Search Results (349)

Road freight transport (RFT) faces growing pressure from increasing freight demand, stricter environmental requirements, and persistent driver shortages. Automation technologies (ATes)—especially semi-autonomous driving—are increasingly viewed as a practical pathway toward improving the sustainability performance of freight operations; however, their effects depend strongly on infrastructure and operational conditions. This study evaluates the sustainability potential of autonomous and semi-autonomous trucks through an integrated framework combining (i) a structured review of technical and regulatory developments, (ii) surveys of transport enterprises (TEes) and road users (RUs), (iii) SWOT/TOWS analysis, and (iv) a cost minimization logistics model that links operational feasibility to infrastructure readiness (IR). The proposed model minimizes cost per tonne-kilometre and introduces an Infrastructure Readiness Score (IRS) to represent the share of a route that can be operated in automated mode; it also accounts for fuel savings from platooning and higher maintenance and capital costs of semi-autonomous vehicles (SAVs). Results indicate that, as IRS increases, semi-autonomous operations achieve higher daily mileage and lower unit costs, with a break-even point at approximately IRS ≈ 0.125. Beyond this threshold, unit costs decline from EUR 0.0433 to EUR 0.0348 per tonne-kilometre as IRS rises toward 0.6, after which further infrastructure improvements yield diminishing mileage gains. These cost and utilization improvements imply sustainability benefits via improved energy efficiency and reduced emissions intensity per tonne-kilometre. Nevertheless, survey evidence highlights major adoption barriers, including insufficient IR, regulatory uncertainty, technological reliability concerns, and limited public trust in fully autonomous systems. Overall, the findings support semi-autonomous trucking as the most feasible near-term stage of transition, while emphasizing that infrastructure upgrades and governance mechanisms are critical for scaling sustainability gains. Full article

Across Latin America, numerous traditional agroecological systems face increasing challenges in integrating into formal markets. This issue is also evident in the Chakra Kichwa, an ancestral agroecological production system primarily managed by Indigenous women. Despite its cultural, environmental, and nutritional significance, its integration into the formal market is hindered by structural limitations that keep producers in conditions of subsistence and marginalization. This study analyzes the economic benefits derived from the commercialization of agricultural products by 642 producers, intermediaries, and vendors who belong to 20 associations based in the rural parishes of Tena canton (Napo Province) and who market their products in urban areas. A total of 234 surveys were conducted, with a 95% confidence level and a 5.2% margin of error. Findings indicate that the sale of Chakra products generates an average monthly net income of $211.06, provided the value of family labor is not accounted for. However, when imputing the monthly cost of this unpaid labor, the system shows losses of $409.48. Additionally, four scenarios are simulated: the first three assess the profitability of the commercial circuit under alternative transportation logistics; the fourth explores potential gains from increased selling prices associated with improvements in infrastructure, inputs, and transportation. In all cases, sales labor is replaced with hired personnel. The results indicate that these scenarios could increase net income by between 28.45% and 92.23%. Nevertheless, when accounting for family labor costs, all scenarios continue to reflect losses. Consequently, the model indicates that achieving economic breakeven would require quadrupling the current productivity of the Chakra system. Full article

Rural logistics faces persistent challenges such as high distribution costs, dispersed demand, and limited transport infrastructure, which hinder efficient last-mile delivery. To address these issues, this study proposes a bus–heterogeneous drone collaborative delivery system that integrates the fixed-route coverage of rural buses with the flexibility of multiple types of drones. The proposed system enables synchronized operations between buses and drones, where buses serve as mobile depots for drone launching and recovery along predefined routes. A mixed-integer programming (MIP) model is developed to jointly optimize bus schedules and drone routing under spatiotemporal synchronization constraints, considering drone endurance, payload capacity, energy consumption, and bus departure times. Due to the NP-hard nature of the problem, an Improved Genetic Algorithm (IGA) is designed, incorporating a three-layer encoding scheme, adaptive crossover and mutation operators, and a local search repair mechanism to enhance convergence and solution feasibility. A real-world case study from Baihe County, Shaanxi Province, China, is conducted to evaluate the performance of the proposed model and algorithm. Comparative experiments under the reported case-study setting show that the proposed bus–heterogeneous drone system achieves notable cost reduction and improved overall delivery performance. Sensitivity analyses further confirm the robustness of the model with respect to drone endurance, drone payload capacity, and bus stop quantity. This research contributes to the literature by bridging the methodological gap between truck–drone coordination and bus-based collaborative delivery, offering an innovative framework for sustainable rural logistics and multi-modal last-mile optimization. Full article

