Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data
Sustainability, Vol. 14, No. 2, pp. 122022This paper deals with fuel consumption prediction based on vehicle velocity, acceleration, and road slope time series inputs. Several data-driven models are considered for this purpose, including linear regression models and neural network-based ones. The emphasis is on accounting for the road slope impact when forming the model inputs, in order to improve the prediction accuracy. A particular focus is devoted to conversion of length-varying driving cycles into fixed dimension inputs suitable for neural networks. The proposed prediction algorithms are parameterized and tested based on GPS- and CAN-based tracking data recorded on a number of city buses during their regular operation. The test results demonstrate that proposed neural network-based approach provides a favorable prediction accuracy and reasonable execution speed, thus making it suitable for various applications such as vehicle routing optimization, driving cycle validation, transport planning and similar. driving cycle; data processing; feedforward neural networks; city buses; fuel consumption; prediction
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Sustainability, Vol. 14, No. 2, pp. 12
2022
Cited by 20
▾
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[1] AutonomieAI: An efficient and deployable vehicle energy consumption estimation toolkit🔗Transportation Research Part D: Transport and Environment, 2025
-
[3] Harnessing ECU Data for Real-Time Fuel Consumption Forecasting🔗2025 International Conference on Next Generation Communication & Information Processing (INCIP), 2025
-
[4] Fuel Consumption Prediction in Regional Transport Based on Selected Bus Line Characteristics🔗Journal of Advanced Transportation, 2025
-
[5] Incorporating driving behavior into vehicle fuel consumption prediction: methodology development and testing🔗Discover Sustainability, 2024
-
[7] Intelligent information system for resource planning in grain crops delivery projects on the basis of machine learning🔗International Conference on Computer Science and Information Technologies, 2023
-
[8] Truck Fuel Consumption Prediction Using Logistic Regression and Artificial Neural Networks🔗International Journal of Operations Research and Information Systems, 2023
-
[12] Driving cycle prediction based on Markov chain combined with driving information mining🔗Proceedings of the Institution of mechanical engineers. Part D, journal of automobile engineering, 2023
-
[15] Sustainable City Evaluation Using the Database for Estimation of Road Network Performance🔗Sustainability, 2022
-
[16] Application of algorithmic models of machine learning to the freight transportation process🔗Transport technologies, 2022
-
[17] Synthesis and Feature Selection-Supported Validation of Multidimensional Driving Cycles🔗Sustainability, 2021
-
[18] Supervised Machine Learning Models for Forecasting Fuel Consumption by Vehicles During the Grain Crops Delivery🔗MATEC Web of Conferences, 2024
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[19] Modeling Vehicle CO2 Emissions: Assessing Alternative Methods, Lag Effects, and Internal-External Factors🔗Social Science Research Network, 2024