基于机器学习的中欧集装箱货运方式选择预测研究
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1.大连海事大学交通运输工程学院;2.大连交通大学交通运输工程学院

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中图分类号:

U116.2

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国家自然科学基金资助项目(72172023);辽宁省教育厅基本科研项目(LJKMR20220378);大连交通大学人文社科研究-支持人文社科融合发展专项研究项目(面上项目);


Prediction on Sino-Europe container transportation mode choice based on machine learning
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Dalian Maritime University College of Transportation Engineering

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    摘要:

    为高效准确预测重大突发事件下货运代理对于中欧间集装箱运输方式选择偏好并揭示影响货运代理选择的相关因素,本文采用陈述性偏好方法对货运代理进行调查,同时考虑了运输属性和货物属性的影响,构建决策树、逻辑回归和随机森林预测模型,对货运代理的选择行为进行预测。通过准确率、精确率、召回率和F1值这4个评价指标,将三个机器学习模型与离散选择模型的预测结果进行了综合对比;并利用随机森林算法对疫情不同阶段下影响货运代理运输方式选择的属性重要性进行排序。研究结果表明:三个机器学习模型的预测精度均比离散选择模型高,其中随机森林模型相较于决策树模型和逻辑回归模型在中欧集装箱运输方式选择问题具有更高预测准确度,更加适用本问题;影响因素方面:在平稳期,货物属性是最重要的影响因素,当重大突发事件发生时货运代理更加看重阈值延迟时间。此外,货物目的地和货物价值对中欧集装箱运输方式一直选择有着重要影响。该研究可为全球重大突发事件影响下更准确地分析货运代理的运输方式选择行为的决策机制,以及帮助航运公司和中欧班列经营人更好地理解货运代理偏好和决策因素,为应对类似的突发事件提供了有力依据。

    Abstract:

    In order to efficiently and accurately predict freight forwarders' transportation modes preferences between China and Europe during major emergencies, as well as to uncover the relevant factors influencing freight forwarders' choices, the stated preference method was employed in this study to survey freight forwarders. Additionally, considering the influences of transportation and cargo attributes, decision trees, logistic regressions, and random forest prediction models were constructed to forecast the selection behavior of freight forwarders. The prediction results of the machine learning model and the discrete choice model were comprehensively compared through four evaluation metrics: accuracy, precision, recall, and F1 score. Furthermore, the random forest algorithm was utilized to rank the importance of attributes influencing freight forwarders' transportation mode choices during different stages of the pandemic. The study results demonstrate that the prediction accuracy of all three machine learning models was higher than that of the discrete choice model. Among them, the random forest model exhibited superior prediction accuracy compared to the decision tree and logistic regression models in addressing the choice of Sino-Europe container transport modes, making it more suitable for this problem. Regarding influencing factors, during stable periods, cargo attributes were identified as the most important factors. When major emergencies occur, freight forwarders place greater emphasis on the threshold delay time. Furthermore, the destination and value of the cargo were found to have significant impacts on the choice of Sino-Europe container transport modes. This study proposes an accurate analysis of the decision-making mechanisms guiding freight forwarders' mode choice behavior during major global emergencies. Furthermore, it was utilized by shipping companies and operators of the China Railway Express to gain a deeper understanding of the preferences and decision-making factors influencing freight forwarders. The insights derived from this study were considered a solid basis for effectively responding to similar emergency situations.

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郭姝娟,耿晓洁,李纲,等. 基于机器学习的中欧集装箱货运方式选择预测研究[J]. 科学技术与工程, , ():

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  • 收稿日期:2024-03-09
  • 最后修改日期:2024-04-19
  • 录用日期:2024-04-25
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