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        <title>Frontiers in Future Transportation | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/future-transportation</link>
        <description>RSS Feed for Frontiers in Future Transportation | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-08-14T22:02:22.77+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/ffutr.2026.1792757</guid>
        <link>https://www.frontiersin.org/articles/10.3389/ffutr.2026.1792757</link>
        <title><![CDATA[The impact of navigational alarms in ECDIS training: assessing physiological signals]]></title>
        <pubdate>2026-08-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Dejan Žagar</author><author>Matija Svetina</author><author>Tanja Brcko Satler</author>
        <description><![CDATA[The increasing reliance on ECDIS in maritime operations underscores the need to understand how navigational alarm affect human performance, yet evidence on their physiological impact during training remains limited. The study aimed to explore the effects of a stressful event on general autonomic arousal during navigational operations and how this relationship changes over the course of ECDIS training. A longitudinal design was employed with nine novice participants (N = 9), who completed repeated simulator sessions while physiological signals (EDA, ECG, PPG) were recorded and combined into a composite arousal index. The results showed that alarms significantly increased physiological arousal, with an initial rise followed by gradual attenuation across sessions. The findings indicate adaptation to repeated exposure and demonstrate that objective physiological measures capture dynamic training responses, supporting the need for their integration into simulator-based training to improve situational awareness, workload regulation, and maritime safety.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/ffutr.2026.1801962</guid>
        <link>https://www.frontiersin.org/articles/10.3389/ffutr.2026.1801962</link>
        <title><![CDATA[Immune-enhanced NSGA-II for multi-objective multi-UAV disaster relief material distribution]]></title>
        <pubdate>2026-08-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jian Shang</author><author>Heng Li</author><author>Enzhong Li</author><author>Zhonglin Zhang</author><author>Lijun Wang</author><author>Yiran Jiang</author><author>Yingqi Bai</author><author>Hanjie Kang</author><author>Chen Xiao</author><author>Youwei Zhu</author><author>Min Dong</author><author>Jingpu Zhang</author>
        <description><![CDATA[In disaster-relief logistics, disrupted ground transportation networks and the limited payload and endurance of UAVs make it difficult to allocate emergency materials across geographically dispersed demand points. This study formulates a capacity-constrained bi-objective multi-UAV material distribution problem that simultaneously minimizes total flight distance and workload imbalance. Each demand point is served by one UAV in a single-trip route, and route feasibility is evaluated under payload-capacity and maximum route-distance constraints. To solve this constrained discrete optimization problem, we propose an immune-enhanced NSGA-II algorithm, referred to as INSGA-II. The algorithm represents each solution using an integer assignment vector and a priority vector for route decoding, and integrates immune cloning, stimulation-guided clone allocation, and a linearly decreasing mutation probability to improve the exploration of non-dominated allocation-routing solutions. Comparative experiments were conducted over 30 independent runs against standard NSGA-II, MOEA/D, Weighted-GA, and Weighted-ACO using hypervolume (HV), inverted generational distance (IGD), and runtime as evaluation metrics. INSGA-II achieved the highest mean HV of 0.895 and the lowest mean IGD of 0.184 among the compared algorithms, showing favorable average Pareto-front approximation performance in the tested scenario. Its average runtime was higher than NSGA-II and MOEA/D due to additional immune and repair operations, but lower than Weighted-GA and Weighted-ACO in the tested setting. The results suggest that INSGA-II provides quality-oriented Pareto trade-off solutions for capacity-constrained multi-UAV disaster-relief material distribution.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/ffutr.2026.1884516</guid>
        <link>https://www.frontiersin.org/articles/10.3389/ffutr.2026.1884516</link>
        <title><![CDATA[Predictive multi-criteria decision-support framework for urban flyover design: integrating structural, economic, and environmental constraints]]></title>
        <pubdate>2026-08-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jaya Rajkumar Ramchandani</author><author>Suddhasheel Ghosh</author>
        <description><![CDATA[IntroductionUrban flyover design involves complex multi‐objective optimization that must balance structural requirements, economic viability, and environmental sustainability.MethodsWe developed a predictive decision‐support framework using multivariate radial basis functions (MRBF) with Gaussian kernels to model interdependencies among design parameters. Data from 47 flyover configurations across 12 Indian cities were analyzed, with independent variables including traffic volume, terrain characteristics, urban density, and soil bearing capacity.ResultsThe framework achieved prediction accuracy of 91.4% (R2 = 0.914) for construction cost estimation and 88.7% (R2 = 0.887) for environmental impact assessment. Sensitivity analysis revealed soil bearing capacity and traffic volume accounted for 58% of cost variance, while urban density contributed 42% to environmental impact predictions. Case studies from Delhi, Mumbai, and Bengaluru demonstrated applicability across diverse contexts.DiscussionThe methodology enables rapid preliminary design evaluation, reducing iteration time by 63% while ensuring compliance with IRC:6‐2017 safety standards and sustainability criteria. The framework provides decision‐makers with quantitative tools for trade‐off analysis in constrained urban environments.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/ffutr.2026.1827528</guid>
