<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSJhbGwiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMCIgLz48L3BhcmFtcz48ZmlsdGVycz48ZmlsdGVyIGZpZWxkPSJpbmRleHRlcm1zIiB2YWx1ZT0iJnF1b3Q7T3B0aW1pc2F0aW9uJnF1b3Q7IiBvcmlnaW5hbF92YWx1ZT0iJnF1b3Q7T3B0aW1pc2F0aW9uJnF1b3Q7IiAvPjwvZmlsdGVycz48cmFuZ2VzIC8+PHNvcnRzPjxzb3J0IGZpZWxkPSJwdWJsaXNoZWQiIG9yZGVyPSJkZXNjIiAvPjwvc29ydHM+PHBlcnNpc3RzPjxwZXJzaXN0IG5hbWU9InJhbmdldHlwZSIgdmFsdWU9InB1Ymxpc2hlZGRhdGUiIC8+PC9wZXJzaXN0cz48L3NlYXJjaD4=" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <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>
    </image>
    <item>
      <title>Valorization of Recycled Roofing Membrane Polymer Powder in Asphalt Binders Through Balanced Performance-Based Optimization</title>
      <link>https://trid.trb.org/View/2773213</link>
      <description><![CDATA[Recycling waste roofing membrane materials in asphalt binders can be a promising approach for improving pavement performance while reducing polymeric waste accumulation in landfills. This study investigates the effect of waste roofing membrane polymer powder, characterized using Laser Direct Infrared (LDIR) analysis, on the chemical, physical, and rheological properties of asphalt binders. LDIR characterization identified polyvinyl chloride (PVC) as the dominant constituent of the waste material, together with rubber-based particles and minor polymeric phases. This characterization approach provides quantitative insight into the heterogeneous composition of the waste material, addressing a challenge associated with the use of recycled polymer in asphalt modification. The waste polymer powder was incorporated into both unmodified and styrene–butadiene–styrene (SBS)-modified asphalt binders at dosages of 2%, 4%, and 6% by binder weight through wet blending. Binder characterization included Fourier Transform Infrared (FTIR) spectroscopy, rotational viscosity, storage stability, Dynamic Shear Rheometer (DSR), Multiple Stress Creep Recovery (MSCR), Linear Amplitude Sweep (LAS), Bending Beam Rheometer (BBR), and master curve analysis. The results show that waste polymer incorporation improves fatigue resistance and low-temperature performance, particularly at higher dosages. However, excessive polymer dosage negatively affects storage stability and rutting-related properties. Hybrid SBS/PVC modification provides a more balanced rheological response than PVC-only modification. Among the evaluated binders, the 4% SBS/PVC-modified binder exhibits the most balanced overall performance. Regression-based optimization identifies an optimum PVC content of 4.15%. Overall, the findings indicate that waste roofing membrane-derived PVC-rich polymer powder is an effective asphalt binder modifier, providing balanced improvements in binder performance.]]></description>
      <pubDate>Wed, 09 Sep 2026 09:03:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2773213</guid>
    </item>
    <item>
      <title>Structure Optimization of a Two-Dimensional Mixer for Mixing Uniformity Improvement Using the DEM</title>
      <link>https://trid.trb.org/View/2772514</link>
      <description><![CDATA[A two-dimensional (2-D) mixer has been widely used in the engineering field. The discrete element method (DEM) is capable of simulating and tracking collisions among particles inside the mixer. In this paper, the mixing process of spherical particles inside a 2-D mixer known as EYH150L is simulated by the DEM. The Lacey Index provides a quantitative measure of the blending efficacy achieved by a 2-D mixer. The DEM analysis indicated that the level of blending effectiveness among the particles in proximity to the rotating blades is significantly superior to that in regions devoid of blades. The rotational velocities of particles in blade-free zones are about 40% of those near the rotating blades, which serves as a key factor accounting for the slower increase in mixing efficiency observed in these regions. To address this disparity and enhance overall mixing performance, a mirrored rotating blade was incorporated, positioned to the left of the baseline revolving cylinder, thereby optimizing the structural configuration of the 2-D mixer. The verification tests indicated that the modification increases the mixing efficiency of the mixer at its left side, and enhances the blending uniformity, ensuring the four particle types are mixed equitably.]]></description>
      <pubDate>Wed, 09 Sep 2026 08:52:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2772514</guid>
    </item>
    <item>
      <title>Chicago's O'Hare Runway Configuration Management System (RCMS) - Volume I - Description of the Operational Software</title>
      <link>https://trid.trb.org/View/2742410</link>
      <description><![CDATA[Volume I of this report describes the proposed Runway Configuration Management System (RCMS) operational software for review by the facility personnel. It also serves as an input to RCMS functional specifications for the Traffic Management System (TMS) program. Using interactive computer logic, RCMS helps supervisors select runway configurations which reduce aircraft delays by optimizing throughput capacity in dynamic operational environments. Volume II of this report is the User's Guide to the RCMS.]]></description>
