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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.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>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Carbon sinks under carbon trading system: Timing of plant tissue culture technique sharing and optimal mode selection for a manufacturer</title>
      <link>https://trid.trb.org/View/2605019</link>
      <description><![CDATA[With the development of modern agriculture, the plant tissue culture (PTC) technique has been applied by more and more manufacturers. We in this work consider an agricultural supply chain consisting of two manufacturers where one is a regular manufacturer and the other is a manufacturer that owns the plant tissue culture (PTC) technique (PTC manufacturer). The latter may share this technique with the former in either revenue-sharing or pre-product selling modes. The main research questions of this paper are to analyze whether to introduce the PTC technique, and the selection of revenue-sharing mode and pre-product selling mode. First, in the revenue-sharing mode, the optimal profit of the regular manufacturer adopting this technique always increases while the optimal profit of the PTC manufacturer increases (decreases) after sharing this technique at the low (high) unit carbon absorption. In the pre-product selling mode, the regular manufacturer’s optimal profit decreases (increases) after adopting this technique at the low (high) PTC quantity power, while the PTC manufacturer’s optimal profit always increases after sharing this technique. Second, at the low revenue-sharing rate, the optimal profit of the regular (PTC) manufacturer is lower (higher) in the pre-product selling mode than in the revenue-sharing mode. Nevertheless, we interestingly discover that at the high revenue-sharing rate, the optimal profit of the regular manufacturer is higher (lower) in the pre-product selling mode than in the revenue-sharing mode at low (high) unit carbon absorption. In contrast, for the PTC manufacturer, the opposite result holds. In addition, we make several extensions.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2605019</guid>
    </item>
    <item>
      <title>Multiple Effects of Shore Power and Its Berthing-priority Policy on Ship-in-port Performance</title>
      <link>https://trid.trb.org/View/2743224</link>
      <description><![CDATA[This study aims to systematically investigate the multiple effects of Shore Power (SP) deployment and its berthing-priority policy on ship-in-port performance. For this purpose, an integrated Operation-Environment-Policy-Power (OEPP) simulation is developed to embed SP adoption and berthing-priority rules into a discrete-event model of container terminal operations. The framework efficiently links terminal operations, ship emissions, policy interventions, and port electricity demand and is validated using empirical terminal data, expert consultation, scenario analysis, and sensitivity testing. The results indicate that SP adoption can reduce ship-in-port emissions by up to 43% under full utilisation, while berthing-priority policies introduce transitional trade-offs between environmental benefits and operational efficiency. These adverse effects gradually diminish as SP utilisation approaches full adoption. The analysis further reveals that ship-in-port emissions are more sensitive to changes in traffic growth than to increases in SP utilisation. Moreover, large-scale SP deployment significantly increases port electricity demand, with an average consumption of approximately 19.5 MWh per SP-equipped vessel, underscoring the need for coordinated port–power infrastructure planning. These findings provide policy-relevant evidence to support adaptive and phased SP and its berthing-priority policy, highlighting the need to balance emission-reduction objectives with operational efficiency and power-system readiness in port decarbonisation strategies.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2743224</guid>
    </item>
    <item>
      <title>Multi-objective optimization of large cruise ship power plant configuration based on NSGA-Ⅱ algorithm and AHP-entropy weighted TOPSIS-VIKOR</title>
      <link>https://trid.trb.org/View/2679214</link>