Urban last-mile delivery is an increasingly important source of transport-related emissions, yet evidence on low-altitude logistics under real-order demand and urban spatial constraints remains limited. Taking Shanghai as a representative megacity, this study integrates 185,673 real parcel orders with 3D urban spatial data to develop a unified unmanned aerial vehicle (UAV)–courier carbon accounting framework. The framework combines 3D UAV route-planning algorithms, UAV energy-consumption models, electric courier-vehicle energy models, and grid emission factors to compare carbon emissions between UAV and conventional delivery modes. The results show that, under the modeled operating assumptions, UAV delivery tends to provide lower per-delivery carbon emissions under lightweight and high-speed operating conditions. Scenario analysis further suggests that UAV deployment in Shanghai could reduce carbon emissions by approximately 343,300 t CO₂ annually by 2030. These findings provide quantitative support for urban low-altitude logistics planning, infrastructure deployment, and policy design for low-carbon last-mile delivery. The framework is transferable to other Chinese cities with similar urban conditions, but the numerical results require local recalibration of parcel demand, urban morphology, airspace constraints, and electricity-related carbon factors. Full article

The International Maritime Organization (IMO) established an initial strategy for maritime decarbonization and later specified its long-term target of achieving net-zero strategy by 2050. The institutional framework for mid-term measures was introduced by IMO, and mid-term measures were originally scheduled to be adopted at the end of 2025, but will be re-discussed in 2026 due to opposition from some current member states; South Korea relies on maritime transport for over 99% of its total import/export volume, meaning that the national shipping sector constitutes a core infrastructure supporting trade-driven economic activity. However, mid-term measures are expected to increase logistics costs and weaken route competitiveness and contract markets, affecting individual shipping companies and the entire export–import industrial base. However, quantitative analyses of the cross-industry ripple effects remain limited. Existing studies assess regulatory burdens on shipping but rarely estimate economy-wide spillovers or provide empirical guidance for policy strategies. Therefore, research is needed to move beyond regulatory interpretation, assess domestic response capabilities, and quantitatively analyze the macroeconomic impacts of mid-term measures to support sound policy decision-making. This study aims to quantitatively evaluate the nationwide economic impact of the IMO mid-term measures and propose strategic policy solutions for effective domestic responses. Full article

The expansion of Brazil’s agricultural frontier in Arco Norte has intensified environmental and socioeconomic concerns that may worsen under climate change. This study evaluates how climate-driven shifts in soybean suitability may reconfigure production patterns and affect logistical vulnerabilities. Three scenarios were modeled using the MaxEnt algorithm: a historical baseline (1970–2000) and two future projections (2041–2060) based on the CMIP6 climate pathways. The model integrated bioclimatic, physical, land-use and land-cover, and infrastructure variables. The results showed that soybean expansion was highly concentrated across all scenarios. Mato Grosso, Goiás, and Tocantins accounted for 82.7% to 85.5% of total projected expansion, while Bahia and Maranhão increased this share to more than 92% of total gains. Although consolidated areas absorbed most of the expansion, new frontiers still represented nearly 30% of the total gains. A logistical vulnerability index linked potential expansion areas to grain storage deficits and revealed critical conditions in the main soybean-producing municipalities of Mato Grosso. These findings indicate a growing mismatch between emerging production areas and existing logistics infrastructure, highlighting the need for coordinated investments in storage, intermodal transportation, and territorial planning. Full article