      <pubDate>Sun, 06 Sep 2026 16:10:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742410</guid>
    </item>
    <item>
      <title>Chicago's O'Hare Runway Configuration Management System (RCMS) - Volume II - User's Guide</title>
      <link>https://trid.trb.org/View/2742411</link>
      <description><![CDATA[Volume I of this report describes the proposed Runway Configuration Management System (RCMS) operational software for review by the facility personnel. It also serves as an input to RCMS functional specifications for the Traffic Management System (TMS) program. Using interactive computer logic, RCMS helps supervisors select runway configurations which reduce aircraft delays by optimizing throughput capacity in dynamic operational environments. Volume II of this report is the User's Guide to the RCMS.]]></description>
      <pubDate>Sun, 06 Sep 2026 16:10:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742411</guid>
    </item>
    <item>
      <title>Integrated optimization for coordinated traffic signal and vehicle routing with parallel distributed solution</title>
      <link>https://trid.trb.org/View/2705502</link>
      <description><![CDATA[Traffic signal control and vehicle routing are two promising strategies in traffic congestion management. However, conventional studies always treat these two strategies as separate modules, limiting the potential for system-wide efficiency gains. This study proposes an Integrated Traffic Signal and Route Guidance (ITSRG) optimization model in the Connected Vehicle (CV) environment. It simultaneously optimizes signal timing plans and individual vehicle routes. This study formulates the intrinsic coupling between signal plans and routing decisions as an explicit constraint, enabling the two variables to be jointly optimized within an integrated model. The proposed ITSRG model incorporates individualized route guidance for CVs, enabling full use of real-time vehicle information to achieve more accurate traffic control. Furthermore, to efficiently solve the complex integrated model, a parallel distributed solution algorithm based on the Alternating Direction Method of Multipliers (ADMM) algorithm is proposed. It decomposes the integrated problem into multiple subproblems, including the intersection signal control subproblem and the individual CV route selection subproblem, enabling scalable computation and real-time applicability in large networks. To validate the model’s effectiveness, experiments were conducted on a real-world road network in Qinzhou City, China. The results demonstrate that the proposed ITSRG model significantly outperforms benchmark methods in reducing average vehicle travel time. Furthermore, sensitivity analyses are conducted to evaluate the model’s performance under varying traffic demand levels, compliance rates, and parameters. Moreover, the effectiveness and computational efficiency of the proposed parallel distributed algorithms are validated through numerical experiments.]]></description>
      <pubDate>Thu, 03 Sep 2026 09:37:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2705502</guid>
    </item>
    <item>
      <title>An Integrated Multi-Scenario Calibration Framework for Driving Behavior Parameters in Highway Traffic Simulation</title>
      <link>https://trid.trb.org/View/2705492</link>
      <description><![CDATA[Simulation models are critical for management and control of highway. A critical issue of applying simulation models is the calibration of parameters, referring to determine simulation parameters such that the simulation outputs best fits real-world observations. Existing methods typically focus on isolated scenarios, e.g., free-flow sections, limiting the applicability of the parameters to diverse highway environments such as toll plaza and accident sections. Additionally, such scenario-specific approaches lead to inconsistent representations of driving behaviors across a full highway corridor. To tackle this issue, this paper presents an integrated framework for multi-scenario calibration of driving behavior parameters, considering three representative highway scenarios: toll plaza, free-flow section, and accident section. The method combines global sensitivity analysis with simulation-based optimization. Specifically, variance-based Sobol indices are first used to identify influential parameters across multiple highway scenarios, reducing calibration complexity. Then, a joint calibration model incorporates cross-scenario shared parameters and scenario-specific adjustments, optimized through a Differential Evolution algorithm. Experimental results demonstrate that the proposed framework improves calibration accuracy and robustness compared to single-scenario approaches. Sensitivity analysis reduces computational cost while maintaining performance, with jointly calibrated parameters achieving consistent replication of traffic metrics across all scenarios. This study provides a systematic approach for developing reliable simulation models applicable to complex highway environments.]]></description>
      <pubDate>Thu, 03 Sep 2026 09:37:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2705492</guid>
