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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>
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      <link>https://trid.trb.org/</link>
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    <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>Joint vehicle clustering and dynamic power allocation optimization in sectorized 6G networks for V2X communication</title>
      <link>https://trid.trb.org/View/2618047</link>
      <description><![CDATA[Vehicle-to-Everything (V2X) communication is essential for developing fully autonomous vehicles, but it raises significant challenges due to high data rate demands and energy consumption in dense networks. This paper proposes a novel joint optimization framework integrating vehicle clustering and power allocation in sectorized 6G networks with beamforming. The framework uses a k-medoids-based clustering algorithm and a dynamic power allocation scheme to reduce interference and minimize power consumption while meeting Service Level Agreement (SLA) requirements. Our results demonstrate that the proposed framework improves SLA compliance by up to 98.7% under highly dense and variable traffic conditions compared to non-clustered networks. Furthermore, dynamic power allocation reduces communication power consumption by 69%, and Remote Radio Head (RRH) on/off switching decreases overall system power by 3.7%. This approach significantly enhances network capacity and energy efficiency, making it a promising solution for sustainable V2X communications in future autonomous vehicle networks.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2618047</guid>
    </item>
    <item>
      <title>A Gated Transformer MADDPG Algorithm for Latency and Energy Aware Task Offloading in Digital Twinning Aerial Edge Computing</title>
      <link>https://trid.trb.org/View/2659615</link>
      <description><![CDATA[Unmanned aerial vehicles (UAVs) have seen breakthroughs in forming Aerial Edge Computing (AEC), which executes computationally intensive tasks generated by Internet of Things (IoT) devices, thanks to their ease of deployment, especially in scenarios where traditional terrestrial base stations are damaged and unable to process tasks due to natural disasters. However, an AEC faces significant challenges due to the limited battery capacity of UAVs and the need for efficient collaboration among them to execute tasks. Existing studies often overlook fine-grained task prioritization and balanced load distribution across UAVs, leading to inefficiencies in energy usage and service delay. In this paper, we have developed an optimization framework for efficiently offloading computationally intensive IoT tasks in a three-stage Digital Twin-enabled multi-UAV-based AEC network environment, which jointly minimizes service latency and energy consumption while ensuring the expected load distribution among the UAVs. The formulated framework is a Mixed-Integer Nonlinear Programming (MINLP) problem, which is inherently NP-hard. To address this, we design GLEMATO, a scalable GTrXL-assisted MADDPG framework that learns high-quality offloading policies through memory-aware task prioritization and cooperative multi-agent decision-making in dynamic AEC scenarios. In GLEMATO, while the GTrXL model ensures adaptive task prioritization by considering factors such as task generation time, energy budget, and application deadlines, while the MADDPG enables decentralized policy learning through sharing cooperative state–actions among UAVs. The experimental results, carried out on the OpenAI Gym simulator platform, demonstrate that the developed GLEMATO framework reduces average energy consumption and service latency by 21.8% and 23.3%, respectively, and increases the average task completion ratio by up to 20.1% for computationally intensive tasks compared to the state-of-the-art approaches.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659615</guid>
    </item>
    <item>
      <title>Deep Reinforcement Learning for Multi-Objective Latency Minimization in 5G Heterogeneous Vehicular Networks</title>
      <link>https://trid.trb.org/View/2762144</link>
      <description><![CDATA[Modern intelligent transportation systems rely fundamentally on vehicular networks capable of ultra-responsive communication, a capability brought into reach by 5G and beyond technologies for applications such as autonomous driving. A persistent obstacle within these fast-changing, mixed-technology networks is the dual and often competing requirement to slash both data transmission delays and power usage, a multi-faceted optimization that existing solutions, focused on singular objectives, fail to address adequately.Confronting this trade-off, we present SONG-PO-DRL, a new hybrid framework that fuses a swarm-optimized non-dominated sorting genetic algorithm with deep reinforcement learning to drive adaptive, multi-criteria decision-making. Evaluation with seven algorithms as baselines, using simulation implemented with OMNET++, Simu5G, Veins, and SUMO, shows that this approach surpasses current state-of-the-art methods in critical areas such as latency and competitively; energy consumption, cluster head longevity, congestion levels, and processing overhead. By providing a foundational design strategy, this framework enables the development of higher-performance, more stable, and scalable vehicular networks, thereby contributing directly to the evolution of dependable and energy-conscious future transportation infrastructures.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2762144</guid>
