings highlight the necessity for differentiated, equity-oriented policies and targeted support to reconcile aviation growth with global decarbonization and fairness goals.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676418</guid>
    </item>
    <item>
      <title>Resilient warehouse supply chains in post-conflict economies: Evidence on prepositioning, multi-echelon inventory, and lateral transshipment</title>
      <link>https://trid.trb.org/View/2646866</link>
      <description><![CDATA[Purpose. This paper synthesizes empirical evidence on how to strengthen warehouse-centred supply chains in post-conflict economies facing damaged infrastructure, intermittent access, and volatile demand. Methodology. A systematic review of 15 protocol-verified empirical and simulation studies is conducted, coding contexts, warehouse interventions, and outcomes such as response time, service level/OTIF, inventory stability, delivery days, and unit logistics costs, with limited random-effects aggregation when comparable metrics are available. Results. Robust effects are identified for warehouse prepositioning, temporary or modular depots, lateral transshipment policies, and multi-echelon inventory control, which collectively reduce response times, increase service levels, and stabilise supply with moderate cost impacts. Digital enablers such as offline-capable warehouse management systems and energy-autonomous facilities further enhance performance under grid and connectivity failures, though quantitative evidence remains sparse. Theoretical contribution. The review integrates the four-R flexibility perspective with the resilience capacities of absorption, adaptation, and recovery, showing how specific warehouse design and control levers operationalise resilience in the recovery phase of humanitarian and essential goods supply chains. Practical implications. The paper proposes a phased implementation roadmap for practitioners in post-conflict settings, distinguishing quick wins in the first 0–3 months, network reconfiguration over 3–12 months, and longer-term investments beyond 12 months to embed digital and energy autonomy in warehouse networks.]]></description>
      <pubDate>Tue, 24 Mar 2026 09:10:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646866</guid>
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    <item>
      <title>Data-Driven Insights into Bridge Deck Condition Dynamics Using Cluster Analysis of National Bridge Inventory Data</title>
      <link>https://trid.trb.org/View/2669382</link>
      <description><![CDATA[Transportation infrastructure is critical to ensuring safety and efficiency. Among its components, bridges play a pivotal role, necessitating effective maintenance and evaluation to preserve their functionality. However, with a vast number of bridges, constrained budgets, and limited resources, state departments of transportation face significant challenges in maintaining their bridge networks. This study analyzes bridge deck deterioration in Colorado using data from the National Bridge Inventory (NBI). Temporal trends in bridge conditions are measured with dynamic time warping, and similar deterioration patterns are grouped through agglomerative clustering. These patterns are associated with structural attributes and environmental conditions identified through a random forest model to highlight key factors such as traffic metrics that influence deterioration. The study identified 14 distinct deterioration patterns using agglomerative clustering, with a random forest model achieving 92% accuracy in predicting cluster assignments. The findings enhance understanding of bridge deterioration dynamics and support targeted, cost-effective maintenance.]]></description>
      <pubDate>Fri, 20 Mar 2026 17:00:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669382</guid>
    </item>
    <item>
      <title>An enhanced dynamic programming approach to the deterministic time-dependent lot-sizing problem under shared warehousing</title>
      <link>https://trid.trb.org/View/2645504</link>
      <description><![CDATA[The lot-sizing problem for one-to-one distribution with time-dependent demand has long been studied in logistics at the operational level. Conventionally, the heuristic-based forward dynamic programming method is usually utilized for the case considering only the trade-off among operational costs, such as transportation and inventory costs. With the onset of e-commerce, many small- and medium-sized e-retailers that have insufficient volume to warrant a dedicated facility are now outsourcing warehousing and logistics services. A single warehouse today may be shared by multiple e-retailers with time-dependent space requirements. Thus, it becomes critical to incorporate the shared warehousing cost, or the rent cost, which was often considered at the tactical level into the lot-sizing problem. This paper deals with the optimal lot-sizing problem in which rent cost and other operational costs are considered jointly. Four necessary conditions for the optimal solution are identified. An enhanced forward dynamic programming approach based on the identified necessary conditions is proposed to solve this multi-objective optimization problem. The performance of the proposed approach is examined for a broad range of cost parameters and demand functions. The results indicate that the proposed approach is promising in terms of computation time and solution quality.]]></description>