    </item>
    <item>
      <title>TAWJEEH: An Integrated Deep Reinforcement Learning and Heuristic Framework for Cellular Vehicle-to-Everything Enabled Real-Time Multidepot Vehicle Routing Optimization in Urban Logistics</title>
      <link>https://trid.trb.org/View/2772597</link>
      <description><![CDATA[Urban logistics are increasingly strained by dynamic traffic conditions and complex operational constraints, rendering traditional optimization methods for the multidepot capacitated vehicle routing problem inadequate. This paper introduces TAWJEEH, a novel hybrid framework that integrates deep reinforcement learning, classical heuristics, and cellular vehicle-to-everything (C-V2X) communications for time-dependent MDCVRP optimization. The framework employs a deep Q network to learn adaptive policies for customer-to-vehicle assignment, complemented by clustering algorithms for initial customer grouping and heuristics for route refinement. Leveraging real-time data streams from C-V2X messages, TAWJEEH dynamically adjusts routes in response to live traffic conditions. Extensive and realistic simulations using SUMO on Hamburg and Luxembourg road networks validate our approach. The performance of TAWJEEH is benchmarked against the Clarke-Wright savings (CWS) heuristic and ant colony optimization (ACO). Results show significant and consistent reductions in key performance metrics; for instance, in the large-scale Luxembourg scenario, TAWJEEH reduces total travel distance by up to 55.4% compared with CWS and 8.9% against ACO. These improvements translate to substantial reductions in cumulative travel time, fuel consumption, CO2 emissions, and overall operational costs. TAWJEEH proves to be a robust, scalable, and computationally efficient solution, highlighting the potential of combining advanced artificial intelligence techniques with vehicular communication technologies to address complex urban logistics challenges.]]></description>
      <pubDate>Thu, 03 Sep 2026 09:08:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2772597</guid>
    </item>
    <item>
      <title>Optimizing demand-responsive feeder transit stop locations for transportation hubs: A multi-objective approach</title>
      <link>https://trid.trb.org/View/2728294</link>
      <description><![CDATA[This study develops a multi-objective stop location optimization framework for demand-responsive feeder transit serving transportation hubs. First, a negative binomial regression model that integrates aggregated historical travel data and built-environment data is constructed to predict fine-grained travel demand at the point-of-interest (POI) level. Second, using the predicted POI-level demand as input, a multi-objective optimization model is formulated to maximize demand coverage, minimize investment cost, and maximize equity improvement. The proposed framework is validated through a large-scale case study covering the entire city of Shanghai, China. The optimization results reveal trade-offs among the three objectives. Coverage exhibits a positive linear correlation with cost, whereas equity and coverage follow a quadratic relationship. Different planning preferences lead to distinct spatial configurations. The coverage-maximizing and cost-minimizing solutions primarily adjust the number of stops. In contrast, the equity-maximizing solution adopts an equity-driven spatial reallocation, reducing stops in high-accessibility central areas while increasing stops in low-accessibility peripheral areas. Planning schemes also exhibit specific land-use associations. The coverage-maximizing solution prioritizes accommodation and workplace areas, whereas the cost-minimizing solution reduces coverage of residential and workplace areas. The equity-maximizing solution increases coverage of institution areas while reducing other areas.]]></description>
      <pubDate>Wed, 02 Sep 2026 16:33:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2728294</guid>
    </item>
    <item>
      <title>Design and Optimization of a Hybrid VLC/THz Infrastructure-to-Vehicle Communication System for Intelligent Transportation</title>
      <link>https://trid.trb.org/View/2672830</link>
      <description><![CDATA[This paper proposes a hybrid infrastructure-to-vehicle (I2V) communication framework to support future 6G-enabled intelligent transportation systems (ITS) in smart cities. Leveraging existing LED streetlighting infrastructure, the system simultaneously delivers energy-efficient illumination and high-speed wireless connectivity. The proposed scheme integrates visible light communication (VLC) with a complementary terahertz (THz) antenna array to overcome VLC limitations under high ambient light and adverse weather conditions. Key contributions include the design of a VLC/THz access network, seamless integration with lighting infrastructure, a proposed switching-combination (PSC) mechanism, and a physical layout optimization strategy. Using a grid search method, thousands of configurations were evaluated to maximize lighting coverage, received power, signal-to-noise ratio (SNR), signal-to-interference-and-noise ratio (SINR), and minimize outage probability. Results show that optimized lighting coverage improves from 35% to 97%, while hybrid communication coverage increases from 49% to 99.9% at the same power level. Under extreme environmental conditions, the hybrid system maintains up to 99% coverage, compared to 69% with VLC alone. These results demonstrate the scalability, cost-efficiency, and practicality of the proposed system for next-generation ITS deployment.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672830</guid>
