Profit margin prediction in sustainable road freight transportation using machine learning

Budak, Ayşenur; Sarvari, Peiman Alipour

doi:10.1016/j.jclepro.2021.127990

Cited by 20 publications

(11 citation statements)

References 49 publications

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“…Economic inequality can lead to adverse outcomes such as poverty, unemployment and limited access to fundamental necessities including health care and education (Hidalgo et al, 2020). Various studies have examined the effects of reducing inequality on global poverty (Lakner et al, 2022), the prediction of sulphur dioxide emissions and regulation of air quality (Ribeiro, 2021), the prediction of profit margins in sustainable road freight transportation (Budak and Sarvari, 2021), and computational modelling techniques for wet waste valorization (Li et al, 2022a(Li et al, , 2022b(Li et al, , 2022c(Li et al, , 2022d. Achieving a balance between social, economic and environmental sustainability necessitates the development of inclusive, safe and resilient cities where the actions of one community do not adversely affect others (Li et al, 2019).…”

Section: Leveraging Technology Formentioning

confidence: 99%

Machine learning for sustainable development: leveraging technology for a greener future

Kagzi,

Khanra,

Paul

2023

JSIT

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Purpose From a technological determinist perspective, machine learning (ML) may significantly contribute towards sustainable development. The purpose of this study is to synthesize prior literature on the role of ML in promoting sustainability and to encourage future inquiries. Design/methodology/approach This study conducts a systematic review of 110 papers that demonstrate the utilization of ML in the context of sustainable development. Findings ML techniques may play a vital role in enabling sustainable development by leveraging data to uncover patterns and facilitate the prediction of various variables, thereby aiding in decision-making processes. Through the synthesis of findings from prior research, it is evident that ML may help in achieving many of the United Nations’ sustainable development goals. Originality/value This study represents one of the initial investigations that conducted a comprehensive examination of the literature concerning ML’s contribution to sustainability. The analysis revealed that the research domain is still in its early stages, indicating a need for further exploration.

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Section: Leveraging Technology Formentioning

confidence: 99%

Machine learning for sustainable development: leveraging technology for a greener future

Kagzi,

Khanra,

Paul

2023

JSIT

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“…In addition, [26] presented a novel container transport optimisation model that incorporates the road network along with connectivity metrics, aiming to minimise total trip distance, truck fuel cost, container rental cost, and container movements between multiple consignees and haulers. More recently, [27] proposed a novel approach to predict the profit margin, on a customer basis, in the sustainable road freight transport sector by combining different ML methods. This helps managers to obtain useful information on strategic and sustainable development perspectives.…”

Section: Previous Work On Digitisationmentioning

confidence: 99%

An Intelligent Visualisation Tool to Analyse the Sustainability of Road Transportation

et al. 2022

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Road transport is an integral part of economic activity and is therefore essential for its development. On the downside, it accounts for 30% of the world’s GHG emissions, almost a third of which correspond to the transport of freight in heavy goods vehicles by road. Additionally, means of transport are still evolving technically and are subject to ever more demanding regulations, which aim to reduce their emissions. In order to analyse the sustainability of this activity, this study proposes the application of novel Artificial Intelligence techniques (more specifically, Machine Learning). In this research, the use of Hybrid Unsupervised Exploratory Plots is broadened with new Exploratory Projection Pursuit techniques. These, together with clustering techniques, form an intelligent visualisation tool that allows knowledge to be obtained from a previously unknown dataset. The proposal is tested with a large dataset from the official survey for road transport in Spain, which was conducted over a period of 7 years. The results obtained are interesting and provide encouraging evidence for the use of this tool as a means of intelligent analysis on the subject of developments in the sustainability of road transportation.

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“…One of machine learning's most prominent tools is the neural network (NN). In civil engineering, NNs have been employed to replace analysis and simulations when the data are sufficient; however, even with limited data, reliable models of substantial relative accuracy can be obtained [12][13][14][15][16][17]. From structural engineering to multi-scale modeling and geotechnical engineering, as well as hydraulics and transportation, all subtopics of infrastructure design have been employing NNs to reduce the analyses needed for the design and response prediction of physical and mechanical systems.…”

Section: Introductionmentioning

confidence: 99%

An Estimation of Clayey-Oriented Rock Mass Material Properties, Sited in Koropi, Athens, Greece, through Feed-Forward Neural Networks

Savvides,

Antoniou,

Papadopoulos

et al. 2023

Geotechnics

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Rock mechanics and the estimation of their material properties through field tests are important aspects and challengees in civil and geotechnical engineering. However, this procedure is expensive and difficult to attain, while the machine learning and neural network theory provide a computational tool for estimating the material properties with limited data. In this work, an estimation of the Young Modulus and the cohesion of a clayey-originated rock through feed-forward neural networks constructed from in situ data measurements is given. The input values come from the Geological Strength Index (GSI) proposed values of the point load index Is50, the uniaxial compression strength σs, as well as the specific gravity γ of the rock mass. The convergence analysis revealed that the convergence occurs at approximately 2000 epochs, with the largest L2 mean square error norm being no greater than 10−5. In addition, it is demonstrated that augmenting γ results in the estimation of rock that is stiffer and stronger. The aforementioned increase in the specific site may be up to 20% for the stiffness and up to 25% for the cohesion. This model, aside from readability and accuracy, offers the convenience of enriching it with more in situ data, thereby enhancing the flexibility of the proposed numerical tool proposed. However, its applicability is limited to the specific data acquired from the particular site, so a more general estimation requires a substantially larger dataset. Finally, the justification of the proposed model has been carried out based on suggestions from the literature for common values of clayey-oriented rock, which is fairly disintegrated as seen in the field.

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Profit margin prediction in sustainable road freight transportation using machine learning

Cited by 20 publications

References 49 publications

Machine learning for sustainable development: leveraging technology for a greener future

Machine learning for sustainable development: leveraging technology for a greener future

An Intelligent Visualisation Tool to Analyse the Sustainability of Road Transportation

An Estimation of Clayey-Oriented Rock Mass Material Properties, Sited in Koropi, Athens, Greece, through Feed-Forward Neural Networks

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