        <link>https://www.frontiersin.org/articles/10.3389/ffutr.2026.1827528</link>
        <title><![CDATA[Cyber-resilient flight architecture for software-defined UAVs using digital twin-based validation]]></title>
        <pubdate>2026-07-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>I. C. Emeto</author><author>A. G. Adamu</author><author>I. H. Ezeh</author><author>E. C. Ochuba</author><author>C. A. Okoloegbo</author>
        <description><![CDATA[Software-Defined Unmanned Aerial Vehicles (SD-UAVs) rely heavily on software control and networked communication, making them vulnerable to cyber-physical attacks that threaten flight safety and mission reliability. Traditional UAV security approaches are largely reactive and lack integrated resilience mechanisms capable of sustaining stable flight under adversarial conditions. This study proposes a cyber-resilient flight architecture that integrates software-defined control, AI-driven anomaly detection, and Digital Twin-based validation. A Long Short-Term Memory (LSTM) model was implemented for telemetry anomaly detection, while SHAP-based Explainable AI was used to ensure interpretable resilience decisions. The system was implemented using Pixhawk 6X hardware, NVIDIA Jetson Orin Nano processing, MAVLink communication, and a Gazebo Garden + ROS2 Humble simulation environment. A Lyapunov-based stability analysis was conducted to validate the 50 m synchronization loop. Comparative evaluation demonstrated improved anomaly detection latency, reduced false detection rates, faster recovery time, and lower trajectory deviation compared to a conventional UAV architecture. The proposed system maintained stable flight under simulated cyberattack scenarios including command manipulation and sensor perturbation. The integration of Trustworthy AI with digital twin validation enhances UAV resilience, operational safety, and adaptive recovery under cyber threats, providing a scalable framework for secure autonomous flight systems.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/ffutr.2026.1875006</guid>
        <link>https://www.frontiersin.org/articles/10.3389/ffutr.2026.1875006</link>
        <title><![CDATA[Mining of bus spatiotemporal distribution based on multi-source data fusion and OD deduction]]></title>
        <pubdate>2026-07-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jiaojiao Xi</author><author>Jie Ma</author><author>Ming Lei</author>
        <description><![CDATA[This study provides a spatiotemporal characteristic analysis method based on multi-source data fusion and origin–destination (OD) deduction for the field of intelligent bus dispatching. First, it comprehensively considers the impact of data from the foundation layer, interaction layer, and output layer. Combining the Dempster–Shafer theory evidence method with Jensen–Shannon divergence to enhance fusion credibility can ensure the quality of collected data and the accuracy of its fusion. Second, based on multi-source data cross fusion, the critical value of travel time difference is used to solve the number of passengers boarding at each station, and the travel chain method and attraction method are used to solve the number of passengers disembarking at each station. Finally, based on OD information, K-means clustering analysis was used to analyze the characteristics of bus operating time. A spatial delay propagation analysis was conducted by combining the running time between stations, the stopping time at stations, and the waiting time at intersections. This study can optimize the OD attraction intensity and address the issue of low accuracy in cross-layer data fusion. At the same time, the study can comprehensively obtain spatiotemporal distribution characteristics and provide decision-making support for optimizing bus routes.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/ffutr.2026.1842188</guid>
        <link>https://www.frontiersin.org/articles/10.3389/ffutr.2026.1842188</link>
        <title><![CDATA[Antecedents and performance consequences of green logistics management practices: evidence from Vietnamese logistics enterprises]]></title>
        <pubdate>2026-07-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kim-Tuan Phan</author><author>Van-Hai Hoang</author><author>Thanh-Dat Ho Nguyen</author>