    </item>
    <item>
      <title>Physics-informed multi-agent deep reinforcement learning for dynamic emergency resource scheduling in marine oil spills</title>
      <link>https://trid.trb.org/View/2742978</link>
      <description><![CDATA[An efficient marine oil spill response is essential for mitigating environmental impacts. However, dynamic ocean conditions and uncertain spill evolution make timely and flexible emergency resource scheduling challenging. This study proposes a physics-informed multi-agent deep reinforcement learning framework for maritime oil spill response. The framework integrates oil evolution processes and environmental dynamics into the decision-making environment, enabling agents to adapt scheduling strategies under changing spill conditions. A multi-objective reward mechanism is developed to balance recovery performance, response time, and resource utilization. Simulation experiments demonstrate that the proposed MADDPG-based method consistently outperforms benchmark approaches across multiple objectives. Furthermore, the framework is validated using a real-world Bohai Sea oil spill scenario, demonstrating its applicability under realistic marine conditions. Multi-seed experiments show stable performance with a low coefficient of variation of 2.7% for the Overall Performance Index, confirming the robustness and reliability of the proposed approach for intelligent oil spill emergency response.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742978</guid>
    </item>
    <item>
      <title>Data Center Impacts on Airports</title>
      <link>https://trid.trb.org/View/2772539</link>
      <description><![CDATA[Airports are experiencing increasing electrical demand from airport electrification, artificial intelligence (AI)-enabled operations, and future electric aircraft while also facing growing interest in data center development on or near airport property. Data center development may affect airport electrical capacity, utility planning, land use, water use, environmental considerations, and airport operations as a result of cooling-system plumes. Airport operators are increasingly being asked to evaluate proposed data center developments but have limited guidance to help assess potential impacts or respond to developers and local governments. Existing research addresses some aspects of competing utility demands, but airports continue to seek practical information on the broader impacts of data centers.

The objective of this research is to develop planning frameworks and guidance that help airports understand and evaluate the impacts of data center development while coordinating with utilities, regulators, developers, and other stakeholders. The project will begin with a First Look to provide timely information for airport operators on issues that should be considered when evaluating proposed data center developments. Based on information gathered during the First Look process, the project will identify potential follow-on research needed to address technical issues associated with data centers and airport operations, including planning, utility coordination, operational impacts, potential mitigation measures, and legal considerations.]]></description>
      <pubDate>Thu, 03 Sep 2026 08:32:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2772539</guid>
    </item>
    <item>
      <title>Multi-Agent Deep Reinforcement Learning for Joint Optimization of Latency and Energy Consumption Oriented to Communication-Sensing-Computation Integration in IoV</title>
      <link>https://trid.trb.org/View/2761387</link>
      <description><![CDATA[The rapid development of the Internet of Vehicles (IoV) has driven the need for efficient Integration of Communication, Sensing, and Computation (ICSC) to support advanced applications such as autonomous driving and real-time traffic management. However, the dynamic and resource-constrained nature of IoV environment poses significant challenges in achieving optimal performance, particularly in balancing multiple optimization objectives such as latency and energy consumption. Traditional optimization methods, such as Genetic Algorithms (GA), often struggle with inefficiency and unpredictability in dynamic scenarios. In this paper, we first propose an IoV Communication-Sensing-Computation Integration architecture to address the problem that the traditional IoV architectures cannot meet the needs of the ICSC. Then, we formulate the joint optimization of latency and energy consumption in ICSC as a constrained Multi-objective Optimization Problem (MOP). We employ Multi-Agent Deep Reinforcement Learning (MADRL) with a transformer module, combined with a weighted decomposition method and a neighborhood parameter transfer technique, to solve the problem. Simulation results show that our approach outperforms traditional methods in terms of convergence and diversity, and has a lower running time.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761387</guid>