      <description><![CDATA[Large cruise ships are characterized by intricate power loads and fluctuating operational conditions, which introduce significant safety hazards, complicate energy efficiency optimization and present challenges in the spatial arrangement of onboard systems. Optimizing the power plant configuration of large cruise ships can enhance system redundancy and reliability, mitigate failure risks, leverage diverse energy sources to improve overall efficiency, and employ modular design principles to reduce system mass and spatial footprint. This study incorporates a multi-faceted objective framework, including economic indicators, the energy efficiency design index, generator set reliability, maximum ship speed, generator set mass, and volume considerations, to comprehensively address the unique operational and structural characteristics of large cruise ships. The Non-dominated Sorting Genetic Algorithm II (NSGA-II), in combination with the Analytic Hierarchy Process (AHP)-entropy weighted TOPSIS-VIKOR method, is employed to ascertain the optimal power plant configuration by comprehensively considering all relevant assessment indicators. The results indicate that operational expenditure and maximum ship speed are the most critical factors influencing configuration optimization. Among all configurations, generator sets powered by Marine Gas Oil (MGO) exhibit the most balanced performance, whereas those utilizing alternative fuels present varying disadvantages—for instance, generators fueled by Liquefied Natural Gas (LNG) are significantly less cost-effective. Under the assumed operating profile and the reference performance parameters adopted in this study, the fuel price sensitivity analysis indicates that the MGO-fueled generator configuration (i.e., 4 × Wärtsilä 16V31 and 3 × Wärtsilä 14V31) yields the best overall performance across the examined fuel price range. In contrast, configurations using alternative fuels exhibit greater sensitivity to price fluctuations, resulting in variations in their configuration rankings. The methodology delineated in this paper enhances the evaluative criteria for cruise ship power plant configuration and provides shipowners with more viable alternatives, customized to the unique operational and structural dynamics of cruise ships. The combination of AHP entropy weight TOPSIS-VIKOR and NSGA-II is first applied to the configuration optimization of large cruise ship power systems to strengthen decision support for selecting an implementable solution.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679214</guid>
    </item>
    <item>
      <title>Timing the Vaccine Flow: An Intelligent Navigator for Strategic Delivery and Coverage</title>
      <link>https://trid.trb.org/View/2737031</link>
      <description><![CDATA[The unprecedented impacts of the COVID-19 outbreak have increased global interest in secure and efficient vaccine distribution. This study investigates the challenges of planning vaccine distribution, including demand fluctuations, limited data availability, and inequitable access to vaccines. It focuses on the allocation of vaccines to mobile and stationary Public Health Units (PHUs) and the management of demand through online registration and real-time updates. To address these challenges, we develop a data-driven Decision Support Framework (DSF) comprising a data-based and a model-based framework. The data-based framework introduces an augmented robust optimization approach that uses dynamic uncertainty sets to adapt to changing conditions. Two data generation algorithms, integrated with predictive analytics, enhance limited data, improve out-of-sample performance, and increase the explainability of robust solutions. These uncertainty sets are fed into the model-based framework, which integrates three mathematical models to dynamically configure PHUs and optimize the vaccination distribution plan. As a conceptual contribution, a new policy is proposed to maximize coverage through a Dynamic PHU Allocation (DPA) algorithm that updates mobile PHU locations. Additionally, an assignment model reallocates surplus inventory to address shortages and ensures proper storage for future use. The numerical results demonstrate that the proposed DSF enables adaptive decision-making, effectively coping with fluctuations in vaccination demand and reducing vaccination shortages by 80% compared to other approaches. The adaptability of the DSF, combined with the role of mobile PHUs in managing demand fluctuations and ensuring equitable access, offers insights for resource allocation and logistical planning in uncertain scenarios.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2737031</guid>
    </item>
    <item>
      <title>Effective global maritime supply: Determinants, dynamics, and economic impacts</title>
      <link>https://trid.trb.org/View/2762518</link>
