The COVID-19 pandemic has caused significant disruptions in the freight transport sector. The number of studies on the impact of COVID-19 on freight transport and possible mitigation strategies are growing. However, a systematic and comprehensive review highlighting the research themes, main findings, research methods, and future research directions of these studies remains scarce. Therefore, this study presents a mixed review comprising scientometric and systematic reviews to cover these research gaps. Results show that 68 studies have been published on this topic since the beginning of 2020 and that they cover three main themes: the impacts of COVID-19 on freight transport, mitigation strategies, and recovery during and after COVID-19. In addition, we describe the research methods, main findings, and possible research directions in each of them. Thus, the findings of our work present both theoretical and practical analyses of COVID-19-related research on freight transport and provide important future research directions in this domain.
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http://dx.doi.org/10.3390/ijerph191912287 | DOI Listing |
PLoS One
January 2025
School of Economics and Management, Yunnan Normal University, Kunming, China.
The development of cross-border e-commerce platform promotes the new channel model between domestic and international. How to determine the dual-channel pricing decision of manufacturers and retailers under the condition of tariff and transportation heterogeneity has become an important and realistic problem. Based on the perspective of cross-border e-commerce dual-channel supply chain, this paper considers the impact of import tariff, transport heterogeneity and export tax rebate, compares and analyzes the performance difference between decentralized decision-making and centralized decision-making, and analyzes the impact of import tariff, export tax rebate and transport heterogeneity on cross-border e-commerce dual-channel pricing, demand and profit.
View Article and Find Full Text PDFEntropy (Basel)
December 2024
School of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China.
Diagnosing faults in wheelset bearings is critical for train safety. The main challenge is that only a limited amount of fault sample data can be obtained during high-speed train operations. This scarcity of samples impacts the training and accuracy of deep learning models for wheelset bearing fault diagnosis.
View Article and Find Full Text PDFPLoS One
December 2024
College of Engineering, Northeast Agricultural University, Harbin, Heilongjiang, China.
Current research on building carbon emissions primarily focuses on various carbon emission assessment models and the use of life cycle analysis to evaluate overall building carbon emissions, with limited attention given to excavation engineering. Based on the life cycle method and process analysis, this study analyzes carbon emissions in excavation engineering by optimizing the evaluation model for fuel consumption standards of freight vehicles during the transportation phase in China. To account for the difference between actual and rated fuel consumption of transport vehicles, factors such as road conditions, traffic congestion, and temperature are introduced to adjust the carbon emission calculation model for the transportation phase.
View Article and Find Full Text PDFEnviron Monit Assess
December 2024
Department of Civil Engineering, National Institute of Technology, Warangal, 506004, Telangana, India.
Rapid urbanization has led to unplanned settlements near railway lines, exposing residents to continuous noise pollution with potential adverse effects on health. This study focuses on the environmental monitoring and assessment of railway noise pollution in urban areas and its impact on human health and daily activities. Noise levels were quantified across different residential zones using precise sound level meters, and a detailed human perception survey was conducted to assess the relationship between noise exposure, annoyance, and health disturbances.
View Article and Find Full Text PDFSci Prog
January 2024
School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China.
With the continuous improvement in the efficiency of the heavy-haul railway freight transportation, the pressure on on-site maintenance is increasing. In-depth research on fault characteristics carries significant importance for fault scientific judgment and fault prevention. This study proposes an efficient association rule mining (ARM) algorithm, HM-RDHP, for analyzing fault data from heavy-haul railway freight trains.
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