Forecasting fuel consumption in means of transport with the use of machine learning

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dc.citation.epage9
dc.citation.issue2
dc.citation.spage1
dc.contributor.affiliationSilesian University of Technology
dc.contributor.authorBudzyński, Artur
dc.contributor.authorSładkowski, Aleksander
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2023-02-09T14:47:26Z
dc.date.available2023-02-09T14:47:26Z
dc.date.created2022-03-01
dc.date.issued2022-03-01
dc.description.abstractТранспорт є ключовим чинником, який впливає на викиди парникових газів. У зв’язку з цим, наведено проблеми та виклики, з якими зустрічається транспортна галузь. Розглянуто питання транспортної галузі, пов’язані з Європейською зеленою угодою. Обговорено, наскільки транспортна система є важливою для європейських компаній та глобальних ланцюгів постачання. Проаналізовано також питання, які мають вплив на суспільство з точки зору витрат коштів, зокрема викиди парникових газів та забруднення довкілля. У статті висвітлено матеріали управління транспортними процесами на підприємстві. Прийнято рішення дослідити витрати палива видами транспорту. На основі огляду літературних джерел, визначено 3 категорії характеристик: характеристики автомобілів, водіїв, а також вплив маршруту на витрати палива. Дослідження виконано на основі даних архівів GPS системи моніторингу автомобілів. Вони зібрані на 1890 маршрутах, які здійснювали рух між 30 травня 2020 року та 31 травня 2021 року. На маршрутах працювали 29 водіїв та 8 транспортних засобів. Транспортні засоби – це 40-тонні тягачі з напівпричепами. Наведено аналіз чинників, які впливають на споживання палива. Описано методику отриманих інженерних функцій. Описано переваги методу зменшення споживання палива. Вказано на можливості використання методів прогнозування витрати енергії та водню на різних видах транспорту, включно з громадським транспортом. Дані опрацьовано з використанням бібліотеки “Pandas”. Порівняння моделей виконано з використанням середньої абсолютної похибки. Представлено застосування методів роботи з великими наборами даних. Розрахунки проведено з допомогою бібліотеки “NumPy”. Візуалізація даних – за допомогою моделей “Matplotlib” та “Seaborn. Scikit-Learn”.
dc.description.abstractTransport is a key factor influencing greenhouse gas emissions. In relation to this, the issues and challenges facing the transport industry were presented. The issues of challenges for the transport industry related to the European Green Deal were discussed. It discussed how the transport system is critical for European companies and global supply chains. The issues related to the exposure of society to costs are presented: greenhouse gas emissions and pollution. The article deals with the issues of managing transport processes in an enterprise. It was decided to raise the topic of fuel consumption in means of transport. Based on a review of the scientific literature, 3 categories of features are indicated: the vehicle characteristics, the driver's characteristics, and the route's impact on fuel consumption. The study is based on actual data from the archives of the GPS vehicle monitoring system. Data was collected on 1890 routes operated between May 30, 2020, and May 31, 2021. The routes were performed by twenty-nine drivers and 8 vehicles. The vehicles are 40-ton road sets consisting of a tractor unit and a semi-trailer. The analysis of factors influencing fuel consumption is presented. The methodology for conducting feature engineering is described. The benefits of using the method of reducing fuel consumption are presented. The possibilities of using the methods of forecasting electricity and hydrogen consumption in various means of transport, including public transport, where indicated. The data is processed using the Pandas library. The models are compared according to the MAE success measure. The application of methods of working with large data sets is presented. The calculations are made with the help of the NumPy library. Data visualization is done with Matplotlib and Seaborn. Scikit-Learn models are used.
dc.format.extent1-9
dc.format.pages9
dc.identifier.citationBudzyński A. Forecasting fuel consumption in means of transport with the use of machine learning / Artur Budzyński, Aleksander Sładkowski // Transport Technologies. — Lviv : Lviv Politechnic Publishing House, 2022. — Vol 3. — No 2. — P. 1–9.