Strategies based on the use of recycled materials have been widely discussed as alternatives to reduce environmental impacts in transport infrastructure. In pavement engineering, the use of Reclaimed Asphalt Pavement (RAP) in base layers offers environmental benefits; however, its benefits depend on processing conditions and structural performance. Chemical stabilization techniques, although mechanically effective, tend to introduce environmental hotspots associated with binder production. In this study, controlled thermal conditioning of RAP is evaluated as a warm base solution without chemical stabilizers in the context of airport pavements. A comparative life cycle assessment was conducted under a production- and construction-stage scope (A1–A3 and A5, excluding transportation under equivalent logistical assumptions), considering untreated RAP, heated RAP, and RAP stabilized with emulsion and cement, and was integrated with mechanistic–empirical structural performance analyses. The results indicate that, although heated RAP presents intermediate absolute environmental impacts due to additional energy consumption, it achieves the highest eco-efficiency, expressed as the lowest ratio between global warming potential (IPCC 2023) and estimated structural service life. In the analyzed scenarios, the warm base showed approximately 71% lower environmental impact per year of service than untreated RAP and about 90% lower than the emulsion-stabilized alternative. These findings suggest that performance-based sustainability assessment can reveal environmental advantages in solutions that exhibit moderate increases in production-stage impacts but enhanced structural longevity. It should be noted that the conclusions are conditioned by the adopted production and construction system boundaries, which do not include the use, rehabilitation, or end-of-life phases. Full article

Dry ports are becoming increasingly important elements of port–hinterland transport systems, particularly as maritime gateways face rising congestion, infrastructure pressure, and coordination challenges within global supply chains. As international trade expands and logistics networks grow more complex, inland terminals are progressively evolving into integrated intermodal platforms that support more efficient freight distribution between seaports and their hinterlands. This study presents a PRISMA-based systematic review of research on dry port–seaport systems covering the period 1980–2025. Following a structured screening and selection procedure, peer-reviewed publications were identified and analyzed to examine conceptual developments, thematic orientations, geographical scope, and decision-making perspectives within the field. Particular attention is given to the growing relevance of digital transformation, including artificial intelligence and machine learning, in shaping future dry port operations and network design. By synthesizing existing contributions and identifying research gaps, this review provides a consolidated understanding of the evolution of dry port research and outlines key directions for advancing sustainable, resilient, and data-driven port–hinterland systems. Full article

Background: To address the critical challenges of hazardous material (HAZMAT) incidents in dense urban areas, this study develops a hybrid framework for spatial emergency response optimization tailored for Intelligent Transport Systems (ITSs). Methods: Our approach integrates the Fuzzy Analytic Hierarchy Process (FAHP) with a rigorous technical benchmarking of multiple navigation APIs to improve routing decisions under volatile Bangkok traffic. By employing a normalized cost function (scale 0–1), we evaluated the performance of localized (Longdo Map) versus global (Google Maps and OpenStreetMap) platforms across day and night scenarios. Results: Experimental results, yielding normalized costs between 0.464 and 0.748, identified Bon Kai as the optimal response node, whereas Chan Road showed the lowest efficiency. Interestingly, OpenStreetMap provided the highest temporal consistency for emergency logistics. Conclusions: These findings offer a practical decision-support tool for authorities, proving that integrated API assessment is essential for building resilient and responsive urban mobility infrastructures. Full article

