Abstract
Accurate vehicle fuel consumption prediction is crucial to reduce pollutant emissions and save commercial vehicle operating costs. With the support of Internet of Vehicles data, data-driven multivariate time series forecasting methods have been adopted for fuel consumption prediction. Different types of vehicles are composed of modules with different configurations and contain different domain knowledge. However, existing methods rarely consider these differences, and cannot be adjusted according to the vehicle configuration when facing multiple types of vehicles. Moreover, the number of vehicle samples for some personalized configurations is not enough to support the training of the model. To solve the above problems, we propose the multi-type vehicle fuel consumption prediction model based on Module Graph Convolution Network and Configuration Transfer(MGCN-CT). First, in order to express the vehicle module domain knowledge and driving data uniformly, a module graph embedded with domain knowledge is proposed. Then a module graph convolutional network is proposed to model the spatio-temporal dependence of the module graph and realize fuel consumption prediction. Finally, a configuration transfer module based on a configuration classifier is proposed to realize the fuel consumption prediction of a few-sample personalized configuration vehicles. The effectiveness of the model is verified through extensive experiments on real datasets. Compared with the baseline methods, our method achieves superior accuracy for fuel consumption prediction.
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Acknowledgements
This work is supported by National Natural Science Foundation of China (No. 62072282, No. 62172443), Industrial Internet Innovation and Development Project in 2019 of China, Shandong Provincial Key Research and Development Program (Major Scientific and Technological Innovation Project) (No.2019JZZY010105) and CAAI Huawei MindSpore Open Fund.
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Li, H., Cheng, Q., Peng, Z., Tan, Y., Chen, Z. (2024). MGCN-CT: Multi-type Vehicle Fuel Consumption Prediction Based on Module-GCN and Config-Transfer. In: Song, X., Feng, R., Chen, Y., Li, J., Min, G. (eds) Web and Big Data. APWeb-WAIM 2023. Lecture Notes in Computer Science, vol 14333. Springer, Singapore. https://doi.org/10.1007/978-981-97-2387-4_21
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