      <pubDate>Fri, 20 Mar 2026 08:41:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2645504</guid>
    </item>
    <item>
      <title>An analysis of the ailments in inventory management in the Indian warehouses</title>
      <link>https://trid.trb.org/View/2639332</link>
      <description><![CDATA[Warehousing has been a prominent and efficient tool for developed countries, and it has aided their economy at large with manageable costs and effective control. India, despite being one large country, is yet to set its foot in the warehousing section. Due to an almost absenteeism of warehousing or little warehousing structure India faces highest amount of food, grain, materials wastage. This study concentrates itself on setting up better, efficient, cost-controlled, and result-oriented warehouses and in turn a better economy for the nation. Warehousing provides proficient and hygienic storing facilities of goods to ensure a continuous and timely flow of goods to the market and consumers. It protects perishable and semi-perishable items from deterioration. It maintains and controls the demand and supply chain even for the seasonal commodities. It stabilises prices and helps keep them at an affordable range. The sufferings with the likes of infrastructure, land availability, credit, political interferences, proper segments and their role, taxes, labour, and it is training, etc. have added to the miseries of warehousing. Hence there is sure and need immediate requisite for proper and analytical research which could mend this torn, but important industry.]]></description>
      <pubDate>Thu, 12 Mar 2026 08:49:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2639332</guid>
    </item>
    <item>
      <title>GPT4-Vision Multimodal Model-Powered Query-Answering Chatbot for Bridge PDF Drawings</title>
      <link>https://trid.trb.org/View/2640519</link>
      <description><![CDATA[Managing multiple sets of bridge drawings presents a significant challenge for the state Departments of Transportation (DOTs). For example, state DOTs need to encode each bridge in their jurisdiction according to the new Federal Specifications for the National Bridge Inventory (SNBI). Finding information within these drawings typically involves manual inspection or utilizing search functions within the documents, which is labor-intensive and time-consuming. Introducing a query-answering chatbot as a virtual assistant could significantly help with this information retrieval process. In pursuit of that, in this paper, a Python-based chatbot was created by integrating with the advanced multimodal model, GPT4-Vision, in the proposed system architecture. This program underwent testing using five sets of bridge drawings, comparing its responses against manually retrieved information to assess accuracy. The outcome indicates that the chatbot delivers swift responses with an accuracy of around 58% with information from not only textual data but also graphics.]]></description>
      <pubDate>Fri, 27 Feb 2026 11:00:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640519</guid>
    </item>
    <item>
      <title>A model for relief supply chain optimisation in a multi-product distribution environment</title>
      <link>https://trid.trb.org/View/2624105</link>
      <description><![CDATA[The study investigates a multi-product inventory relief chain where an intermediate distribution centre gets supplies from a collecting centre and caters to the demands of relief centres to emphasise the importance of the relief supply chain in disasters. The relief centres are formed to distribute relief products to affected people in the affected areas. The inventory relief chain system is suggested to provide relief products to affected people at the lowest possible cost. The model optimises the selection of intermediate distribution and relief centres with minimal distribution costs. However, in disaster conditions, every relief centre must receive its requirements. A numerical experiment has been carried out to validate the relevance of the model. A sensitivity analysis of the optimal solution for different parameters is performed to determine how resistant the parameters are to the objective function. The approach supports relief communities in fine-tuning emergency response procedures.]]></description>
      <pubDate>Mon, 23 Feb 2026 11:23:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2624105</guid>
    </item>
    <item>