The rapid diffusion of industry 4.0 technologies has substantially transformed the maritime transportation sectors by enabling data-driven operations, enhanced connectivity, and more intelligent decision-making processes. Digital technologies such as the Internet of Things (IoT), simulation systems, and advanced data analytics are increasingly reshaping operational structures in maritime logistics, positioning technological transformation as a strategic priority for firms. However, the weighting and prioritization of components emerging with industry 4.0 technologies remain an underexplored area in the literature. The primary motivation of this study is to determine the weights of these industry 4.0 components using the Bayesian Best Worst Method (BWM) and to reveal their corresponding credal ranking levels. In this context, the present study aims to evaluate and prioritize the critical industry 4.0 components influencing technological transformation processes using the Bayesian BWM. Bayesian BWM is preferred over alternative Multi Criteria Decision Making (MCDM) approaches due to its ability to explicitly model uncertainty within a probabilistic framework, generate more consistent weighting results, and flexibly incorporate decision-makers’ judgments. The findings reveal that safety and security (0.2945) constitute the most influential main component, underscoring the necessity of robust digital infrastructures and reliable systems within highly digitalized operational environments. Among the sub-components, data privacy (0.1301) demonstrates the highest global weight, highlighting the growing importance of safeguarding sensitive information in data-intensive digital systems. The results further indicate that autonomous operation and coordination play significant roles in facilitating efficient digital operations, particularly through real-time equipment monitoring and IoT-based operational visibility. Moreover, sustainability (0.1968) emerges as the second most important component, suggesting that organizations increasingly assess technological investments not only in terms of operational efficiency but also with respect to long-term resilience. Within this dimension, continuous training (0.0614) is identified as the most influential component, indicating that the success of digital transformation depends not only on technological infrastructure but also on the development of human capabilities. With the increasing digitalization of the maritime industry, protection against cyber threats has become essential for ensuring operational continuity and safeguarding data integrity. In this regard, adopting proactive cybersecurity strategies and continuously monitoring and updating systems are of critical importance. In the digital transformation of maritime transportation, integrating sustainability considerations is essential to ensure long-term operational efficiency and environmental responsibility. These practical implications are particularly relevant for policymakers, port authorities, and shipping companies seeking to enhance both digital capabilities and sustainable performance. Full article

Ports play a pivotal role in global trade but are also associated with significant environmental and social challenges. Despite growing research on green ports, existing studies remain fragmented, with limited integration between technological, environmental, and governance perspectives within the blue economy framework. This review examines the transition from green port initiatives toward integrated blue-economy-oriented port systems by synthesizing recent advances in sustainable maritime infrastructure, smart port technologies, renewable energy integration, and policy frameworks. The analysis reveals three major findings. First, ports are increasingly evolving into energy-integrated hubs, with leading examples adopting shore power systems, renewable energy microgrids, and hydrogen-based infrastructure, thereby contributing to emissions reductions. Second, digitalization through artificial intelligence, IoT, and data-driven logistics significantly enhances operational efficiency, reduces energy consumption, and improves real-time decision-making. Third, effective governance frameworks that combine regulatory measures and incentive-based instruments are critical to accelerating sustainability transitions while ensuring economic competitiveness. In addition, the review highlights the growing integration of biodiversity conservation, marine pollution mitigation, and community engagement into port management strategies, reflecting a shift toward ecosystem-based approaches. Overall, the findings demonstrate that ports are transitioning from conventional logistics hubs into integrated socio-technical systems that enable low-carbon maritime transport while supporting inclusive and resilient coastal development. Full article

Unmanned Aerial Vehicles (UAVs) have been key platforms to perform sensing, analytics and automation across intelligent transportation, construction, smart agriculture, logistics and defense. Generative AI (GenAI) accelerates intelligence of UAVs by creating synthetic data, simulating environments and improving learning with restricted data conditions. When integrated with digital twin and AI frameworks, GenAI enables advanced design, modeling, adaptation and making a decision. In this paper, we survey recent advances for generative AI-enabled UAVs systems and applicable scenarios. Then, we categorize four applicable research branches using generative AI-enabled UAVs for intelligent transportation systems, digital twin and smart infrastructure, smart agriculture, last-mile logistics and delivery. Full article