      <description><![CDATA[Maritime transportation handles over 80% of global trade volume. Recent events such as the COVID-19 pandemic and the Red Sea crisis have demonstrated its vulnerability to sudden disruptions. It is therefore critical to monitor the state of the global supply chain. To this end, we construct a novel weekly Maritime Supply Index (MSI) using high-frequency Automatic Identification System (AIS) data from the global container fleet (2017–2024) to provide a real-time, comprehensive measure of effective shipping service provision in ton-miles. The MSI captures multiple operational dimensions that reflect both dynamic capacity management by shipping companies, including vessel speed adjustments, capacity non-utilization (idling), strategic rerouting, and exogenous constraints from port and chokepoint congestion (e.g., Suez and Panama Canals).By decomposing weekly changes in the index, we reveal that vessel speed adjustments are the primary operational lever driving short-term supply fluctuations, underscoring the importance of carrier-side decision-making in capacity balancing. The MSI serves as an accurate diagnostic tool, reflecting the supply dynamics during major disruptions like the COVID-19 pandemic and the Red Sea crisis. Finally, we disentangle shipping demand and supply shocks through time-series causality analysis, evaluating their complex interactions and inflationary effects.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2762518</guid>
    </item>
    <item>
      <title>The impacts of carbon quota allocation methods, decision-making modes, and shippers’ low-carbon preferences on the maritime supply chain</title>
      <link>https://trid.trb.org/View/2752717</link>
      <description><![CDATA[This study investigates carbon emission reduction in a maritime supply chain (MSC) consisting of a port, a shipping company, and shippers with low-carbon preferences. It considers two carbon quota allocation methods, namely Historical Emission-based Allocation (HEBA) and Benchmark-based Allocation (BBA), and two decision-making modes, namely decentralized decision-making and centralized decision-making. Under decentralized decision-making, the port first determines the unit handling fee and the carbon-reduction investment level, and the shipping company then determines the freight rate and participates in carbon trading. Under centralized decision-making, these decisions are jointly optimized to maximize MSC total profit. Together, the two allocation methods and two decision-making modes yield four scenarios. For each scenario, we formulate the corresponding analytical models, derive the optimal solutions, and compare the optimal decisions and equilibrium outcomes. Three key findings emerge. First, within the model setting, centralized decision-making yields a higher carbon-reduction investment level and higher MSC total profit than decentralized decision-making. Second, in the scenarios examined, BBA yields a higher carbon-reduction investment level and larger shipment demand than HEBA, although its relative advantage is conditional rather than universal. Third, within the model setting, stronger low-carbon preferences among shippers are associated with a higher carbon-reduction investment level and higher MSC total profit, suggesting a potential environmental–economic win–win relationship.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752717</guid>
    </item>
    <item>
      <title>Closed-loop supply chain model with third-party logistics-based return management system</title>
      <link>https://trid.trb.org/View/2728199</link>
      <description><![CDATA[The rapid growth of e-commerce has led to a significant increase in return volumes, highlighting the importance of the closed-loop supply chains (CLSCs). Handling returned items has become a critical challenge, as companies face uncertainty in return quantities, product conditions, and fluctuating demand in secondary markets. In this study, we develop a two-stage stochastic programming model for CLSCs that incorporates a third-party return management system (3PL-RMS). The 3PL-RMS handles returned items through multiple recovery options, including reuse, secondary-market resale, remanufacturing by suppliers, and disposal. The model captures the complexity introduced by reverse flows and integrates uncertainties related to both returns and secondary-market demand. To solve the model efficiently, we employ the sample average approximation method and a STabilization and Root-cut based Benders Decomposition (STaR-BD) algorithm. Computational experiments show that STaR-BD reduces computation time by approximately 85% compared with traditional Benders decomposition while maintaining solution quality. A case study using realistic e-commerce data shows that integrating a third-party return management system reduces total costs by up to 32% compared with a conventional supply chain model, with savings increasing at higher return rates.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2728199</guid>
    </item>
    <item>
      <title>Risk Mitigation in Perishable Supply Chains: An AI- Driven Approach for Managing Meat Distribution</title>
      <link>https://trid.trb.org/View/2743186</link>
