Search Results (2,138)

The growing demand for sustainable urban logistics highlights the need for innovative, low-emission delivery solutions, particularly in the context of small-scale urban food producers. These producers often face logistical challenges in reaching consumers efficiently while minimizing environmental impacts. Green micro-mobility, such as electric cargo bikes and scooters, offers a promising last-mile delivery alternative that aligns with environmental and economic goals. This study addresses the integration of micromobility into urban food logistics, aiming to enhance both efficiency and sustainability. The authors develop a mathematical optimization model that supports real-time decision-making for last-mile deliveries from multiple local food producers to urban customers using micromobility vehicles. The model considers vehicle capacity constraints, and delivery time windows while minimizing greenhouse gas (GHG) emissions and total operational costs. Optimization results based on realistic urban scenario demonstrate that the proposed model significantly reduces GHG emissions compared to conventional delivery methods. Additionally, it enables a more cost-effective and streamlined delivery operation tailored to the specific needs of small producers. The findings confirm that green micromobility-based logistics, supported by optimized planning, can play a crucial role in building cleaner, more resilient urban food distribution systems. Full article

UAV delivery and ground transfer scheduling in emergency scenarios represent critical technological systems for enhancing disaster response capabilities and safeguarding lives and property. This study systematically reviews recent advances across eight core research domains: UAV emergency delivery systems, ground–air integrated transportation coordination, emergency logistics optimization, UAV path planning and scheduling algorithms, collaborative optimization between ground vehicles and UAVs, emergency response decision support systems, low-altitude economy and urban air traffic management, and intelligent transportation system integration. Research findings indicate that UAV delivery technologies in emergency contexts have evolved from single-aircraft applications to intelligent multi-modal collaborative systems, demonstrating significant advantages in medical supply distribution, disaster relief, and search-and-rescue operations. Current technological development exhibits four major trends: hybrid optimization algorithms, multi-UAV cooperation, artificial intelligence enhancement, and real-time adaptation capabilities. However, critical challenges persist, including regulatory framework integration, adverse weather adaptability, cybersecurity protection, human–machine interface design, cost–benefit assessment, and standardization deficiencies. Future research should prioritize distributed decision architectures, robustness optimization, cross-domain collaboration mechanisms, emerging technology integration, and practical application validation. This comprehensive review provides systematic theoretical foundations and practical guidance for emergency management agencies in formulating technology development strategies, enterprises in investment planning, and research institutions in determining research priorities. Full article

To address the challenges of unbalanced demand and high operational costs in highway port logistics, this study investigates the scheduling of tractors and semitrailers under time window constraints in a networked environment, where geographically distributed ports are interconnected by fixed routes, and tractors dynamically transport semitrailers between ports to balance asymmetric demands. A mathematical optimization model is developed, incorporating multiple car yards, diverse transport demands, and temporal constraints. To solve the model efficiently, an Adaptive Large Neighborhood Search (ALNS) algorithm is proposed and benchmarked against an improved Ant Colony System (IACS). Simulation results show that, compared to traditional scheduling methods, the proposed approach reduces the number of required tractors by up to 61% and operational costs by up to 21%, depending on tractor working hours. The tractor-to-semitrailer ratio improves from 1.00:1.10 to 1.00:2.59, demonstrating the enhanced resource utilization enabled by the ALNS algorithm. These findings offer practical guidance for optimizing tractor and semitrailer configurations in highway port operations under varying conditions. Full article

Bruises in beef cattle carcasses are important indicators related to pre-slaughter handling and transport conditions, with implications for animal welfare and meat quality. This study analysed 19.4 million cattle carcasses transported from 42,805 farms to 38 slaughterhouses in Brazil to identify factors associated with bruising. Logistic regression models were used to assess the effects of sex, age, transport distance from farm to industry, and truck class. At least one bruise was found in 33.8% of the analysed carcasses. Older animals had a greater prevalence of bruising, and females were the most affected. The relationship between transport distance and bruising varied across carcass regions, showing distinct patterns rather than a uniform trend. Compared with larger-capacity vehicles, smaller trucks increased the risk of bruising. The round, rump, and flank regions presented the greatest number of bruises. The models assessing individual effects demonstrated good overall performance, with accuracy ranging from 75% to 82% in identifying bruises. The best performance was observed for round-rump, likely due to the higher frequency of bruises in these cuts. These findings highlight the need to improve transport logistics, adopt better handling practices, and implement specific interventions to reduce bruising. Full article