        <description><![CDATA[Motivated by the fragmented understanding and ongoing academic debate regarding the performance outcomes of green operations, particularly in emerging economies, this study investigates the antecedents of Green Logistics Management Practices (GLMPs) adoption and its impact on Market Performance (MPer) and Operational Performance (OP) within Vietnamese enterprises. This study utilized a quantitative approach, using Partial Least Squares Structural Equation Modeling to analyze primary survey data from 183 managers in Vietnam’s logistics sector. The empirical analysis reveals that Economic Pressure, Governmental Regulation, and Managerial Commitment significantly drive GLMPs adoption, whereas Customer Pressure demonstrates no significant effect. Furthermore, GLMPs adoption substantially enhances both MPer and OP. Additionally, Green Traceability (GTR) positively moderates these relationships, amplifying the operational and market benefits of GLMPs. These findings challenge typical stakeholder expectations, indicating that economic efficiency and regulatory mandates, rather than customer demands, primarily drive green logistics in Vietnam, while highlighting the role of information transparency. Ultimately, this study implies that firms should adopt GLMPs as a strategic endeavor to acquire a competitive advantage in today’s environmentally conscious market.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/ffutr.2026.1888695</guid>
        <link>https://www.frontiersin.org/articles/10.3389/ffutr.2026.1888695</link>
        <title><![CDATA[Attempting a strategic planning process model for sustainable public transport across national and regional borders in the south Baltic Sea region]]></title>
        <pubdate>2026-07-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sven Borén</author><author>Henrik Ny</author><author>Juliette Tenart</author>
        <description><![CDATA[IntroductionSeveral plans have been announced recently to reduce emissions, with a focus on climate change. Current methods for creating attractive and sustainable transport take a broader perspective, including transport-related emissions and noise, but lack general guidance on public transport across national and regional borders. To fill this gap, the authors developed a strategic planning model for sustainable mobility.MethodsResearch activities were mainly carried out within the South Baltic Sea Region project Interconnect, whose purpose was to curb car-reliance by increasing public transport across national and regional borders. The study drew on experiences from earlier and related studies, literature reviews, stakeholder collaboration workshops, a survey, and expert interviews.Results and DiscussionsThis resulted in a process model that embeds the Framework for Strategic Sustainable Development, including its Sustainability Principles, and a procedure for planning towards sustainability that, when applied, can lead to a plan for sustainable public transport across national and regional borders.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/ffutr.2026.1884428</guid>
        <link>https://www.frontiersin.org/articles/10.3389/ffutr.2026.1884428</link>
        <title><![CDATA[Insurance-market depth and logistics performance in OECD countries: a comparative institutional association]]></title>
        <pubdate>2026-07-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Seyed Amirhossein Shojaei</author><author>Alireza Pakgohar</author><author>Marjan Orouji</author>
        <description><![CDATA[This study examines whether insurance-market depth is associated with national logistics performance in OECD countries. Logistics-performance research usually emphasises customs, infrastructure, trade facilitation and logistics-service capability, while the broader insurance-market conditions under which freight and supply-chain risks are transferred remain underexamined. Using a balanced panel of 33 OECD countries across four Logistics Performance Index (LPI) waves, 2012, 2014, 2016 and 2018, the study links overall LPI and its six components to insurance density, insurance penetration, retention ratio and non-life insurance share. LPI-wave fixed-effects models with country-clustered standard errors show that logged total insurance density is positively associated with overall LPI and all six LPI components across countries. However, strict country and LPI-wave fixed-effects models do not show a significant within-country relationship. Insurance penetration, retention ratio and non-life insurance share also provide no robust positive evidence once insurance-market depth is considered. The findings therefore support a comparative institutional association: countries in the current-OECD sample with deeper insurance markets tend to have stronger logistics performance, but the evidence does not show that increases in insurance density within a country directly cause logistics-performance improvements. The study contributes by identifying insurance-market depth as an overlooked institutional correlate of logistics performance and by distinguishing insurance density from penetration, retention and market composition.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/ffutr.2026.1782348</guid>
        <link>https://www.frontiersin.org/articles/10.3389/ffutr.2026.1782348</link>
        <title><![CDATA[Public transportation and environmental sustainability nodes in TOD-based Mahakam River city planning]]></title>
        <pubdate>2026-07-17T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author> Tukimun</author><author>Andjar Prasetyo</author><author>Ari Sasmoko Adi</author><author> Eswan</author><author>Yustina Hastrini Nurwanti</author><author>Titi Mumfangati</author><author>Tugas Tri Wahyono</author><author>Fahmi Ranggamurti</author><author>Heri Wahyudianto</author><author>Agustinus Hartopo</author>