      <title>Inventing an AI-Informed Risk Index to Prioritize Transportation Infrastructure Preservation</title>
      <link>https://trid.trb.org/View/2672766</link>
      <description><![CDATA[The proposed project seeks to transform national transportation asset datasets into actionable intelligence for preservation planning. Recognizing the fragmentation between roadway and bridge performance data within the Highway Performance Monitoring System (HPMS) and National Bridge Inventory (NBI), the project introduces an artificial intelligence (AI)-driven framework to systematically connect these datasets and develop a unified risk index. The study will first conduct comprehensive literature and data reviews to identify gaps in cross-asset analysis and assess data quality through spatial joins and validation of key attributes such as average daily traffic (ADT). Using descriptive, prescriptive, and predictive analytics, the research will examine relationships among international roughness index (IRI), bridge condition ratings, and traffic loading to uncover deterioration trends and key predictive features. The research will apply advanced machine learning models to forecast performance and support prioritization under budget constraints. The resulting risk index will provide transportation agencies with an objective method to rank preservation needs. This result will enhance Transportation Asset Management Plans (TAMPs) and ensure data-driven resource allocation. Expected outcomes include a validated analytic framework, cross-asset integration methods, predictive deterioration models, and interactive visualization dashboards to aid decision-making. The project directly supports USDOT's strategic goals of economic strength and global competitiveness by improving asset reliability, minimizing disruptions to freight and passenger mobility, and extending infrastructure service life. Educationally, it will train at least one doctoral student in advanced analytics and risk-based asset management. The research will also integrate the methods and results into graduate coursework and research. Technology transfer activities will disseminate results through academic publications, conference presentations, outreach products, and online tools. Stakeholder engagement will ensure practical adoption. Overall, this project aims to deliver a replicable, scalable decision-support tool to strengthen national transportation resilience and investment efficiency.]]></description>
      <pubDate>Sun, 22 Feb 2026 10:44:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672766</guid>
    </item>
    <item>
      <title>An Open Data Approach to Curbside Management [Data Management Plan]</title>
      <link>https://trid.trb.org/View/2662881</link>
      <description><![CDATA[Develop a digital, data-driven curb management ecosystem to enable dynamic curbside management and operations. The ecosystem includes integrations to ensure digital curb inventory records are kept updated, and includes a collection of multi-faceted, open-source application programming interfaces  (APIs) to communicate to the public and curb users, Minneapolis’ policies and regulations, real-time changes to curb usage, and provide a historical view of curb usage, impacts, and efficiencies.]]></description>
      <pubDate>Mon, 09 Feb 2026 08:39:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2662881</guid>
    </item>
    <item>
      <title>Development of Adjustment Factors and Load Ratings via Statistical Analyses of the National Bridge Inventory Database</title>
      <link>https://trid.trb.org/View/2662988</link>
      <description><![CDATA[This study developed a new approach to establish baseline load ratings for bridges in Kansas without plans using data from the National Bridge Inventory (NBI). The approach is comprised of linear regression models to estimate load ratings for bridges with a condition rating of 8 or higher and adjustment factors to lower the estimated load rating to account for bridge condition ratings of 7 or lower. This approach beneficially establishes baseline load rating estimates for structures without prior ratings and secondary load ratings for bridges with prior load ratings to identify outliers and potential errors. The adjustment factors can be used to adjust load ratings obtained by any method to account for bridge condition if the condition was not specifically integrated into the analyses. Both the linear regression models and condition adjustment factors are designed to reflect trends among Kansas bridges within the NBI, not engineering judgment. This approach answers the following question for a given bridge: Knowing nothing more about the structure than what is available within the NBI, what is the expected rating based on similar bridges in similar condition within Kansas? The proposed linear regression models include bridge age, modeled design load, structure kind (construction material), structure type (truss, girder, etc.) and deck width because, among variables reported in the NBI, these were most closely correlated with load rating. The adjustment factors were developed based on the median reported load rating for bridges with various condition ratings, and uncertainty was estimated using a bootstrapping simulation. The proposed models demonstrated satisfactory performance, capturing approximately half the variance observed in the data for the Inventory (R2 = 0.50) and Operating (R² = 0.49) Ratings. Further validation and refinement, inclusion of additional predictors, and exploration of alternative methods are suggested to improve accuracy and applicability.]]></description>