dc.identifier.citationenBudzyński A. Forecasting fuel consumption in means of transport with the use of machine learning / Artur Budzyński, Aleksander Sładkowski // Transport Technologies. — Lviv : Lviv Politechnic Publishing House, 2022. — Vol 3. — No 2. — P. 1–9.
dc.identifier.doidoi.org/10.23939/tt2022.02.001
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/57313
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofTransport Technologies, 2 (3), 2022
dc.relation.references1. Yao, Y., Zhao, X., Liu, C., Rong, J., Zhang, Y., Dong, Z., & Su, Y. (2020). Vehicle fuel consumption prediction method based on driving behavior data collected from smartphones. Journal of Advanced Transportation, 2020. 1–11. doi: 10.1155/2020/9263605 (in English).
dc.relation.references2. Zargarnezhad, S., Dashti, R., & Ahmadi, R. (2019). Predicting vehicle fuel consumption in energy distribution companies using ANNs. Transportation Research Part D: Transport and Environment, 74, 174–188. doi: 10.1016/j.trd.2019.07.020 (in English).
dc.relation.references3. Çapraz, A. G., Özel, P., Şevkli, M., & Beyca, Ö. F. (2016). Fuel consumption models applied to automobiles using real-time data: A comparison of statistical models. Procedia Computer Science, 83, 774–781. doi: 10.1016/j.procs.2016.04.166 (in English).
dc.relation.references4. Moradi, E., & Miranda-Moreno, L. (2020). Vehicular fuel consumption estimation using real-world measures through cascaded machine learning modeling. Transportation Research Part D: Transport and Environment, 88, 102576. doi: 10.1016/j.trd.2020.102576 (in English).
dc.relation.references5. Budzyński, A., & Sładkowski A. (2021). The use of machine learning to predict diesel fuel consumption in road vehicles. 19th European Transport Congress of the EPTS Foundation e.V. European Green Deal Challenges and Solutions for Mobility and Logistics in Cities, pp. 207–221 (in English).
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dc.relation.referencesen4. Moradi, E., & Miranda-Moreno, L. (2020). Vehicular fuel consumption estimation using real-world measures through cascaded machine learning modeling. Transportation Research Part D: Transport and Environment, 88, 102576. doi: 10.1016/j.trd.2020.102576 (in English).
dc.relation.referencesen5. Budzyński, A., & Sładkowski A. (2021). The use of machine learning to predict diesel fuel consumption in road vehicles. 19th European Transport Congress of the EPTS Foundation e.V. European Green Deal Challenges and Solutions for Mobility and Logistics in Cities, pp. 207–221 (in English).
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dc.relation.referencesen7. McKinney, W. (2010). Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference, 445(1), pp. 51–56 (in English).
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dc.relation.referencesen15. NumPy. Retrieved from: https://numpy.org/doc/stable/last accessed2022/10/03 (in English).
dc.relation.referencesen16. Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., & et al. (2013). API design for machine learning software: experiences from the scikit-learn project. European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases, pp. 1–15. doi: 10.48550/arXiv.1309.0238 (in English).
dc.relation.urihttps://docs.python.org/3lastaccessed2022/10/03
dc.relation.urihttps://pandas.pydata.org/docs/last
dc.relation.urihttps://numpy.org/doc/stable/last
dc.rights.holder© Національний університет „Львівська політехніка“, 2022
dc.rights.holder© A. Budzyński, A Sładkowski, 2022
dc.subjectтранспорт
dc.subjectуправління транспортом
dc.subjectмашинне навчання
dc.subjectмоделювання
dc.subjectспоживання палива
dc.subjecttransport
dc.subjecttransport management
dc.subjectmachine learning
dc.subjectmodeling
dc.subjectfuel consumption
dc.titleForecasting fuel consumption in means of transport with the use of machine learning
dc.title.alternativeПрогнозування споживання палива різними видами транспорту з використанням машинного навчання
dc.typeArticle

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Transport Technologies. – 2022. – Vol. 3, No. 2