        <description><![CDATA[This study addresses the growing need for sustainable and integrated transportation systems in East Kalimantan, particularly in the context of low-carbon development and the strategic role of the Mahakam River as a key regional transport corridor. Despite its potential to support Transit Oriented Development (TOD), the river-based transportation system remains underutilized and lacks integrated planning. This research aims to analyze the reciprocal relationships among four main TOD dimensions: Transportation Accessibility and Integration, Socio-Economic Impact, Environmental and Ecological Sustainability, and Satisfaction and Policy Support. Using an explanatory quantitative approach, a survey of 105 respondents was conducted and analyzed using Random Forest Regression (RFR) to model complex variable interactions. The results show that the Integrated Transport Access model achieved the highest performance (R2 = 0.868; MAPE = 7.44%), followed by the Socio-Economic Impact model (R2 = 0.744). The Ecological Environment variable consistently emerged as the most influential factor in three out of four models, underscoring the critical role of environmental sustainability in transportation effectiveness and policy acceptance. These findings highlight that successful TOD-based riverfront revitalization requires a balance between transportation development and ecological conservation, supporting Indonesia’s Sustainable Development Goals (SDGs). The study also suggests that future research should incorporate qualitative approaches and secondary data analysis to further enrich policy recommendations.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/ffutr.2026.1711068</guid>
        <link>https://www.frontiersin.org/articles/10.3389/ffutr.2026.1711068</link>
        <title><![CDATA[Optimizing drivable area detection for autonomous vehicles through shadow and road edge line analysis in U-net-based models]]></title>
        <pubdate>2026-07-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chun Chen</author><author>Xingfeng Li</author>
        <description><![CDATA[Reliable drivable area detection is essential for autonomous vehicle operation. However, built environments are often confused with other objects that share similar local features because of these environments’ variability and complexity. Utilizing an improved U-Net model, this research systematically evaluates how road shadows and edge markings affect detection accuracy while exploring built environment optimization. Experimental results demonstrate that strong shadows reduce drivable area detection accuracy from 95.9% to 88.2% under consistent road conditions. Complex local contrast changes caused by shadows lead to redundant elements corresponding to shadow boundaries during the segmentation process. Detection accuracy dropped from 95.6% to 91.4% due to missing or worn road edge lines. Furthermore, in areas with missing edge lines, the accuracy of detecting drivable areas is closely related to the color contrast with the surroundings. This study suggests that future road designs for autonomous driving should minimize environmental elements that create strong shadows, such as tall trees or buildings. In addition, autonomous driving roads should have clear edges and high color contrast between drivable areas and the surrounding nondrivable areas.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/ffutr.2026.1765037</guid>
        <link>https://www.frontiersin.org/articles/10.3389/ffutr.2026.1765037</link>
        <title><![CDATA[Dynamic traffic signal scheduling system based on adaptive quad agent Double Deep Q -network algorithm]]></title>
        <pubdate>2026-07-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Bharathi Ramesh Kumar</author><author>Sachin Salunkhe</author><author>Sachin Shinde</author><author>Lenka Cepova</author>
        <description><![CDATA[IntroductionReal-time estimation of vehicle queue lengths at signalized intersections remains a significant challenge, particularly when conventional input–output traffic models fail to capture queues extending beyond detector coverage. Although Deep Q-Networks (DQNs) have demonstrated considerable potential for dynamic traffic signal control, existing approaches often suffer from large state spaces, unstable reward signals, and inefficient utilization of high-quality traffic data. To address these limitations, this study proposes an Adaptive Quad-Agent Double Deep Q-Network (AQDDQN) framework for intelligent traffic signal optimization.MethodsThe proposed AQDDQN framework improves learning stability and Q-value estimation accuracy through a multi-agent reinforcement learning strategy. The model analyzes the relationship between vehicle queue length and reward values to optimize signal control decisions. Historical traffic data are utilized to establish preconditions, time-based prediction errors are computed, and optimal signal phases are selected based on minimum loss across multiple preconditions. The experimental evaluation includes agent-wise behavioral analysis and comparative assessments against Double Deep Q-Network (DDQN), Fixed Point Techniques, and Improved DDQN methods. Performance is evaluated using metrics such as reward values, queue lengths, predicted overflow delays, and queue length–reward relationships.ResultsThe proposed adaptive framework demonstrates superior performance compared with existing approaches by improving traffic signal control accuracy, reducing prediction errors, and enhancing overall traffic throughput. Simulation results indicate that the AQDDQN model effectively supports dynamic signal phase adaptation, minimizes congestion, and provides more accurate queue length estimations under complex traffic conditions.DiscussionThe findings confirm the effectiveness and robustness of the proposed AQDDQN framework for real-time intelligent traffic management. By improving learning stability and adaptive decision-making capabilities, the model offers a practical solution for optimizing traffic operations at signalized intersections and has strong potential for deployment in future smart transportation systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/ffutr.2026.1760027</guid>