This research considers an Artificial Intelligence (AI)-driven omnichannel logistics network for bioethanol supply from Russia to China. As a renewable, low-carbon transport fuel, bioethanol plays a critical role in energy diversification and decarbonization strategies for both Russia and China. However, its flammability and temperature sensitivity impose stringent requirements on transport infrastructure and supply chain management, making it a typical application scenario for exploring intelligent logistics models. The proposed model integrates information, transportation, and financial flows into a unified simulation framework designed to support flexible and sustainable cross-border (CB) logistics. Using a combination of machine learning, multi-objective evaluation, and reinforcement learning (RL), the system models and ranks alternative transportation routes under varying operational conditions. Results indicate that the mixed corridor through Kazakhstan and Kyrgyzstan achieves the best overall balance of cost, time, emissions, and customs reliability, outperforming single-country routes. The findings highlight the potential of AI-enhanced logistics systems in supporting low-carbon energy trade and CB infrastructure coordination. Full article

This study examines the adoption of real-time visibility (RTV) technologies in the Australian meat cold supply chain, a sector where sustainability challenges such as food spoilage, energy inefficiency, and waste are acute. RTV technologies offer promising solutions by enhancing traceability, operational efficiency, and decision-making across supply chain stages. However, adoption remains uneven due to a range of contextual, organisational, and perceptual factors. Through a nationally distributed quantitative survey targeting stakeholders across inventory, logistics, and retail operations, we identify key drivers and barriers influencing RTV adoption. We explore how demographic factors (e.g., age, role), perceived usefulness and ease of use, and supply chain characteristics interact to shape adoption outcomes. Importantly, the study investigates how horizontal collaboration and data-sharing practices moderate these relationships, especially within the transport and logistics stages where cold chain vulnerabilities are highest. Spearman and partial correlation analyses, alongside binary logistic regression, reveal that perceived ease of use and usefulness are significant predictors of adoption, while barriers such as cost and technical complexity impede it. However, strong collaboration and data-sharing networks can mitigate these barriers and enhance adoption likelihood. Our findings suggest that targeted digital infrastructure investment, workforce training, and policy support for cross-organisational collaboration are essential for advancing sustainability in meat cold chains. This research contributes to a growing body of knowledge that connects technological innovation with food system resilience and waste minimisation. Full article

Active school transportation (AST), including walking or cycling to school, is common among children and youth in India. However, rising air pollution and public health advisories may encourage parents to restrict outdoor activities. The role of parental restrictions on children’s and youths’ participation in AST remains largely unexplored. This study examines how parental restrictions on outdoor activity influence children’s and youths’ engagement in AST. We surveyed children and youth aged 5 to 17 from 41 schools across 28 urban and rural locations in five Indian states, collecting data on AST, parental restrictions, perceptions of air pollution, sociodemographic factors, and school distance. Data were analyzed using multiple logistic regression models, adjusted and unadjusted for children’s and youths’ perceptions of air pollution, segregated by age, gender, and location. Reported parental restrictions due to air pollution were associated with lower odds of engaging in AST overall (OR = 0.625, 95% CI = 0.400–0.971), for ages 5–12 (OR = 0.460, 95% CI = 0.208–0.985, and in urban areas (OR = 0.433, 95% CI = 0.198–0.881). Adjusting for children’s and youths’ air pollution perceptions, these associations persisted in overall and urban analyses. Living over 2 kilometres from school also lowered odds of AST participation (p < 0.05 across all models). The interplay between AST, air pollution, and parental restrictions is self-reinforcing: air pollution can trigger parents to restrict child and youth mobility and reduce AST and, in turn, lower AST may contribute to worsening air quality because of increased motorized transport. Integrated policies are required to simultaneously mitigate pollution and enhance active transportation infrastructure. Full article