    </item>
    <item>
      <title>Cross-sectoral synergies for household energy savings: the role of electric vehicles, solar photovoltaics, and remote work</title>
      <link>https://trid.trb.org/View/2709499</link>
      <description><![CDATA[Over the past two decades, new technologies and behavioral shifts – such as electric vehicles, solar photovoltaics, and increased work-from-home practices – have reshaped residential electricity consumption and cost. However, their combined or synergistic impact on a household’s electricity cost remains unexplored. Leveraging data from the 2020 Residential Energy Consumption Survey, this study uses a structural equation model to unravel the extent to which the bundled adoption of EV-PV and stay-at-home decisions impact the total electricity cost of households. Results indicate that adopting both EVs and PV reduces electricity costs by 31% despite a 16% rise in consumption. When combined with stay-at-home practices, households still experience a 13% cost reduction, even with a 25% increase in electricity consumption. These findings suggest that financial savings are not merely a byproduct of adopting new technologies or behavioral changes but could serve as a key consideration for household contemplating engagement with multiple modern energy solutions simultaneously.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709499</guid>
    </item>
    <item>
      <title>Transportation carbon emission reduction channel: The role of new energy vehicle promoting application policies in urban energy structure substitution</title>
      <link>https://trid.trb.org/View/2711662</link>
      <description><![CDATA[The promotion of new energy vehicles (NEVs) is expected to facilitate the development of a low-carbon urban transportation system and accelerate the transformation of regional energy structures. Based on panel data from 278 cities in China, this study is the first to empirically examine regional and urban energy structure substitution (ESS) effects and underlying mechanisms, using the New Energy Vehicle Promotion Policy (NEVP) as a quasi-natural experiment. The results indicate that NEVP significantly promotes ESS, with an average effect of 5.7%. This policy impact operates through several potential channels, including the reinforcing role of green technological innovation, the constraining effect of resource dependence, and the scale effect of industrial development. After accounting for the interactive effects of subsidy reductions, the energy transition exhibits noticeable short-term fluctuations, while its substitution effect on fossil energy remains prominent in the long run. Furthermore, heterogeneous analysis reveals that NEVP is more effective in promoting the green transformation of energy structures in northern cities, non-old industrial base cities, cities with high transportation pressure, and cities with lower levels of attention to energy transition.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2711662</guid>
    </item>
    <item>
      <title>Energy Minimization in UAV-Enabled Cargo Pickup Systems: A Radio Map-Aided Hierarchical Optimization Framework</title>
      <link>https://trid.trb.org/View/2685905</link>
      <description><![CDATA[This article studies the energy efficiency optimization of cargo uncrewed aerial vehicle (UAV) pickup systems, with constraints on on-board energy and load capacity. In the UAV-enabled cargo pickup system, minimizing the total energy consumption and ensuring the safe flight of the cargo UAV is a problem to be solved. However, due to building blockages, the channel between the UAV and ground base stations (GBSs) frequently switches between line-of-sight (LoS) and non-line-of-sight (NLoS), thereby affecting the UAV’s communication quality. This effect is further aggravated by environmental noise interference. Moreover, limited by the on-board energy, it is unrealistic for the UAV to pick up all the cargo in a single flight without charging or replacing the battery. To address the above-mentioned challenges, we propose a UAV pickup system energy efficiency optimization (UPSEEO) framework. In this framework, the UAV’s trajectory between any two pickup points is optimized via the A ${}^{*}$  algorithm to ensure the stability of the UAV communication link. Next, we employ the particle swarm optimization (PSO) algorithm to optimize both task allocation and flight speed to minimize the total energy consumption, subject to constraints on UAV on-board energy limits and payload capacity. Numerical results show that the proposed framework can ensure the UAV’s communication quality in any spatial topology, with an improvement in energy efficiency of approximately 5% to 50% compared to the comparison experiment.]]></description>