This study examines the spatio-temporal evolution and restricting mechanisms of agricultural supply chain resilience in the Yangtze River Basin from a gradient perspective. An evaluation index system encompassing the dimensions of the supply side, demand side, circulation side, and support side was developed. The Entropy-Weighted TOPSIS method, kernel density estimation, and obstacle degree model were comprehensively applied to measure and dynamically analyze supply chain resilience across 11 provinces from 2013 to 2023. The findings reveal distinct spatio-temporal evolution patterns: while the overall resilience shows an upward trend, significant gradient disparities exist, with downstream areas exhibiting markedly higher resilience than the mid- and upstream regions. Regarding the restricting mechanisms, the circulation and support sides exhibit higher levels of obstacles, representing key constraints to resilience enhancement. Among these, express delivery volume, freight turnover, and local R&D personnel full-time equivalents are the core obstacle factors affecting resilience. Based on these findings, this study proposes targeted recommendations, including optimizing rural last-mile logistics, upgrading inter-provincial freight hubs, improving rail–water intermodal transport, and strengthening cold-chain infrastructure, as well as implementing differentiated regional strategies and establishing cross-regional coordination mechanisms. These recommendations aim to provide decision-making guidance for enhancing the risk-response capabilities of agricultural supply chains in the Yangtze River Basin and to promote balanced regional development. Full article

Global supply chains face increasing vulnerability to disruptions from geopolitical tensions, natural disasters, and demand shocks. The global trade network for railway vans, critical for transcontinental freight transport, remains understudied despite its foundational role in global logistics. This study addresses the gap in understanding how the railway vans trade network structure evolves and responds to different types of shocks, moving beyond static analyses to capture dynamic vulnerabilities. Using UN Comtrade data (2013–2024), multi-level network analysis examined structural evolution at macroscopic, mesoscopic, and microscopic scales. Three risk propagation models simulated supply disruption, demand shock, and cooperation disruption scenarios to assess systemic vulnerabilities. The network transformed from a polycentric to core-periphery structure, with China dominating exports (67 partners in 2024) and Germany leading European integration. Supply disruptions from Romania and Czechia affected up to 114 countries under low risk absorption capacity (α = 0.1), while demand shocks from the USA impacted 53 countries. The disruption of strategic trade links, such as China–Australia, triggered severe systemic risks. The systemic criticality of risk sources varies by shock type, requiring context-specific resilience strategies. The findings guide policymakers in identifying critical vulnerabilities and designing targeted interventions for enhancing supply chain resilience in infrastructure sectors. Full article

Transportation agencies receive large volumes of free-form public comments describing infrastructure conditions, safety concerns, and service issues. These comments are often processed manually for downstream operational actions, which is time-consuming, inconsistent across reviewers, and difficult to scale, thereby limiting their value for operational decision-making. This study presents a machine learning and Large Language Model (LLM) framework for automated triage of free-form public comments, assigning each report to a three-level hierarchical taxonomy consisting of Category, Subcategory, and Final Decision. The proposed framework uses agency historical data together with retrieval-based evidence, where semantically similar past comments are provided to the LLM as contextual support to better align predictions with agency-specific labeling practices. The framework was evaluated using TF-IDF with Logistic Regression, TF-IDF with Linear SVM, embedding-based kNN with cosine similarity, few-shot LLM prompting, and retrieval-based LLM prompting. Results show that retrieval-based prompting achieved the best overall performance, with the highest accuracy at both the Category and Subcategory levels. At the Final Decision level, retrieval-based prompting slightly outperformed kNN, while few-shot prompting performed worse. Error analysis showed that many misclassifications were semantically plausible alternatives, reflecting the overlap across infrastructure-related complaint categories. When a second candidate label was allowed, further improving performance. Latency analysis also indicated that the framework can process more than 2000 comments in under 30 min, supporting faster and more consistent agency workflows. Full article