      <description><![CDATA[The meat supply chain (MSC) faces significant challenges due to the perishability of products, leading to substantial economic and environmental costs. Traditional monitoring and logistics methods often fail to provide real-time predictive insights needed to mitigate spoilage risks. In this study, we propose an artificial intelligence (AI) -driven framework integrating fine-tuned vision-language models (VLMs) – specifically NVILA and Qwen Vision Language (Qwen-VL) – with a robust optimization model for improving freshness prediction and logistics management in MSCs. Using the Meat Freshness Image Dataset, we fine-tune VLMs to classify meat into freshness categories and employ these predictions within a distributionally robust optimization (DRO) model to manage storage, transportation, and spoilage decisions under uncertainty. Our framework leverages Wasserstein-based DRO to model uncertainties in freshness, demand, and transportation times, leading to improved cost efficiency, service levels, and supply chain resilience. Numerical experiments demonstrate that our approach reduces worst-case costs and spoilage compared to nominal optimization methods, offering a scalable and effective solution for perishable supply chain management.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2743186</guid>
    </item>
    <item>
      <title>Beggar or enrich-thy-neighbor? Impact of platform cross-border acquisition</title>
      <link>https://trid.trb.org/View/2742980</link>
      <description><![CDATA[E-commerce platforms have increasingly pursued cross-border acquisitions in recent years, targeting either foreign online platforms or foreign brick-and-mortar retailers. Motivated by this practice, this paper investigates how the choice of acquisition target reshapes global supply chain dynamics. We develop a game-theoretic model that captures the data-driven network effect associated with acquiring foreign platforms and the in-store service investment and resulting showrooming effect from acquiring foreign retailers. Several findings emerge. First, both acquisition strategies are double-edged swords for the upstream manufacturer. Acquiring a foreign retailer raises the manufacturer’s profit but heightens its exposure to tariff increases, whereas acquiring a foreign platform may reduce its profit but mitigates the adverse effects of tariff increases. Second, the two strategies generate contrasting channel spillovers. Acquiring a foreign platform constitutes a “beggar-thy-neighbor” strategy that benefits acquisition-related channels at the expense of non-acquired channels, whereas acquiring a foreign retailer may serve as an “enrich-thy-neighbor” strategy. Third, provided that the acquisition price is appropriate, either strategy can improve social welfare in both the domestic and foreign countries. Finally, although a stronger showrooming effect may seem to favor the foreign platform, it does not necessarily do so. These findings offer managers a framework for evaluating cross-border acquisition targets and provide policymakers with a welfare-based lens for assessing and regulating such acquisitions.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742980</guid>
    </item>
    <item>
      <title>E-Commerce Middle-Mile Network Design with Delivery Speed Choices and Service Level Constraints</title>
      <link>https://trid.trb.org/View/2709159</link>
      <description><![CDATA[The increasing demand for expedited e-commerce deliveries, with delivery times of one to three days, highlights the importance of optimizing the middle-mile network. Most retailers store a considerable portion of their inventory at the regional distribution centers (RDCs) outside urban areas, from where it is moved to the customer zones equipped with last-mile distribution facilities as required. Thus, RDC locations become critical in middle-mile operations, directly impacting the transit times to customer zones and, ultimately, the delivery times in the last mile. This paper presents a middle-mile network design problem arising in the context of e-commerce companies in the presence of customers with different delivery time preferences. Specifically, it allows RDCs to satisfy demands from customer zones using delivery times longer than requested, albeit with penalties, if that helps reduce cost without violating the service level requirements of fulfilling at least a given threshold of the demands within the requested delivery times. The problem is formulated as a mixed-integer linear program, for which an exact Lagrangian relaxation-based branch-and-bound algorithm is proposed. Several enhancements to the algorithm are provided, including an efficient Lagrangian heuristic for the primal-bound, a Benders decomposition framework to solve one of the Lagrangian subproblems efficiently, an analytical approach for obtaining Benders optimality cuts, and a partial analytical characterization of Pareto-optimal Benders cuts. With these enhancements, our final algorithm substantially outperforms the state-of-the-art commercial solver, as highlighted by our computational experiments on an extensive set of 220 instances with up to 80 potential RDC locations and 1,000 customer zones. Our best algorithm solves 204 of the 220 instances to 0.50% duality gap compared with only 108 that CPLEX could solve to the same gap within an allowed 10-hour CPU time limit. Furthermore, it achieves an average time savings of 63.24% compared with CPLEX across all the instances.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709159</guid>