      <pubDate>Fri, 28 Aug 2026 16:25:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685905</guid>
    </item>
    <item>
      <title>Integrating Autonomous Last-mile Deliveries with Public Transit</title>
      <link>https://trid.trb.org/View/2731059</link>
      <description><![CDATA[Lately, research has shown the potential of automation in freight transport by reducing energy consumption, emissions, noise, and operating costs—often focused exclusively on either freight (e.g., robots and drones) or passenger (e.g., automobiles and buses) transport. Hence, this research investigates the potential of technological advancements in self-driving and connected vehicles for better coordination between freight and passenger vehicles. To achieve the objective of integrating robotic last-mile deliveries with autonomous minibuses, Chalmers University in Sweden has been chosen as a case study. Pilot tests were conducted to measure the travel, handling, boarding and alighting times, and energy consumption for robots and the minibus. Freight and passenger demand were obtained from the historical daily demand data. Several scenarios for routing and bus stop locations were examined for the energy efficiency of freight deliveries via electric pickup trucks. The minibus required around 34 daily trips, operating two to three buses during peak times. The results demonstrated that robots exhibited superior energy efficiency at lower demand levels, while integrating with minibuses led to a further reduction in energy use by 20-50%. Two-way minibus movement maximised the overlapping of routes between the robot and the minibus, reducing passenger travel time; however, with an increase in the bus kilometres travelled. To reap maximum benefits of integration, it is vital to prepare an optimal plan considering the demand, vehicle size, routing, and bus stops to overlap with freight destinations.]]></description>
      <pubDate>Fri, 28 Aug 2026 14:41:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2731059</guid>
    </item>
    <item>
      <title>Joint Deployment, Association and Power Optimization for UAV-Mounted Star-RIS-Assisted Maritime Communications</title>
      <link>https://trid.trb.org/View/2761424</link>
      <description><![CDATA[With the rapid development of maritime activities, the demand for an efficient maritime communication system has grown significantly. Due to the lack of fixed infrastructure of maritime environments, the uncrewed aerial vehicle-mounted simultaneously transmitting and reflecting reconfigurable intelligent surface (USTAR) can provide flexible and efficient services for maritime communications, by integrating the advantages of both technologies. In this work, we explore the deployment of multiple USTARs to enhance uplink communications from maritime users (MUs) to a terrestrial base station (TBS). Specifically, a joint deployment, association, and power multi-objective optimization problem (JDAPMOP), which involves three optimization objectives, i.e., maximizing the system capacity, minimizing the total flight energy consumption of the USTAR system, and minimizing the total transmit power of all MUs, is formulated. Since JDAPMOP is an NP-hard and non-convex mixed integer non-linear programming problem (MINLP), we propose an enhanced non-dominated sorted genetic algorithm-III with Gaussian map initialization, inverse denoising iteration, and Pareto front purification based on convex optimization (ENSGA-GIC) to solve the problem. Simulation results demonstrate that the proposed ENSGA-GIC performs better than other benchmarks.]]></description>
      <pubDate>Fri, 28 Aug 2026 13:33:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761424</guid>
    </item>
    <item>
      <title>Joint Optimization of Berth Allocation and Ship Speed Considering Port Group Transshipment Rationalization</title>
      <link>https://trid.trb.org/View/2685887</link>
      <description><![CDATA[To address the increasing demand for efficient and sustainable port operations amid the complex dynamics of global shipping, this study investigates the coordinated scheduling problem of berth allocation and shipping speed within port groups under the transshipment rationalization strategies (BSCS-TRS). A multi-objective mixed-integer linear programming (MILP) model is proposed for the BSCS-TRS. A multi-objective discrete combinatorial optimization algorithm based on decomposition and adaptive large neighborhood search (MODA/D-ALNS) is proposed to solve the problem, where a shortest path-based ship speeds optimization method (SPSOM) is developed to optimize the shipping speed. The performance of the proposed model and MODA/D-ALNS is systematically through comparative numerical instances and case studies. The results indicate that the proposed model can optimally solve small-scale instances of BSCS-TRS using Gurobi, while MODA/D-ALNS exhibits good performance in benchmark knapsack problems and BSCS-TRS. Sensitivity analysis results indicate that the proposed method can reduce ship waiting time by 79.06% and fuel consumption by 2.25% within port groups. These findings provide a scalable decision-making tool for port operators to balance operational efficiency with environmental sustainability.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:33:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685887</guid>