        <link>https://www.frontiersin.org/articles/10.3389/ffutr.2026.1760027</link>
        <title><![CDATA[Joint optimization of UAVs nest siting and parallel scheduling under solar power supply constraints]]></title>
        <pubdate>2026-07-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Weihao Zhang</author><author>Wenbin Wu</author><author>Lihui Lin</author><author>Xiaojie Wu</author>
        <description><![CDATA[As power grid maintenance moves toward digitalization and decarbonization, UAVs have become a key technological asset for transmission line inspection, with supporting nests (automated UAV base stations) increasingly evolving toward autonomous, renewable-energy-powered infrastructures. Solar energy is gradually being adopted as the primary power source for these nests, reducing the construction and maintenance costs of external power systems. Nevertheless, the high upfront investment in solar and energy storage, coupled with substantial spatial variability in solar irradiance, poses challenges for efficient nest deployment and UAVs scheduling under large-scale, long-range inspection scenarios. To address these challenges, this paper proposes a two-stage integrated optimization framework for nest siting and UAVs parallel scheduling. In the first stage, a Mixed-Integer Linear Programming (MILP) model is formulated with the objective of minimizing nest construction costs, while an Epsilon-constraint method is used to set a quality threshold. The quality threshold is determined by a synergy score that combines factors such as solar irradiance, tower density, and spatial proximity, to achieve the optimal nest layout. In the second stage, based on the established nest layout and coverage relationships, a variant of the Vehicle Routing Problem (VRP) with endurance and charging constraints is developed, aiming to minimize the total inspection duration. Experimental results show that the optimized nest layout effectively reduces overall construction cost while achieving a higher synergy score. Under the cost-effective siting solution, the cross-tower feasible swapping strategy reduces the system Makespan by approximately 1.3%. The framework has been validated for its effectiveness and scalability in solar-driven UAV inspection deployment and scheduling.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/ffutr.2026.1804269</guid>
        <link>https://www.frontiersin.org/articles/10.3389/ffutr.2026.1804269</link>
        <title><![CDATA[Detecting CycleGAN-Spoofed AIS data using a GAN fingerprinting method with LSTM-Based classification]]></title>
        <pubdate>2026-07-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>S. M. Ashfaq uz Zaman</author><author>Faizan Qamar</author><author>Masnizah Mohd</author><author>Nur Hanis Sabrina Suhaimi</author><author>Amith Khandakar</author>
        <description><![CDATA[The identification of spoofed AIS data is crucial for ensuring secure and dependable maritime navigation, vessel tracking, and surveillance systems. Recent advances in generative modeling, particularly CycleGANs, have made identifying falsified AIS trajectories significantly more difficult as synthetic data can closely replicate the spatiotemporal characteristics of genuine vessel movements. Conventional anomaly detection and supervised learning approaches often fail to generalize to such sophisticated spoofing strategies. This paper presents an LSTM-based fingerprinting framework for robust detection of CycleGAN-generated AIS spoofing. The proposed method learns intrinsic spatiotemporal fingerprints of authentic AIS trajectories by modeling long-term temporal dependencies and vessel motion dynamics, without requiring labeled synthetic data during training. Incoming AIS sequences are classified as real or spoofed by comparing their learned fingerprint representations using a similarity-based decision mechanism. Experimental evaluations conducted on a large dataset comprising real AIS data and CycleGAN-generated synthetic trajectories demonstrate that the proposed approach achieves a precision of 0.95, recall of 0.96, F1-score of 0.955, outperforming state-of-the-art anomaly detection methods including kinematic interpolation, GeoTrackNet, and Transformer-based classifiers. Furthermore, the method exhibits strong generalization to unseen GAN variants, with only a marginal recall degradation from 0.96 to 0.93 and a consistently low false positive rate of 0.02–0.03. These findings demonstrate the effectiveness and robustness of the proposed fingerprinting approach in real-world maritime security scenarios, offering a dependable mechanism for mitigating sophisticated AIS spoofing attacks.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/ffutr.2026.1871420</guid>
        <link>https://www.frontiersin.org/articles/10.3389/ffutr.2026.1871420</link>