    </item>
    <item>
      <title>Green efficiency deviation in ports: an empirical analysis of scale-greenness trade-offs in Zhejiang, China</title>
      <link>https://trid.trb.org/View/2709504</link>
      <description><![CDATA[As global ports expand, balancing scale with green development grows crucial. While studies on conventional (CPE) and green port efficiency (GPE) is extensive, research on their efficiency deviation (ED) remains limited. Utilizing data from the 𝘡𝘩𝘦𝘫𝘪𝘢𝘯𝘨 𝘗𝘳𝘰𝘷𝘪𝘯𝘤𝘦 𝘗𝘰𝘳𝘵 𝘌𝘯𝘦𝘳𝘨𝘺 𝘙𝘦𝘱𝘰𝘳𝘵𝘪𝘯𝘨 𝘚𝘺𝘴𝘵𝘦𝘮 for 2019–2024, this study evaluated CPE, GPE and the ED in Zhejiang ports using the Super-SBM model, and examines influencing factors through a multilevel regression model. Results indicate that cargo throughput influences ED through mediating and moderating mechanisms, with no direct effect. Cargo throughput’s impact on ED is realized through CPE; it indirectly increases ED by boosting CPE, and port location moderates the relationship between cargo throughput and ED. Large hub ports achieve higher CPE through technological collaboration, regulation-driven initiatives, and efficiency reinvestment, thereby accelerating GPE improvement. These findings offer valuable insights for port managers seeking to balance scale expansion with low-carbon transformation.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709504</guid>
    </item>
    <item>
      <title>Adaptive large neighborhood search for the two-echelon location routing problem with simultaneous pickup and delivery and parcel lockers</title>
      <link>https://trid.trb.org/View/2709501</link>
      <description><![CDATA[Last-mile delivery in urban areas faces challenges due to traffic congestion and difficult access, leading to increased costs. To address these issues, the literature has explored alternative strategies such as parcel lockers and network optimization. This research enhances the Two-Echelon Location Routing Problem with Simultaneous Pickup and Delivery (2E-LRPSPD) by integrating parcel lockers into the new 2E-LRPSPD-PL model. In this model the first echelon includes a single depot, open satellites, and parcel lockers, while the second echelon features routes from satellites to home service and parcel locker customers. We propose an Adaptive Large Neighborhood Search (ALNS) heuristic and compare its performance with the CPLEX solver. Results show that ALNS consistently finds high-quality solutions more quickly than CPLEX. Most importantly, integrating parcel lockers results in a significant reduction in total travel distance, up to 64%, demonstrating the effectiveness of this approach in improving operational efficiency and customer convenience in urban logistics.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709501</guid>
    </item>
    <item>
      <title>An incentive- and policy-responsive model for formal-channel entry of retired EV batteries: A macro-micro framework with normalized reverse logistics scale</title>
      <link>https://trid.trb.org/View/2709886</link>
      <description><![CDATA[This study develops a policy-planning and budgeting framework for the formal-channel entry of retired electric-vehicle batteries into certified and traceable channels. The framework links consumer-facing incentives and bottleneck-normalized certified-network scale to formalization performance. At the macro level, a logistic-Cobb-Douglas policy aggregator provides a bounded and invertible mapping from subsidy intensity and certified-network scale to the formalization rate. At the micro level, a transparent linear transportation baseline quantifies certified-entry flows, transport-related costs, node-level throughput, and capacity bottlenecks under a certified chain-of-custody setting. The framework is designed for formalization-target setting and minimum-subsidy calculation rather than downstream pathway allocation; second-life repurposing and material recycling are treated as regulated post-entry routes, not endogenous choices. Calibrated to Shanghai and supported by robustness checks in Xi'an and Guangzhou, the framework allows planners to invert policy targets and compute the minimum subsidy required for a desired formalization rate at a given