      <pubDate>Thu, 05 Feb 2026 11:56:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2662988</guid>
    </item>
    <item>
      <title>A heuristic algorithm based on beam search and iterated local search for the maritime inventory routing problem</title>
      <link>https://trid.trb.org/View/2641784</link>
      <description><![CDATA[The Maritime Inventory Routing Problem (MIRP) is a challenging optimization problem, and there are still no well-established methodologies capable of efficiently solving large instances or their variants due to its high structural complexity. To address this gap, a heuristic framework is proposed for the deterministic, finite-horizon, single-product MIRP. Unlike most existing heuristic approaches, which depend on mathematical programming models and commercial solvers, the presented method integrates Beam Search (BS) and Iterated Local Search (ILS) within an open-source environment. Its contribution lies not in the individual use of BS or ILS, but in their tailored adaptation and combined design, which captures the interdependence between routing and inventory decisions. The methodology introduces a greedy randomized algorithm for evaluating partial solutions during BS, a problem-specific solution representation, and new neighborhoods designed to explore the MIRP solution space efficiently. To the best of our knowledge, this is the first fully self-contained heuristic approach capable of solving all 72 MIRPLib Group 2 instances, a publicly available benchmark dataset, without relying on mathematical programming. It improves the best-known solutions for 18 instances, achieving an average best gap of −1.85%, and delivers competitive performance on the remaining 54, with an average best gap of 2.72%, all with consistent execution times. These results highlight the potential of the proposed heuristic to address large and complex MIRPs and support practical applications in maritime logistics planning.]]></description>
      <pubDate>Thu, 05 Feb 2026 09:16:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2641784</guid>
    </item>
    <item>
      <title>Optimizing Warehouse Operations with Autonomous Mobile Robots</title>
      <link>https://trid.trb.org/View/2616184</link>
      <description><![CDATA[Autonomous mobile robots (AMRs) can support human pickers in warehouse picking operations by reducing picker walking distance and increasing the warehouse’s throughput. AMR-assisted order picking is becoming popular as it can be conveniently implemented in conventional warehouses. This study proposes an integrated optimization model for scheduling the operations in AMR-assisted picker-to-parts warehouse systems. The model aims to minimize the makespan of all picking operations for a batch of orders by assigning batched orders to AMRs, selecting storage racks for AMRs and pickers to visit, and determining the routes of the AMRs and the pickers. A column- and row-generation algorithm is designed to solve the model using synchronization constraints between AMRs and pickers. Numerical experiments are conducted to validate the applicability of our proposed algorithm in a warehouse that handles 16,000 orders per day. Our algorithm can solve small-scale instances to optimality. Our algorithm can also obtain better solutions in less time than a column generation (CG)–based method. Extensive experiments are conducted to derive managerial insights.]]></description>
      <pubDate>Mon, 02 Feb 2026 09:33:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2616184</guid>
    </item>
    <item>
      <title>Scenario Predict-Then-Optimize for Data-Driven Online Inventory Routing</title>
      <link>https://trid.trb.org/View/2616173</link>
