Characterizing the variation of particles in varied sizes from a container truck in a port area

Zhao, Hongmei; He, Hong-di; Lu, Wangtao; Hao, Yangyang

doi:10.1007/s10661-020-08752-x

Cited by 2 publications

(2 citation statements)

References 57 publications

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“…Moreover, using two real container terminals, i.e., the port of Altamira (Mexico) and the port of Genoa (Italy) as case studies, the authors of reference [51] proposed a methodological framework to reduce empty truck trips to minimize the deviation from their preferred time slots and turnaround times in container terminals and reduce emissions. Te authors of reference [52] studied the relationship between trafc volume and the particle number concentrations (PNC) caused by emissions of container trucks in the port of Waigaoqiao (China). For this, they combined a machine learning technique with statistical methods to characterize the variation of particles in the port area.…”

Section: Landside Areamentioning

confidence: 99%

“…Te authors reported the beneft of PCA for reducing the dimensions of image features. Moreover, to identify the relationship between the trafc and particle number concentrations (PNC) data from container truck emissions in the yard, the authors of reference [52] applied the PCA and proposed container truck volume, other vehicles' volume, and PNC data as uncorrelated variables for characterizing the variation of particles. Tey found that the method had a high performance in dimensionality reduction when combined with a Pearson correlation analysis.…”

Section: Unsupervised Learningmentioning

confidence: 99%

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Machine Learning for Promoting Environmental Sustainability in Ports

Mansoursamaei

Moradi

González-Ramírez

et al. 2023

Journal of Advanced Transportation

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Maritime transportation is one of the essential drivers of the global economy as it enables both lower transportation costs and intermodal operations across multiple forms of transportation. Maritime ports are essential interfaces that support cargo handling between sea and hinterland transportation. Besides, in this area, environmental protection is becoming extremely important. Global warming, air pollution, and greenhouse gas emissions are all having a detrimental influence on the environment and will most likely continue to do so for future generations. Hence, there is a growing need to promote environmental sustainability in maritime-based transportation. The application of machine learning (ML), as one of the main subdomains of artificial intelligence (AI), can be considered a component within the process of digital transformation to advance green activities in maritime port logistics. Thus, this article presents the results of a systematic literature review of the recent literature on machine learning for promoting environmentally sustainable maritime ports. It collects and analyses the articles whose contributions lie in the interplay between three main dimensions, i.e., machine learning, port-related operations, and environmental sustainability. Throughout a review protocol, this research is constituted on the major focuses of impact, problems, and techniques to discern the current state of the art as well as research directions. The research findings indicate that the articles using polynomial regression models are dominant in the literature, and the recurrent neural network (RNN) and long short-term memory (LSTM) are the most recent approaches. Moreover, in terms of environmental sustainability, emissions and energy consumption are the most studied problems. mAccording to the research gaps observed in the review, two broad directions for future research are identified: (i) altering attention on a greater diversity of machine learning approaches for promoting environmental sustainability in ports and (ii) leveraging new outlooks to perform more green practical works on port-related operations.

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Section: Landside Areamentioning

confidence: 99%

Section: Unsupervised Learningmentioning

confidence: 99%