    </item>
    <item>
      <title>Optimization of Urban Emergency Multimodal Transportation Scheduling With UAV-Ground Traffic Coordination</title>
      <link>https://trid.trb.org/View/2672825</link>
      <description><![CDATA[Advanced Air Mobility (AAM) is a crucial component of future intelligent transportation systems, where Uncrewed Aerial Vehicles (UAVs) undertake tasks such as inspection, monitoring, rescue, and logistics. However, in the early stages of development, it is essential to coordinate with other ground transportation vehicles to address issues such as battery life, multi-UAV conflicts, and computational load, thereby enhancing system reliability. Consequently, this paper investigates an urban emergency response paradigm, termed UBT, where UAVs routinely ride on the roofs of buses for charging and inspection, and collaborate with surrounding taxis and those near emergency sites to respond to unpredictable random incidents in urban areas. Within the UBT paradigm, a multi-modal emergency response process model for a single UAV is established, considering the mobility of buses and the spatiotemporal characteristics of taxis. Building on this, a multi-UAV emergency dispatch model is developed, incorporating data-driven spatiotemporal prediction models for taxis, and considering emergency response delays, UAV hovering time, and relay costs, to maximize urban spatiotemporal coverage benefits. Finally, the performance of UBT is comprehensively evaluated using a large-scale real-world vehicle trajectory dataset. The results indicate that, compared to baseline methods, under the premise of 95% spatiotemporal coverage, the UBT scheme increases UAV hovering time by 65.3%, reduces infrastructure costs by 99.6%, and increases emergency response delays by only 9.36%.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672825</guid>
    </item>
    <item>
      <title>Optimization for Dynamic Multi-RIS-assisted SWIPT-Enabled V2I Networks: A Deep Learning Approach</title>
      <link>https://trid.trb.org/View/2622052</link>
      <description><![CDATA[Reconfigurable intelligent surfaces (RISs) have emerged as a highly promising technology in sixth-generation (6G) vehicular systems, offering the ability to dynamically control the wireless propagation environment. In this paper, we examine simultaneous wireless information and power transfer (SWIPT) by employing multiple RISs within a vehicle-to-infrastructure (V2I) communication system. The wireless environment exhibits high complexity due to fading and shadowing effects. To model this accurately, we adopt the double generalized Gamma (dGG) distribution. This comprehensive modeling approach enables a more realistic and insightful performance evaluation of RIS-assisted SWIPT systems under practical mobility and fading conditions. To reflect real-world vehicular dynamics, we incorporate a statistical Random Waypoint (RWP) mobility model, while also accounting for imperfections in channel state information (CSI) that arise due to high mobility and channel estimation errors. The study also integrates a non-linear energy harvesting (NL-EH) scheme to enhance performance via the power-splitting (PS) protocol. A unified objective function is proposed to jointly optimize transmit power and PS factors, aiming to maximize both the harvested energy and information rate. To address the non-convex nature of the problem, an iterative algorithm is utilized, supported by closed-form solutions derived from the Karush-Kuhn-Tucker (KKT) conditions and joint optimization (JO) method. Monte-Carlo simulations are conducted to verify the accuracy of the analytical results. Additionally, a deep neural network (DNN) framework is introduced for optimized value prediction, demonstrating superior SWIPT performance compared to single RIS configurations, with reduced complexity and faster execution.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2622052</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 GWO-based approach for task scheduling in heterogeneous vehicular fog computing environments</title>
      <link>https://trid.trb.org/View/2663731</link>