This study examines the spatio-temporal evolution of precipitation in the State of Guanajuato, Mexico, from 1981 to 2016 by analyzing monthly series from 65 meteorological stations. A rigorous data quality protocol was implemented, selecting stations with more than 30 years of continuous data and less than 10% missing values. Multiple Imputation by Chained Equations (MICE) with Predictive Mean Matching was applied to handle missing data, preserving the statistical properties of the time series as validated by Kolmogorov–Smirnov tests ($p=1.000$ for all stations). Homogeneity was assessed using Pettitt, SNHT, Buishand, and von Neumann tests, classifying 60 stations (93.8%) as useful, 3 (4.7%) as doubtful, and 2 (3.1%) as suspicious for monthly analysis. Breakpoints were predominantly clustered around periods of instrumental changes (2000–2003 and 2011–2014), underscoring the necessity of homogenization prior to trend analysis. The Trend-Free Pre-Whitening Mann–Kendall (TFPW-MK) test was applied to account for significant first-order autocorrelation (${\rho }_1 > 0.3$) present in all series. The analysis revealed no statistically significant monotonic trends in monthly precipitation at any of the 65 stations ($\alpha =0.05$). While 75.4% of the stations showed slight non-significant increasing tendencies (Kendall’s $\tau$ range: 0.0016 to 0.0520) and 24.6% showed non-significant decreasing tendencies ($\tau$ range: −0.0377 to −0.0008), Sen’s slope estimates were negligible (range: −0.0029 to 0.0111 mm/year) and statistically indistinguishable from zero. No discernible spatial patterns or correlation between trend magnitude and altitude ($\rho =-0.022$, $p>0.05$) were found, indicating region-wide precipitation stability during the study period. The integration of advanced imputation, multi-test homogenization, and robust trend detection provides a comprehensive framework for hydroclimatic analysis in semi-arid regions. These findings suggest that Guanajuato’s severe water crisis cannot be attributed to declining precipitation but rather to anthropogenic factors, primarily unsustainable groundwater extraction for agriculture. Full article

Background: Accurate route planning is a core challenge in logistics, particularly for small- and medium-sized enterprises that lack access to costly geospatial tools. This study explores whether usable distance matrices and routing outputs can be generated solely from geographic coordinates without relying on full map-based infrastructure. Methods: A dataset of over 5000 Hungarian postal locations was used to evaluate five models: Haversine-based scaling with circuity, linear regression, second- and third-degree polynomial regressions, and a trained artificial neural network. Models were tested on the full dataset, and three example routes representing short, medium, and long distances. Both statistical accuracy and route-level performance were assessed, including a practical optimization task. Results: Statistical models maintained internal consistency, but systematically overestimated longer distances. The ANN model provided significantly better accuracy across all scales and produced routes more consistent with map-based paths. A new evaluation method was introduced to directly compare routing outputs. Conclusions: Practical route planning can be achieved without GIS services. ML-based estimators offer a cost-effective alternative, with potential for further improvement using larger datasets, additional input features, and the integration of travel time prediction. This approach bridges the gap between simplified approximations and commercial routing systems. Full article

Rail freight transport represents a key tool for the decarbonisation and greening of logistics chains within the European Union. However, in many Central and Eastern European countries, including the Slovak Republic, a vast network of industrial sidings (rail spurs) remains underutilized or neglected. This reduces the overall efficiency of transport infrastructure and represents a missed opportunity for sustainable transport development. This paper proposes a comprehensive public support model for rail sidings. It combines legislative analysis, a tax incentive mechanism, and analytical evaluation of transport and investment benefits. The methodology calculates the potential transport output of reactivated sidings. It also introduces three quantitative indexes: the Siding Efficiency Index (IEV), the Comprehensive Importance Index (ICV), and the Reactivation Value Index (RVI). These indicators allow for a structured, objective assessment of siding suitability for restoration and public funding. We applied the model to a sample of five sidings in Slovakia, deriving values from expert evaluations. The results show that objective indicators, performance estimates, and targeted public support can identify infrastructure with high revitalization potential. These tools help reintegrate such assets into sustainable transport flows. The analysis indicates that reactivating 5% of existing sidings could shift hundreds of thousands of tonnes of freight annually from road to rail. This change would reduce emissions and improve network efficiency. Full article

In this paper, we develop a dynamic pricing mechanism for a meal delivery platform that offers multiple transportation modes for order deliveries. We consider orders from heterogeneous customers who select their preferred delivery mode based on individual generalized cost (GC) functions, where GC captures the trade-off between price and delivery latency for each transportation option. Given the logistics of the underlying transportation network, the platform can utilize a pricing mechanism to guide customer choices toward delivery modes that optimize resource allocation across available transportation modalities. By accounting for variability in the latency and cost of modalities, such pricing aligns customer preferences with the platform’s operational objectives and enhances overall satisfaction. Due to the computational complexity of finding the optimal policy, we adopt a deep reinforcement learning (DRL) approach to design the pricing mechanism. Our numerical results demonstrate up to $143\%$ higher profits compared to heuristic pricing strategies, highlighting the potential of DRL-based dynamic pricing to improve profitability, resource efficiency, and service quality in on-demand delivery services. Full article