        <title><![CDATA[Crosswind stability and critical safe speed of medium-duty box trucks on expressways under strong-wind conditions]]></title>
        <pubdate>2026-07-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shasha Jiang</author><author>Wuping Ran</author><author>Yuan Ling</author><author>Long Cheng</author><author>Weihao Xie</author><author>Jiayi Niu</author><author>Jingjin Wang</author><author>Zhibobo Xing</author>
        <description><![CDATA[IntroductionCrosswind-induced rollover is a major safety concern for medium-duty box trucks operating on expressways in high–wind regions. Existing crosswind studies have substantially improved the understanding of aerodynamic loading and vehicle dynamic responses of high–sided vehicles, especially on bridges, in gusty winds, or under selected vehicle configurations. However, relatively limited attention has been paid to the expressway operation of medium–duty box trucks on long, straight, wind–exposed road sections, where loading state, wind yaw angle, wind intensity, and vehicle speed jointly determine rollover risk.MethodsTo address this gap, this study develops a TruckSim model of a two–axle medium–duty box truck and establishes a mechanism–oriented rollover risk assessment framework considering wind speed, wind yaw angle, loading condition, and vehicle speed. Three loading conditions, namely unladen, half-laden, and fully laden, are examined. Crosswind scenarios are defined with yaw angles of 30 ° to 150 ° and wind intensities corresponding to Beaufort scale levels 6 to 12. A dual–indicator method combining the minimum tyre vertical load and Load Transfer Ratio is adopted. The minimum tyre vertical load is used to identify wheel lift-off and incipient rollover, while the Load Transfer Ratio is used to evaluate lateral load transfer and rollover risk evolution. For the dual–tyre rear axle, same–side inner and outer tyre loads are aggregated into wheel–assembly loads. The front–axle, rear–axle, and whole–vehicle Load Transfer Ratios are then calculated separately, and their maximum value is used as the comprehensive rollover risk indicator.ResultsThe results show that wind yaw angle has a significant effect on rollover risk. The range of 90 ° to 120 ° is the most critical, with 120 ° being the most adverse wind direction. Rollover risk increases nonlinearly with vehicle speed and wind speed, and the unladen condition shows the highest crosswind sensitivity.DiscussionCompared with previous single-factor or scenario–specific evaluations, the proposed framework advances existing studies by linking multi-factor simulation results to critical safe speeds and differentiated engineering control thresholds for medium–duty box trucks. The findings provide a basis for loading–sensitive speed limits, real–time risk warnings, and traffic management of medium-duty box trucks in high–wind expressway environments.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/ffutr.2026.1859914</guid>
        <link>https://www.frontiersin.org/articles/10.3389/ffutr.2026.1859914</link>
        <title><![CDATA[Multimodal logistics systems in Kazakhstan: comparative assessment of logistics performance and international transport corridors]]></title>
        <pubdate>2026-07-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Nurkhan Zhaken</author><author>Zauresh Akhmetova</author><author>Aisulu Moldabekova</author><author>Ozerke Amangeldi</author>
        <description><![CDATA[Multimodal logistics systems in Eurasia are evolving amid increasing freight complexity, intensifying competition for transport corridors, and reconfigured transport links between Europe and Asia. This study aims to evaluate the structural differences in logistics performance in selected countries and identify factors influencing the effectiveness of multimodal logistics corridors, with particular focus on Kazakhstan. The methodology combines a comparative analysis of the Logistics Performance Index (LPI) and its six components; a temporal comparison of Kazakhstan and China from 2007 to 2023; a case study of Globalink Logistics; questionnaire-based operational diagnostics; and multimodal route modelling. The results reveal that Kazakhstan occupies an intermediate position within the Eurasian logistics system, outperforming several Central Asian countries but lagging behind China and Turkey in terms of infrastructure quality, logistics competence, tracking and tracing, and customs efficiency. The findings suggest that the competitiveness of multimodal corridors depends not only on transit location, but also on infrastructure quality, service reliability, node connectivity, digitalization, institutional coordination and the efficient integration of transport modes. The study concludes that strengthening Kazakhstan’s logistics position requires an integrated approach combining infrastructure modernization, customs improvements, digital tracking systems and more effective coordination across logistics hubs, operators and transport corridors.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/ffutr.2026.1819917</guid>
        <link>https://www.frontiersin.org/articles/10.3389/ffutr.2026.1819917</link>
        <title><![CDATA[What drives Chinese users’ satisfaction in EV charging? Uncovering and prioritizing service design factors through a hybrid decision approach]]></title>
        <pubdate>2026-06-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ping Fan</author><author>Shuai Yang</author>
