Evaluation of light commercial vehicles operation process
in a transport company using the regression modelling method

Owczarek, Paulina; Brzeziński, Marian; Zelkowski, Jarosław

doi:10.17531/ein.2022.3.13

Cited by 4 publications

(4 citation statements)

References 27 publications

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“…In this paper, machine learning models based on three types of decision trees are used to predict the migration risk of industry customers. Decision trees are tools that enable the construction of predictive and descriptive models, providing a more accurate analysis of the functional relationship of variables relative to more simple regression models (Owczarek et. al., 2022).…”

Section: Research Methods -Decision Tree Modelsmentioning

confidence: 99%

Application of machine learning methods to analyze customer migration risk in terms of corporate financial security

Stoica,

Vilau,

Voicu

et al. 2023

SLW

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Effective prediction of customer migration is only possible through knowledge of the customer life cycle, which is characterized by the length of the relationship between buyer and provider, i.e. customer retention. A concept of opposite importance is customer migration, defined as the partial or total abandonment of the products or services offered by a company. Its knowledge and ability to predict it is crucial in terms of ensuring the continued financial security of target companies. The primary objective of this article was to present a method for assessing the risk of telecom industry customer migration using machine learning methods. The objective was achieved through the use of research methods, theoretical deductions such as and induction, system analysis and synthesis, and mathematical modelling, which additionally allowed for a practical analysis of the migration of customers of the telecommunications industry. Predictors with the greatest impact on the phenomenon under study were selected. It should be noted that the gain chart indicates that, in the case of contacting the 20% of customers selected by the models, the target coverage would be at the following levels, respectively: 70% for the boosted tree model and the decision tree based on the CART algorithm, and 75% for the random forest model. The research niche addressed in the article is the development of methods for assessing migration risk using machine learning techniques. The tool developed in the article can support decision-making in the creation of marketing campaigns aimed at retaining the largest number of customers.

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Section: Research Methods -Decision Tree Modelsmentioning

confidence: 99%

Application of machine learning methods to analyze customer migration risk in terms of corporate financial security

Stoica,

Vilau,

Voicu

et al. 2023

SLW

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show abstract

“…Machine learning models based on support vector machines [9,22], neural networks [23], or decision trees [24] are widely used. Some studies also use fuzzy logic algorithms [25] or classical regression for modeling [26].…”

Section: Literature Review On Fuel Consumption Modelingmentioning

confidence: 99%

Modeling Vehicle Fuel Consumption Using a Low-Cost OBD-II Interface

Rykała,

Grzelak,

Rykała

et al. 2023

Energies

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As a result of ever-growing energy demands, motor vehicles are among the largest contributors to overall energy consumption. This has led researchers to focus on fuel consumption, which has important implications for the environment, the economy, and geopolitical stability. This article presents a comprehensive analysis of various fuel consumption modeling methods, with the aim of identifying parameters that significantly influence fuel consumption. The scientific novelty of this article lies in its use of low-cost technology, i.e., an OBD-II interface paired with a mobile phone, combined with modern mathematical modeling methods to create an accurate model of the fuel consumption of a vehicle. A vehicle test drive was performed, during which variations in selected parameters were recorded. Based on the obtained data, a model of the vehicle’s fuel consumption was built using three forecasting methods: a multivariate regression model, decision trees, and neural networks. The results show that the multivariate regression model obtained the lowest MSE, MAR, and MRSE coefficients, indicating that this was the best forecasting method among those tested. Sufficient forecast error results were obtained using neural networks, with increases of approximately 73%, 10%, and 131% in MSE, MAE, and MRAE, respectively, compared to regression results. The worst results were obtained with the decision tree model, with increases of approximately 163%, 21%, and 92% in MSE, MAE, and MRAE compared to the regression results.

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“…Undoubtedly, the processes of operation of exploitation systems, in which decisions made by humans are an important factor, constitute a difficult area of knowledge and formal description. The principles of functioning of systems are difficult to describe using mathematical functions (Owczarek, Brzeziński et al 2022). There is no simple dependence of the evaluation parameters on the extracted forces for the studied processes.…”

Section: The System Of Exploitation Of Military Technique and Its Res...mentioning

confidence: 99%

Analysis and evaluation of the technical equipment maintenance system in the Polish Army

Pszczółkowski,

Goliasz

2023

SLW

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Utrzymanie wysokiego poziomu gotowości operacyjnej wojska wymaga zapewnienia stałej wysokiej gotowości technicznej sprzętu stanowiącego jego wyposażenie. Niezawodność urządzenia technicznego wynika z jego cech konstrukcyjnych i eksploatacyjnych, a w fazie eksploatacji jest zależna wyłącznie od sprawności działania systemu zaopatrzenia logistycznego, obsługiwania, w tym diagnozowania. Zasady realizacji tych procesów i funkcjonowania systemów determinuje przyjęta strategia eksploatacji sprzętu. W opracowaniu przedstawiono wyniki oceny funkcjonowania systemu obsługiwania sprzętu technicznego w wojsku polskim. Scharakteryzowano metody, procedury i narzędzia pozyskiwania danych dla realizacji postawionego celu, metody przetwarzania i opracowania danych oraz analizy uzyskanej informacji. Informacje i dane charakteryzujące funkcjonowanie systemu obsługiwania uzyskano w wyniku zastosowania techniki wywiadu z ekspertami dysponującymi wiedzą teoretyczną jak i praktyczną o funkcjonowaniu systemu w siłach zbrojnych, za pomocą techniki obserwacji, analizy dokumentacji eksploatacyjnej w formie papierowej, jak również zgromadzonej w systemach informatycznych. Podstawowe znaczenie w procesie badawczym miała metoda ankietowania dwóch grup ekspertów, uczestników kursu logistycznego związanych z realizacją i zarządzających eksploatacją sprzętu. Do opracowania uzyskanych wyników liczbowych i lingwistycznych wykorzystano narzędzia informatyczne w postaci MS Excel, MatLab i Statistica. W opracowaniu wyznaczono parametry statystyki opisowej, histogramy i rozkłady prawdopodobieństwa oraz wykorzystano logikę rozmytą. W wyniku przeprowadzonego badania ujawniono przyczyny aktualnie nie w pełni zadowalającego poziomu funkcjonowania systemu utrzymania stanu technicznego sprzętu oraz wskazano możliwe do zastosowania rozwiązania pozwalające na podniesienie sprawności i efektywności systemu, a tym samym gotowości technicznej sprzętu.

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Evaluation of light commercial vehicles operation process in a transport company using the regression modelling method

Cited by 4 publications

References 27 publications

Application of machine learning methods to analyze customer migration risk in terms of corporate financial security

Application of machine learning methods to analyze customer migration risk in terms of corporate financial security

Modeling Vehicle Fuel Consumption Using a Low-Cost OBD-II Interface

Analysis and evaluation of the technical equipment maintenance system in the Polish Army

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