      <description><![CDATA[Vehicular Fog Computing (VFC) empowers automotive networks with fog computing, ensuring minimal delays for user services and vehicle operations. This study introduces a novel meta-heuristic algorithm for optimizing task scheduling in Vehicular Fog Computing (VFC). Leveraging Grey Wolf Optimization (GWO), the method differentiates between static and dynamic fog nodes, representing stationary servers and moving vehicles, respectively. Afterwards, a new stage is applied to refine the results of the GWO. The purpose of this step is to identify the fog node with the highest workload and distribute a percentage of its workload to several other fog nodes. This will reduce waiting time and improve makespan. Lastly, resource-intensive tasks are prioritized and allocated to these nodes. The paper includes a thorough evaluation of the GWO-based approach, analyzing the impact of various algorithm parameters. Performance is assessed using both real-world applications and synthetic data.Moreover, our implementation is considered ARM processor as computing resources in dynamic fog node. Experimental results demonstrate that the proposed algorithm achieves lower monetary costs than existing solutions and it shows the improvement at wait and makespan.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663731</guid>
    </item>
    <item>
      <title>Traffic Aware Adaptive Neighbor Discovery for Vehicular Networks</title>
      <link>https://trid.trb.org/View/2663673</link>
      <description><![CDATA[In vehicular networks, neighbor discovery is achieved via frequently broadcasting a certain kind of control message, which is called beacon message. Since there are both control messages and various types of service messages which coexist and share wireless channels with limited communication resources, it is essential to balance the resource allocation between control messages and service messages. Specifically, if too much communication resource is allocated to transmit the control message, the communication quality of services may be degraded. Conversely, it may lead to unstable network connections and further impact the communication quality of services in vehicular networks. Thus in this work, we focus on optimizing the broadcast rate of beacon messages in a vehicular network by jointly considering the vehicle mobility, the channel randomness and the multi-traffic network characteristics, for achieving the optimal tradeoff between the accuracy and overhead of neighbor discovery. We first establish a theoretical analysis model for deriving the closed-form relationship between the stable hitting probability and the broadcast rate of beacon messages. Based on that, we then propose an optimal neighbor discovery scheme called Mobility and Multi-Traffic based Adaptive Neighbor Discovery method (MMTAND), which can adjust the broadcast rate of beacon messages according to dynamical network environments and achieve the optimal tradeoff between the accuracy and overhead of neighbor discovery. Extensive simulation results show that the proposed method outperforms the existing methods in terms of performance, which is expected to be applied to neighbor discovery in practical vehicular networks.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663673</guid>
    </item>
    <item>
      <title>Quantum federated reinforcement learning-based energy efficiency optimization for IRS-assisted underlaying UAV communication</title>
      <link>https://trid.trb.org/View/2659406</link>
      <description><![CDATA[Unmanned aerial vehicle (UAV)-assisted vehicular networks have garnered researchers’ attention as a promising solution to the limitations of vehicle-to-everything (V2X) communication, especially in the dynamic and dense urban scenario due to the non-line-of-sight (NLoS) setup, interference and unreliable links. Intelligent reflecting surfaces (IRS) further enhance communication quality by intelligently manipulating wireless signals when integrated with a UAV-assisted vehicular network. Although the IRS also supports simultaneous transmission and reflection (STAR), here, the passive reflection mode is considered alone. However, within such networks, due to dynamic topology, multi-dimensional state space, energy constraints and decentralized data, efficient resource management, power control and task offloading are compromised. Due to the limited adaptability, poor scalability and convergence of classical optimization techniques and deep reinforcement learning (DRL), we have presented a novel framework based on quantum federated reinforcement learning (QFRL) in this article. The suggested system makes effective use of the quantum properties of superposition and entanglement to facilitate decision-making. Task offloading, power allocation, and UAV trajectory are all taken into account when modelling the optimization problem as a Markov decision process (MDP). In order to guarantee privacy and decentralized intelligence while drastically cutting down on convergence time and computational overhead, a Quantum Neural Network (QNN) is used in federated learning (FL). The suggested QFRL framework performs better than the conventional Deep Deterministic Policy Gradient (DDPG) and Federated Reinforcement Learning (FRL) approaches, according to simulation results. In particular, the QFRL scheme outperforms FRL by 10.62% and DDPG by 49.32% in terms of energy efficiency. Additionally, QFRL exhibits better scalability and convergence speed as the number of vehicle terminals and IRS elements increases. A quantum-enhanced learning technique is established in this work as a potent remedy for the next generation of energy-efficient UAV communication networks.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659406</guid>
    </item>
  </channel>
</rss>