        <description><![CDATA[The promotion of electric vehicles development and its supporting industries has emerged as a critical strategy in the global transition toward sustainable mobility. Notably, the inadequate service capacity of charging stations presents an urgent challenge that requires resolution. To address this challenge and improve service quality, this study proposes a hybrid model to investigate factors influencing user experience and to inform charging station design innovations. User demands were first collected through interviews and behavioral observations. An initial index system of these demands was then established using the Analytic Hierarchy Process (AHP), with preliminary weights assigned by expert evaluation. Subsequently, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was employed to assess the causal interrelationships among these demand indicators. The initial weights were then adjusted by integrating the DEMATEL results, yielding a prioritized ranking of user requirements. This study employs the charging stations as a case study to quantitatively validate the findings of the comprehensive weight analysis and to develop a scientifically grounded scheme for charging stations. This composite model effectively addresses issues related to weak element correlation and static weight interference that are often encountered in traditional user demand analyses, thereby enhancing the scientific rigor of the development process and providing a theoretical foundation for charging station design.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/ffutr.2026.1848458</guid>
        <link>https://www.frontiersin.org/articles/10.3389/ffutr.2026.1848458</link>
        <title><![CDATA[Perceived transport infrastructure and service measures for people with disabilities in the City of Tshwane, South Africa]]></title>
        <pubdate>2026-06-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Babra Duri</author>
        <description><![CDATA[IntroductionTransport accessibility is a core component of transport equity, encompassing not only the physical availability of infrastructure but also the quality of service. In South Africa, progressive transport and disability policies emphasize universal access; however, people with disabilities continue to experience significant barriers in everyday mobility. This study aims to identify perceived transport infrastructure and service measures for people with disabilities in the City of Tshwane, South Africa.MethodsA quantitative research design was adopted using a structured questionnaire administered to people with mobility, vision, and hearing impairments. Data were collected from 214 respondents across the City of Tshwane. Respondents rated 16 transport-related measures on a 5-point Likert scale. Data were analyzed using descriptive statistics and exploratory factor analysis (EFA) in SPSS. Reliability testing using Cronbach’s alpha was conducted to assess the internal consistency of the extracted factors.ResultsDescriptive statistics indicated that all transport measures were perceived as important, with intersections with safety devices, reliable transport services, cheaper fares, and reserved seating receiving the highest mean scores. EFA revealed three underlying dimensions: (1) infrastructure and service quality measures; (2) physical accessibility measures; and (3) bus service and awareness measures.DiscussionThe findings highlight that accessibility for people with disabilities is multidimensional and shaped by both infrastructure and service delivery. The results emphasize that transport equity requires integrated interventions that combine service quality improvements, accessible infrastructure, and institutional support. The findings provide user-centered evidence to guide inclusive transport planning and policy development in the City of Tshwane. By highlighting priority measures, this study contributes to the literature on transport equity and accessibility.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/ffutr.2026.1833505</guid>
        <link>https://www.frontiersin.org/articles/10.3389/ffutr.2026.1833505</link>
        <title><![CDATA[Understanding safety perceptions, trust, and acceptance of autonomous shuttles: findings from a UK pre-trial survey]]></title>
        <pubdate>2026-05-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hisham Y. Makahleh</author><author>Tami Kalsi-Rogers</author><author>Matthew Shelton</author><author>Emma J. S. Ferranti</author><author>Christopher D. F. Rogers</author>
        <description><![CDATA[IntroductionAutonomous shuttles (AS) are increasingly being trialled for campus and first-/last-mile transport services. However, their successful adoption depends on public attitudes towards safety, trust, and service reliability. This study examined pre-trial perceptions and willingness to ride (WTR) autonomous shuttles in the United Kingdom.MethodsA pre-trial survey (n = 922) was conducted with travellers at the National Exhibition Centre (NEC), Birmingham, United Kingdom. The survey assessed demographics, travel behaviour, attitudes towards AS, and WTR across four operational scenarios with varying levels of human supervision. Statistical analysis included principal component analysis to identify underlying attitudinal dimensions.ResultsWTR was highest for manned services involving drivers (driver + steward: 95%; driver only: 87%) and declined significantly when human supervision was reduced, with steward-only (65%) and staff-free (31%) operations receiving the lowest levels of acceptance. Two attitudinal components, reflecting psychological perceptions and service/operational expectations, explained 62% of the variance. Travel behaviour findings revealed high levels of car dependency and predominantly solo travel among both staff and visitors. Safety (25%), trust (23%), and reliability (23%) were the most frequently reported barriers, whereas environmental friendliness (33%) and reliability (17%) were the most commonly perceived benefits.DiscussionThe findings suggest that pre-use acceptance of autonomous shuttles is strongly influenced by visible human supervision and confidence in service reliability. Early deployment strategies should therefore prioritise safety assurance, operational reliability, and clear human oversight to support public acceptance and adoption.