The quality of transport services is not only a measure of operational efficiency but also an important factor of strategic logistics management in the pursuit of sustainable development. This study identifies five key transport service quality indicators (timeliness, routing, economy, safety, efficiency) and uses data from a logistics monitoring system to assess them with Harrington’s desirability function. Each indicator’s performance is converted into a partial desirability score and these scores are combined into a single overall desirability score (D), with weights determined from the data. Notably, a threshold around D = 0.63 emerged as the benchmark for acceptable service quality. This numeric threshold provides managers with a clear KPI target—if the service quality index falls below 0.63, it signals the need for corrective action, whereas consistently achieving values near 0.8 reflects very good performance aligned with strategic sustainability goals. Based on the proposed approach, an algorithm and software tool were developed to automate the assessment process. The obtained results show how improvements in service reliability, safety and efficiency can be aligned with broader sustainability goals in automotive transportation. The proposed approach offers managerial decision makers a robust tool to guide policy and investment, ensuring that enhancements in transport service performance also advance environmental and social sustainability. In doing so, the framework advances SDG 9 by turning logistics telemetry into an actionable management index that strengthens resilient transport infrastructure and fosters practical innovation at the enterprise level. Full article

To achieve the objective of a “15 min living circle” for educational services, this study develops an integrated method for primary school site selection in Tianjin, China, by combining multi-source data and ensemble learning techniques. At a 500 m grid scale, a suitability prediction model was constructed based on the existing distribution of primary schools, utilizing Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models. Comprehensive evaluation, feature importance analysis, and SHAP (SHapley Additive exPlanations) interpretation were conducted to ensure model reliability and interpretability. Spatial overlay analysis, incorporating population structure and the education supply–demand ratio, identified highly suitable areas for primary school construction. The results demonstrate: (1) RF and XGBoost achieved evaluation metrics exceeding 85%, outperforming traditional single models such as Logistic Regression, SVM, KNN, and CART. Validation against actual primary school distributions yielded accuracies of 84.70% and 92.41% for RF and XGBoost, respectively. (2) SHAP analysis identified population density, proximity to other educational institutions, and accessibility to transportation facilities as the most critical factors influencing site suitability. (3) Suitable areas for primary school construction are concentrated in central Tianjin and surrounding areas, including Baoping Street (Baodi District), Huaming Street (Dongli District), and Zhongbei Town (Xiqing District), among others, to meet high-quality educational service demands. Full article

The complex topography of China’s Tibetan Plateau mountainous roads, characterized by diverse curve types and frequent traffic accidents, significantly impacts the safety and sustainability of the transportation system. To enhance driving safety on these mountain roads and promote low-carbon, resilient transportation development, this study investigates the mechanisms through which different curve types affect driving safety and proposes optimization strategies based on interpretable machine learning methods. Focusing on three typical curve types in plateau regions, drone high-altitude photography was employed to capture footage of three specific curves along China’s National Highway G318. Oblique photography was utilized to acquire road environment information, from which 11 data indicators were extracted. Subsequently, 8 indicators, including cornering preference and vehicle type, were designated as explanatory variables, the curve type indicator was set as the dependent variable, and the remaining indicators were established as safety assessment indicators. Linear models (logistic regression, ridge regression) and non-linear models (Random Forest, LightGBM, XGBoost) were used to conduct model comparison and factor analysis. Ultimately, three non-linear models were selected, employing an explainability-oriented dynamic ensemble learning strategy (X-DEL) to evaluate the three curve types. The results indicate that non-linear models outperform linear models in terms of accuracy and scene adaptability. The explainability-oriented dynamic ensemble learning strategy (X-DEL) is beneficial for the construction of driving safety models and factor analysis on Tibetan Plateau mountainous roads. Furthermore, the contribution of indicators to driving safety varies across different curve types. This research not only deepens the scientific understanding of safety issues on plateau mountainous roads but, more importantly, its proposed solutions directly contribute to building safer, more efficient, and environmentally friendly transportation systems, thereby providing crucial impetus for sustainable transportation and high-quality regional development in the Tibetan Plateau. Full article

The logistics industry saw substantial growth in the second half of the 20th century, and logistics companies play a vital role in today’s modern market. Constant shifts in the market present challenges for logistics firms, which must find the optimal balance between achieved goals and utilized resources. The primary indicator that reflects this relationship is efficiency. Measuring and monitoring efficiency in logistics companies is extremely demanding because the final product is not a tangible item; instead, it often consists of transportation, storage, transloading, and forwarding services that require extensive resources. This paper focuses on measuring and improving efficiency. Numerous approaches and methods for evaluating the efficiency of logistics companies are examined. To measure and enhance efficiency, as well as rank companies based on operational efficiency, a three-phase DEA-fuzzy AHP model has been developed. This model was tested using a real-world example by analyzing the efficiency of ten logistics companies in the Republic of Serbia. The results of the analysis indicate the applicability of this model for measuring and improving the efficiency of logistics companies, as well as for their ranking. Full article