      <description><![CDATA[The real-time joint optimization of inventory replenishment and vehicle routing is essential for cost-efficiently operating one-warehouse, multiple-retailer systems. This is complex because future demand predictions should capture (auto)correlation and lumpy retailer demand, and based upon such predictions, inventory replenishment and vehicle-routing decisions must be taken. Traditionally, such decisions are made by either making distributional assumptions or using machine-learning-based point forecasts. The former approach ignores nonstationary demand patterns, whereas the latter approach provides only a point forecast ignoring the inherent forecast error. Consequently, in practice, service levels often do not meet their targets, and truck fill rates fall short, harming the efficiency and sustainability of daily operations. We propose Scenario Predict-then-Optimize. This fully data-driven approach for online inventory routing consists of two subsequent steps at each real-time decision epoch. The scenario-predict step exploits neural networks—specifically multi-horizon quantile recurrent neural networks—to predict future demand quantiles, upon which we design a scenario sampling approach. The subsequent scenario-optimize step then solves a scenario-based two-stage stochastic programming approximation. Results show that our approach outperforms a classic sequential learning and (stochastic) optimization approach, distributional approaches, empirical sampling methods, residuals-based sample average approximation, and a state-of-the-art integrated learning and (stochastic) optimization approach. We show this on both synthetic data and large-scale real-life data from our industry partner. Our approach is appealing to practitioners. It is fast, does not rely on any distributional assumption, and does not face the burden of single-scenario forecasts. It also outperforms residuals-based scenario generation techniques. We show that it is robust for various demand and cost parameters, enhancing the efficiency and sustainability of daily inventory replenishment and truck-routing decisions. Finally, scenario Predict-then-Optimize is general and can be easily extended to account for other operational constraints, making it a useful tool in practice.]]></description>
      <pubDate>Mon, 02 Feb 2026 09:33:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2616173</guid>
    </item>
    <item>
      <title>Inventory-constrained online learning for revenue management with delayed feedback</title>
      <link>https://trid.trb.org/View/2652541</link>
      <description><![CDATA[Delayed feedback is a prevalent challenge in modern logistics and transportation systems, especially on digital retail platforms. This paper investigates an online learning and pricing problem characterized by aggregated and anonymous delays. In this setting, neither demand nor revenue is immediately observable following a pricing decision; instead, these metrics become available to the retailer only after some stochastic delay. The retailer also faces an initial inventory constraint, creating a complex exploration-exploitation trade-off among learning demand, generating revenue, and managing inventory. To address this challenge, we propose a novel batch-based learning algorithm, referred to as Bandits with Dual Mirror Descent (BUD for short), which integrates mirror descent with bandit control. The algorithm employs a carefully designed batch structure to isolate the impact of delayed feedback, while combining Upper Confidence Bound (UCB) for pricing with dual updates for inventory management. Our theoretical analysis shows that the regret (defined as the revenue gap between the optimal policy and the learning algorithm) of BUD grows sublinearly with the selling horizon and matches the known lower bounds in both bandit with delays and online pricing problems. We conducted numerical experiments to demonstrate that the regret of BUD converges to 0 in various scenarios.]]></description>
      <pubDate>Thu, 29 Jan 2026 17:01:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652541</guid>
    </item>
    <item>
      <title>Platelet inventory management in hospital networks: A reinforcement learning approach</title>
      <link>https://trid.trb.org/View/2655993</link>
      <description><![CDATA[This study proposes a reinforcement learning (RL)-based framework incorporating the Proximal Policy Optimization (PPO) algorithm to improve platelet inventory management. The proposed approach considers an inventory system with varying ordering intervals, incorporating ABO-Rh substitution decisions and hospital collaborations through transshipment. In this framework, transshipment is modeled as a fixed policy, reflecting real-world practices where blood units nearing expiration are proactively transferred from smaller local hospitals to larger hospitals, where they are more likely to be used in time. We extend our analysis by exploring several RL models, including Trust Region Policy Optimization (TRPO) and Soft Actor-Critic (SAC). The results show that PPO-Complete outperforms the other RL models, and all considered RL approaches outperform the base-stock strategy, which is commonly used in hospital platelet inventory management. The analyses indicate that lower transshipment costs, when coupled with effective substitution decisions, lead to a reduction in total cost and enable larger order sizes, thereby mitigating shortages.]]></description>
      <pubDate>Tue, 27 Jan 2026 09:21:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655993</guid>
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