Search Results (10)

The freight transportation system faces complex operations globally to meet customer demands. Intense competition prompts companies to enhance performance. Transportation modes (road, sea, air) impact service levels, each with distinct features, benefits, costs, environmental effects, and societal risks. Shippers confront challenges in mode selection due to numerous factors, compounded by an increase in low-volume, high-frequency shipments. Rising logistics costs for a few products of exporters affect the socio-economic situation of a country. This research introduces a hybrid approach for a shipment selection model, focusing on pharmaceutical drugs. Utilizing ma- chine learning algorithms (decision tree, Random Forest, logistic regression, XGboost, SVM) and multi-criteria decision-making methods (SAW, MARCOS, TOPSIS, MULTIMOORA, VIKOR), this study predicts the optimal shipping method (land, air, sea) based on dataset features (shipping cost, origin-destination, cargo weight, dimensions). Evaluation metrics include F1 score, Recall, Precision, and Accuracy score. XGboost stands out as the optimal algorithm, demonstrating an accuracy of eighty-four percent, with random forest, decision tree, SVM, and logistic regression following in descending order. This comprehensive approach addresses the complexities of pharmaceutical shipment selection, considering various influential factors. Full article

Integrating advanced air mobility (AAM) into the logistics of high-value electronic commodities can enhance efficiency and promote sustainability. The objective of this study is to optimize the logistics network for high-value electronics by integrating AAM solutions, specifically using heavy-lift cargo drones for middle-mile transport and using the mostly rural and small urban U.S. state of North Dakota as a case study. The analysis utilized geographic information system (GIS) and spatial optimization models to strategically assign underutilized airports as multimodal freight hubs to facilitate the shift from long-haul trucks to middle-mile air transport. Key findings demonstrate that electronics, because of their high value-to-weight ratio, are ideally suited for air transport. Comparative analysis shows that transport by drones can reduce the average cost per ton by up to 60% compared to traditional trucking. Optimization results indicate that a small number of strategically placed logistical hubs can reduce average travel distances by more than 13% for last-mile deliveries. Cost analyses demonstrate the viability of drones for middle-mile transport, especially on lower-volume rural routes, highlighting their efficiency and flexibility. The study emphasizes the importance of utilizing existing infrastructure to optimize the logistics network. By replacing truck traffic with drones, AAM can mitigate road congestion, reduce emissions, and extend infrastructure lifespan. These insights have critical implications for supply chain managers, shippers, urban planners, and policymakers, providing a decision support system and a roadmap for integrating AAM into logistics strategies. Full article

Childhood asthma is a major health issue in Australia, and traffic emissions play a causative role. Two urban planning policies that impact children’s exposure to traffic emissions are considered in terms of the potential health risks to children in a Melbourne suburb with high truck volumes and hospital attendances for childhood asthma. Firstly, the health impact assessment component of the state planning approval of a major road project, and secondly, local government placement of childcare centres and schools in relation to freight routes. Three sources of air quality monitoring data were examined: (i) a Victorian EPA reference site; (ii) a site with planning approval for development into a childcare centre; and (iii) five sites within the boundary of the West Gate Tunnel Project, an AUD 10 billion road and tunnel project. The Australian Urban Research Infrastructure Network data was utilised to assess distances of childcare centres and schools from major truck routes. A range of cconcentration–response functions for childhood asthma (0–18 years) from international systematic meta-analyses and a smaller Australian cross-sectional study were applied to comparative elevations in fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) concentrations between the EPA reference monitor (used for project risk assessment) and local roadside data. It was found that comparative elevations in NO₂ concentrations were associated with the following risk increases: developing asthma 13%, active asthma 12%, and lifetime asthma 9%. Overall, 41% of childcare centres (n = 51) and 36% of schools (n = 22) were ≤150 m to a high-density truck route. Truck emissions likely make a substantial contribution to childhood asthma outcomes in the project area. This study exemplifies how current practices may not be commensurate with guiding policy objectives of harm minimisation and equitable protection. Full article

