Volume 43 Issue 1
Feb.  2025
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Article Navigation >  Journal of Transport Information and Safety  > 2025  >  43(1): 97-106
ZHANG Hanyu, YIN Qinzhi, JIN Liangzhen, QIAN Weiwen, ZHANG Wenqiang, QIN Letian. Intelligent Segmentation Method of Inland Waterway for Energy Efficiency Optimization[J]. Journal of Transport Information and Safety, 2025, 43(1): 97-106. doi: 10.3963/j.jssn.1674-4861.2025.01.009
Citation: ZHANG Hanyu, YIN Qinzhi, JIN Liangzhen, QIAN Weiwen, ZHANG Wenqiang, QIN Letian. Intelligent Segmentation Method of Inland Waterway for Energy Efficiency Optimization[J]. Journal of Transport Information and Safety, 2025, 43(1): 97-106. doi: 10.3963/j.jssn.1674-4861.2025.01.009
shu

Intelligent Segmentation Method of Inland Waterway for Energy Efficiency Optimization

doi: 10.3963/j.jssn.1674-4861.2025.01.009
  • 1.

    School of Naval Architecture Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China

  • 2.

    State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430063, China

  • 3.

    School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China

  • 4.

    East Lake Laboratory, Wuhan 420202, China

  • 5.

    Weichai Power Company Limited, Weifang 261061, Shandong, China

  • Received Date: 2024-05-13
    Available Online: 2025-06-27
    Abstract
  • The automatic scientific segmentation of inland waterways is significant to enhancing the accuracy of ship energy efficiency models. To address the problem of low accuracy in fuel consumption and speed prediction models built based on conventional waterway segmentation for energy efficiency optimization, an intelligent segmentation method of inland waterways aimed at optimizing energy efficiency is studied. The method incorporates the influence of navigational environmental parameters on ship energy performance into the clustering process by normalizing the data and calculating correlation coefficients between environmental parameters and energy efficiency indicators. The K-means clustering algorithm is employed to segment the entire route into multiple sections. For each segment, Random Forest algorithm is used to establish models for fuel consumption and ship speed prediction. The optimal number of clusters is determined by minimizing the overall mean absolute percentage error (MAPE) of the energy efficiency models. A case study involving an inland bulk carrier is conducted to demonstrate the application and validate the effectiveness of the proposed method, along with an analysis of the influence of data volume on the model accuracy. The results show that optimizing the number of clusters significantly improves model accuracy: in the firstly voyage, the comprehensive MAPE of the energy efficiency model is reduced from 3.53% to 3.32%. Increasing the volume of energy-related data used in model construction further enhances the performance: when the dataset expanded from one voyage to five, the comprehensive MAPE decreased from 3.32% to 1.65%. The optimal number of clusters varies with different datasets, and selecting an optimal number of clusters based on multi-voyage data leads to the optimal waterway segmentation. Compared to the conventional segmentation method, the proposed approach reduced the comprehensive MAPE of energy efficiency model by 0.54%, validating the effectiveness of the method in improving the prediction accuracy of ship energy efficiency model.

     

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