    </item>
    <item>
      <title>Dynamic Speed Optimization and Berth Reallocation for Autonomous Vessels Under Sailing Time Disturbances</title>
      <link>https://trid.trb.org/View/2685879</link>
      <description><![CDATA[Autonomous vessels (AVs) have attracted growing attention due to their potential advantages in operational efficiency and navigational safety. However, their voyages may be affected by stochastic disturbances, which can lead to delayed arrivals at ports and the unavailability of pre-assigned berths. This paper first proposes a dynamic optimization approach for AV speed optimization and berth reallocation to mitigate the impacts of stochastic disturbances. Specifically, the sailing speeds of AVs are dynamically adjusted if stochastic disturbances affect their expected arrival times. Meanwhile, the real-time berth reallocation for AVs is performed when their originally allocated berths become unavailable. To meet real-time operational requirements, a rolling horizon framework is employed, which supports dynamic and adaptive adjustments to sailing speeds and berth reallocation based on the latest information on stochastic disturbances and berth occupancy. In each decision period, the problem is formulated as a mixed integer nonlinear programming model to minimize the total cost. To solve the proposed model efficiently, a tailored branch-and-cut algorithm incorporating an outer approximation method is developed. To evaluate the performance and effectiveness of the proposed model and solution method, extensive numerical experiments based on the operational data of a maritime logistics company were conducted. The results demonstrate that the proposed algorithm significantly outperforms both the Gurobi solver and a “first-come-first-served” greedy algorithm in terms of solution quality. Sensitivity analyses revealed that greater sailing time disturbances and lower penalty costs for arrival delays tend to reduce the punctuality of AVs at ports. Moreover, higher fuel prices prompt AVs to adopt lower sailing speeds to reduce energy costs.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:33:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685879</guid>
    </item>
    <item>
      <title>How Assisted Driving-Based Intelligent Transportation Systems Promote Sustainability: Insights From Penetration-Aware Experiments</title>
      <link>https://trid.trb.org/View/2685863</link>
      <description><![CDATA[In the assisted driving-based Intelligent Transportation System (ADITS) featuring speed-guided functionality, intelligent vehicles are crucial for enhancing transportation sustainability. However, the impact of ADITS on transportation sustainability at different penetration rates remains unclear. This study combines road testing with simulation to address this gap. By integrating road test data, traffic survey data, ADITS algorithms, and Monte Carlo uncertainty analysis, the study validates the simulation results’ reliability. Correction coefficients are provided to refine energy consumption and carbon emissions simulations. The findings indicate that the differences between simulation data and on-road test data are minimal. However, under varying penetration rate environments, the differences in most variables are statistically significant. At 100% penetration, the adjusted energy use in congested traffic stands at 1.05 MJ/km, with carbon emissions of 81.6 g/km. In uncongested scenarios, these values drop to 0.65 MJ/km and 55.4 g/km, respectively. With the increasing penetration rate, energy efficiency and decarbonization efficiency gradually improve, achieving an optimization of 23%-27%. Intelligent vehicles optimized for uncongested conditions exhibit smoother driving patterns, mitigating aggressive maneuvers and contributing to green transport. In congested scenarios, intelligent vehicles face constraints from leading vehicles, limiting speed optimization. At lower penetration rates (0.25), ADITS can worsen traffic conditions. However, with higher penetration rates, speed fluctuations decrease, leading to more uniform road speeds, reduced high-speed accelerations, and lower energy consumption and carbon emissions. These findings provide theoretical support for the implementation and development of intelligent transportation systems.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:33:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685863</guid>
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