The sustainability of large economies is one of the most important challenges in today’s world. As the world strives to create a greener and more efficient future, it becomes necessary to accurately analyze and forecast freight volumes. By developing a reliable freight transportation forecasting model, the authors will be able to gain valuable insights into the trends and patterns that determine the development of economic systems. This will enable informed decisions on resource allocation, infrastructure development, and environmental impact mitigation. Such a model takes into account various factors such as market demand, logistical capabilities, fuel consumption, and emissions. Understanding these dynamics allows us to optimize supply chains, reduce waste, minimize our carbon footprint, and, ultimately, create more sustainable economic systems. The ability to accurately forecast freight volumes not only benefits businesses by enabling better planning and cost optimization but also contributes to the overall sustainable development goals of society. It can identify opportunities to shift to more sustainable modes of transportation, such as rail or water, and reduce dependence on carbon-intensive modes, such as road or air. In conclusion, the development and implementation of a robust freight forecasting model is critical to the sustainability of large-scale economic systems. Thus, by utilizing data and making informed decisions based on these forecasts, it is possible to work toward a more sustainable future for future generations. Full article

The novel coronavirus (COVID-19) epidemic broke out in Wuhan at the end of 2019 and spread around the whole of China in 2020. In order to reduce the spread of COVID-19, transportation and industrial activities in different regions were limited to varying degrees. This study uses bivariate concentration polar plots, integrated with k-means clustering and temporal variation analyses for PM_2.5 time series data, to understand the PM_2.5 source characteristics in Shanghai during the COVID-19 pandemic in the winter of 2020. Our findings show that 34.33% of the PM_2.5 particles arise from external sources while 65.67% are from local sources. The results of source apportionment combined with land use, wind speed, and direction data are further used to locate the most likely directions of different source categories and geographic origins of PM_2.5. During the lockdown period in 2020, traffic and industrial activity were still primary local sources of PM_2.5 emissions in Shanghai. The growth of motor vehicle ownership, limited public transport, and a large volume of freight transport in Shanghai result in a higher level of PM_2.5 concentrations on weekends than in midweeks. On the other hand, the regional-scale transport of air pollutants from the Yangtze River Delta, the Central Plains, the inland area of northern China, and coastal cities in the north and south of Shanghai aggravates PM_2.5 pollution in Shanghai under unfavorable meteorological conditions. The methods and results presented here lay a basis for further study on the complicated effects of meteorological and anthropogenic factors on PM_2.5 pollution and on the development of detailed and urgent strategies for the improvement of air quality. Full article

As freight volumes increase, airports are likely to require additional infrastructure development, increased air services, and expanded facilities. Prediction of freight volumes could ensure effective investment. Among the computational intelligence models, support vector regression (SVR) has become the dominant modeling paradigm. In this study, a fuzzy-based SVR (FSVR) model was used to solve the freight volume prediction problem in international airports. The FSVR model can use a fuzzy time series of historical traffic changes for predictions. A fuzzy classification algorithm was used for elements of similar levels in the time series to appropriately divide traffic changes into fuzzy sets, generate membership function values, and establish a fuzzy relationship to produce a fuzzy interpolation with a minimal error. A comparison of the FSVR model with other models revealed that the FSVR model had the lowest mean absolute percentage error (all < 2.5%), mean absolute error, and root mean square error for all types of traffic at all the analyzed airports. Fuzzy sets can handle uncertainty and imprecision in time series. Therefore, the prediction accuracy of the entire time series model is improved by taking advantage of SVR and fuzzy sets. By using the highly accurate FSVR model to predict the future growth of air freight volume, airport management could analyze their existing facilities and service capacity to identify operational bottlenecks and plan future development. The FSVR model is the most accurate forecasting model for air traffic forecasting. Full article

For decades, trade between Europe and China has grown consistently, which has resulted in increased container transportation volumes. Such transportation has been dominated by sea-based options. However, over the years, an air-based mode of transport was developed, while it has lately become increasingly popular to use railways utilizing the Trans-Siberian land bridge. This latter approach boomed amid the COVID-19 crisis in 2020. However, the railway container boom in Eurasia has deeper roots than just the COVID-19 era. As is illustrated in this research work, international trade containers (trade between Russia and other countries, mostly China) and transit containers (e.g., serving the Chinese–EU route) were already showing some significance as early as 2003–2004. In 2020, their volume was already measured in the millions, regardless of the railway data source being used. This is well above the starting period in the 1980s and 1990s, when total annual volumes were around 0.1 million twenty-foot equivalent units (TEU). Container capacity has developed over the years, first being used for international trade and only lately for transit. As a preliminary comparison to air freight, the growth rate was roughly double that in the two-decade observation period. Full article