certified-network scale. Results show substantial fiscal asymmetry. Accelerating infrastructure expansion raises total subsidies from 10.313 to 11.398 billion CNY while improving formalization by less than one percentage point, from 98.67% to 99.41%. By contrast, reducing consumer subsidies from 0.15 to 0.05 after network maturity lowers formalization by only about 5% while cutting total public expenditure by about 52%. These findings suggest that phased subsidies combined with targeted bottleneck-relieving investment outperform persistent high subsidies and undifferentiated network expansion. More broadly, the framework should be interpreted as an auditable and invertible planning tool for formal-channel-entry governance and budgeting under certified-network constraints.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709886</guid>
    </item>
    <item>
      <title>Artificial Intelligence-Enabled Generative Design for Additive Manufacturing Inventory Pooling: An Economic and Environmental Sustainability Analysis in Aviation Spare Parts Supply Chain</title>
      <link>https://trid.trb.org/View/2709788</link>
      <description><![CDATA[The potential for artificial intelligence (AI)-enabled generative design (GD) algorithms for additive manufacturing (AM) is vast. By employing GD, parts are optimized for material efficiency, structural performance, longevity, and reduced waste, aligning with circular economy principles. This study provides a quantitative comparison of the cost and environmental impacts of conventional manufacturing (CM) and generatively designed additive manufacturing (GDAM) within the context of centralized spare-parts supply chain (SPSC) inventory pooling in the aviation industry. We analyze two scenarios and estimate the total annual and lifecycle costs of each using a cost model that integrates established inventory management models. Our sensitivity analysis shows that, assuming equal part longevity, the GDAM scenario becomes cost-competitive when the weight reduction exceeds approximately 84% of the CM part’s weight. We also discovered that part durability is a more influential driver for the GDAM scenario than lightweighting. In the GDAM scenario, we observed that when the part is 53% lighter than the CM part, if the GDAM part can also deliver a 100% increase in longevity compared to the CM part, then the result is a 24.6% cost advantage for the GDAM scenario relative to the CM scenario. Moreover, the GDAM scenario is shown to offer substantial sustainability advantages, including reductions in fuel consumption, CO₂ emissions and raw material use. These environmental benefits become increasingly evident under high weight-reduction scenarios. This paper contributes to the literature by integrating GDAM into SPSC cost analysis. We propose a cost model for GDAM utilization in a centralized SPSC pool in the airline industry and compare its cost components, using a jet-engine bracket case study, with those of a CM-based centralized SPSC pool. We also provide insights into when GDAM production-capacity pooling can be viable, particularly for non-critical-to-flight parts. These findings inform strategic decisions for supply chain managers and policymakers by quantifying the economic and environmental trade-offs associated with adopting GDAM-based inventory pooling in aviation.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709788</guid>
    </item>
    <item>
      <title>Characterization of Passenger Search Time Under Varying Supply-Demand Imbalance</title>
      <link>https://trid.trb.org/View/2696090</link>
      <description><![CDATA[Passenger search time is an essential variable in a street-hail taxi operation. Fundamentally, the statistical distributions of passenger search time form the basis of analytical and simulation models for describing the taxi system’s dynamics. To create accurate analytical and simulation models that realistically represent the dynamics of the actual taxi system, it is inevitable to characterize the type of statistical distributions that can effectively model the empirical distributions. While the probability distribution functions of passenger search time are critical elements of analytical and simulation models, their empirical distributions under different levels of supply-demand imbalance have not been characterized in any existing studies. In this paper, based on more than eight million real taxi trips in Bangkok, Thailand, and formal statistical analysis, we identify the probability distributions that can effectively model the empirical distributions of passenger search time under different levels of taxi supply-demand imbalance.]]></description>
      <pubDate>Fri, 28 Aug 2026 08:35:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696090</guid>
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