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/ffutr.2026.1826998</guid>
        <link>https://www.frontiersin.org/articles/10.3389/ffutr.2026.1826998</link>
        <title><![CDATA[Dynamic headway control for capacity optimization: a critical review]]></title>
        <pubdate>2026-05-28T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Zhanibek Shukamanov</author><author>Gulfariza Suleimenova</author>
        <description><![CDATA[Increasing traffic density and the deployment of digital signalling technologies are transforming railway capacity from a static infrastructure constraint into a real-time controllable system-performance variable. This critical review provides a system-level analysis of dynamic headway control and its impact on effective railway capacity within closed-loop operational environments. Modelling paradigms, including analytical methods, microscopic simulation, optimisation-based traffic management, and artificial intelligence approaches, are comparatively assessed with respect to control capability, scalability, real-time applicability, and sensitivity to disturbances. The review shows that conventional planning-oriented capacity assessment systematically overestimates achievable throughput because it neglects traffic stability, disturbance propagation, and feedback-based regulation. The evolution of control architectures from fixed-block signalling to moving-block systems, predictive traffic management, and virtual coupling is interpreted as a transition from infrastructure-limited to control-limited capacity. A unified conceptual framework is proposed that links control architecture, headway dynamics, traffic stability, and effective capacity within a cyber-physical control loop. This perspective enables the identification of key research gaps, including the lack of integrated real-time models and the limited incorporation of artificial intelligence into safety-critical control. Future intelligent railway operation is expected to be based on digital twins, reinforcement learning, and distributed coordination, enabling continuous capacity regulation under stochastic conditions.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/ffutr.2026.1857164</guid>
        <link>https://www.frontiersin.org/articles/10.3389/ffutr.2026.1857164</link>
        <title><![CDATA[Car-free days as sustainable urban transport emission reduction strategies: evidence from Kigali city, Rwanda]]></title>
        <pubdate>2026-05-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Emily Airhart</author><author>Egide Kalisa</author>
        <description><![CDATA[IntroductionCar-Free Days (CFDs) in Kigali, Rwanda, are held twice monthly on Sundays, when motorized vehicle traffic is restricted on major roads from 7:00 AM–11:00 AM to promote physical activity and reduce air pollution. Previous studies assessing the effectiveness of CFDs in reducing air pollution in Kigali have relied solely on short-term, low-cost fine particulate matter (PM2.5) measurements during the intervention period at a single site and have provided no post-intervention rebound assessment. The present study provides the first record of variations in PM2.5 and ozone (O3) to evaluate the effectiveness of CFDs as a sustainable transport emission-reduction strategy. We examine whether reductions in air pollution persist beyond implementation hours and contribute to overall daily improvements in air quality in Rwanda.MethodsContinuous air quality data collected between 2022 and 2024 from two sites were analyzed. Pollutant concentrations during CFD Sundays were compared to normal Sundays across morning intervention (7:00 AM–11:00 AM) and post-intervention (11:00 AM–9:00 PM) periods.Results and discussionResults show that PM2.5 concentrations significantly decreased during CFD hours (−14.8%, P < 0.0001) and remained lower in the post-intervention period (−34.7%; P < 0.0001), with no evidence of rebound. Diurnal profiles showed continued declines after 11:00 a.m. and a suppressed evening peak, indicating a sustained reduction in daily exposure. In contrast, O3 showed no significant differences during CFD hours and increased in the afternoon, consistent with photochemical formation rather than direct traffic emissions. This study highlights that CFDs may serve as an effective, low-cost, and sustainable strategy to reduce transport emissions in Africa, where expensive interventions to reduce air pollution are lacking. These findings also highlight the potential of community-based interventions to enhance urban environmental sustainability and resilience, especially in rapidly urbanizing cities.]]></description>
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