The section of Via Baltica going through the territory of the Republic of Lithuania is the most traffic intensive land logistics corridor in the country. The annual transportation volume has been increasing on this road; thus, the reduction of pollution caused by vehicles has become important. If gas emissions are regulated, and carriers have to pay pollution taxes, this does not apply to noise levels. The article presents the traffic intensity in this logistics corridor, measurements of the noise level at the characteristic points, its relation to the number of vehicles passing through it and an expert evaluation of proposed methods for noise energy reduction. Environmental noise is an unwanted or harmful sound that propagates in terms of both duration and geographical coverage. Noise is associated with many human activities, but road, rail and air traffic noises have the greatest impact. Due to irrationally arranged transport network, the transit flow of freight transport crosses residential areas of the city, places of rest and recreation of the population, causing high noise levels in adjacent areas. This is the biggest problem for the urban environment. Environmental noise affects many Europeans and is therefore considered by society to be one of the biggest environmental problems. This article presents an assessment of a new traffic noise algorithm. The presented expert survey on noise energy reduction allows choosing the most appropriate method for reducing noise energy in Via Baltica transport logistics corridor. Based on the expert survey, a hierarchical table for noise energy reduction was compiled. It will allow assessing the validity of individual noise energy reduction solutions. It has become relevant for improving infrastructure of other transport corridors and choosing the most appropriate solutions to reduce vehicle noise pollution. A further application of this model can be focused on economic evaluation, forecasting of expected benefits and so on. Full article

Freight transportation modes consume enormous amounts of energy. This paper estimates the long- and short-run effects of fuel prices on freight volumes in various modes of transportation in Shanghai. Data included monthly changes during the period 2009–2016. Air cargo series were suggested to include one or two unit roots, and hence were removed from the cointegration analysis. Both the Phillips–Ouliaris and Johansen tests did not detect long-run relationships between real fuel prices, water cargo, road, and rail freight. Conventional first-differenced VAR (vector autoregressive) models were estimated. Overall, whether in the short- or long-run, real fuel prices did not influence freight transportation volumes. However, we found a Granger causality running from rail to road freight, as in the short-run (one month), a 1% change in rail freight would lead to a reduction of 0.07% in road freight. Therefore, simply by increasing fuel prices, the government could seldom encourage the shift from energy-inefficient modes of freight transportation to energy-efficient ones to achieve a sustainable freight transport. The allocation of more time and routes for rail freight traffic and the reduction in rail freight taxes may increase the rail freight volume and hence decrease the overall energy use. Our findings, to some degree, contribute to freight transportation economics. Future research may examine the impact of gasoline prices or diesel prices on freight traffic volumes. Full article

Electrification of the transport sector has been pointed out as a key factor for tackling some of today’s main challenges, such as global warming, air pollution, and eco-system degradation. While numerous studies have investigated the potential of electrifying passenger transport, less focus has been on how road freight transport could be powered in a sustainable future. This study looks at Electric Road Systems (ERS) in comparison to the current diesel system. The Framework for Strategic Sustainable Development was used to assess whether ERS could be a stepping stone on the way towards sustainability. Strategic life-cycle assessment was applied, scanning each life-cycle phase for violations against basic sustainability principles. Resulting sustainability “hot spots” were quantified with traditional life-cycle assessment. The results show that, if powered by renewable energy, ERS have a potential to decrease the environmental impact of freight transport considerably. Environmental payback times of less than five years are achievable if freight traffic volumes are sufficiently high. However, some severe violations against sustainability principles were identified. Still, ERS could prove to be a valuable part of the solution, as they drastically decrease the need for large batteries with high cost and sustainability impact, thereby catalyzing electrification and the transition towards sustainable freight transport. Full article