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A Comparative Study on Fuel Consumption Prediction Methods of Heavy-Duty Diesel Trucks Considering 21 Influencing Factors

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  • Jian Gong

    (School of Transportation, Southeast University, Nanjing 210018, China)

  • Junzhu Shang

    (School of Transportation, Southeast University, Nanjing 210018, China)

  • Lei Li

    (School of Transportation, Southeast University, Nanjing 210018, China)

  • Changjian Zhang

    (School of Transportation, Southeast University, Nanjing 210018, China)

  • Jie He

    (School of Transportation, Southeast University, Nanjing 210018, China)

  • Jinhang Ma

    (School of Transportation, Southeast University, Nanjing 210018, China)

Abstract

With increasingly prominent environmental problems, controlling automobile exhaust has become essential to the environment. The fuel consumption of transportation is the critical factor that determines exhaust gas. By analyzing the naturalistic driving data of heavy-duty diesel trucks (HDDTs), this paper explored the influence of engine technical state, road features, weather, and temperature conditions on fuel consumption during driving. The detailed process is as follows: Firstly, we collected 1153 naturalistic driving data from 34 HDDTs and made a specific analysis and summary description of the data; secondly, by establishing a binary Logistic regression model, we quantitatively explored the influence of significant factors on the fuel consumption; meanwhile, based on quantitative analysis of factor’s effectiveness, this research used several machine learning algorithms (back-propagation neural network, decision tree, and random forest) to build fuel consumption predictors, and compared the prediction performance of different algorithms. The results showed that the prediction accuracy of the decision tree, back-propagation (BP) neural network, and random forest is 81.38%, 83.98%, and 86.58%, respectively. The random forest showed the best performance in predicting. The conclusions can assist transportation companies in formulating driving training strategies and contribute to reducing energy consumption and emissions.

Suggested Citation

  • Jian Gong & Junzhu Shang & Lei Li & Changjian Zhang & Jie He & Jinhang Ma, 2021. "A Comparative Study on Fuel Consumption Prediction Methods of Heavy-Duty Diesel Trucks Considering 21 Influencing Factors," Energies, MDPI, vol. 14(23), pages 1-18, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:8106-:d:694419
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    References listed on IDEAS

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    1. Zhou, Xiaoyi & Lu, Pan & Zheng, Zijian & Tolliver, Denver & Keramati, Amin, 2020. "Accident Prediction Accuracy Assessment for Highway-Rail Grade Crossings Using Random Forest Algorithm Compared with Decision Tree," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
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    4. Zeng, Lijun & Guo, Jiaqi & Wang, Bingcheng & Lv, Jun & Wang, Qin, 2019. "Analyzing sustainability of Chinese coal cities using a decision tree modeling approach," Resources Policy, Elsevier, vol. 64(C).
    5. Toledo, Galit & Shiftan, Yoram, 2016. "Can feedback from in-vehicle data recorders improve driver behavior and reduce fuel consumption?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 194-204.
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    Cited by:

    1. Dengfeng Zhao & Haiyang Li & Junjian Hou & Pengliang Gong & Yudong Zhong & Wenbin He & Zhijun Fu, 2023. "A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption," Energies, MDPI, vol. 16(14), pages 1-20, July.
    2. Zhu, Xinyi & Shen, Xiaoyan & Chen, Kailiang & Zhang, Zeqing, 2024. "Research on the prediction and influencing factors of heavy duty truck fuel consumption based on LightGBM," Energy, Elsevier, vol. 296(C).
    3. Muhammed A. Hassan & Hindawi Salem & Nadjem Bailek & Ozgur Kisi, 2023. "Random Forest Ensemble-Based Predictions of On-Road Vehicular Emissions and Fuel Consumption in Developing Urban Areas," Sustainability, MDPI, vol. 15(2), pages 1-22, January.

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