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首页> 外文期刊>Journal of Transport Geography >Determination of the influence factors on household vehicle ownership patterns in Phnom Penh using statistical and machine learning methods
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Determination of the influence factors on household vehicle ownership patterns in Phnom Penh using statistical and machine learning methods

机译：使用统计和机器学习方法确定对金边家庭汽车拥有方式的影响因素

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摘要

Vehicle ownership patterns and their determinants play an important role in transportation policy-making. This issue has been paid even greater attention in developing countries that aspire to reach sustainable transportation development goals in the era of urbanization and globalization. In this study, the multinomial logit model, neural networks and random forest were applied to examine the features' impact level and to also predict vehicle ownership patterns in Phnom Penh city. The empirical results indicate that household income is the most powerful variable affecting motorization in Phnom Penh. Supplementation of individual trip characteristics such as total number of trips made, number of trips made for work purposes and overall travel distance all make effective contributions as classifiers. Furthermore, it is acknowledged that the machine-learning approach outperformed not only in terms of predicting accuracy, but also in dealing with unbalanced categories when compared with the statistical approach. This detection supplies the advantages of applying machine learning techniques in terms of, but not limited to, the field of vehicle ownership.
机译：车辆所有权模式及其决定因素在运输政策制定中起着重要作用。渴望在城市化和全球化时代实现可持续交通发展目标的发展中国家对此问题给予了更大的关注。在这项研究中，运用多项式logit模型，神经网络和随机森林来检查特征的影响程度，并预测金边市的车辆拥有方式。实证结果表明，家庭收入是影响金边机动化的最强大变量。对个人出行特征的补充，例如总出行次数，出于工作目的的出行次数和总出行距离，均对分类器做出了有效贡献。此外，公认的是，与统计方法相比，机器学习方法不仅在预测准确性方面，而且在处理不平衡类别方面也胜于其他方法。这种检测提供了在但不限于车辆拥有领域方面应用机器学习技术的优势。

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