比较离散选择模型和机器学习模型在预测目的地选择方面的作用

IF 5.1 3区 工程技术 Q1 TRANSPORTATION European Transport Research Review Pub Date : 2024-08-21 DOI:10.1186/s12544-024-00667-9
Ilona Rahnasto, Martijn Hollestelle
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引用次数: 0

摘要

长期以来,基于理论的离散选择模型一直主导着目的地选择建模。与此同时,机器学习在离散选择建模的其他领域表现出了更好的预测性能。本研究旨在比较机器学习模型和多叉 Logit 模型在预测目的地选择方面的优势。使用二元分类和概率分类指标对模型的预测性能进行了评估。结果表明,机器学习模型,尤其是随机森林模型,可以提高预测准确率。与多叉 Logit 模型相比,训练模型时使用的数据越多,机器学习模型的性能就越好。在数据较少的情况下,多叉 Logit 模型的表现相对较好。这些发现与目的地选择建模领域相关,因为在该领域使用机器学习模型的证据非常有限。此外,目的地选择模型的不平衡选择集具有多个非选择性替代方案,这增加了进一步研究模型拟合和参数调整的必要性。
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Comparing discrete choice and machine learning models in predicting destination choice
Destination choice modeling has long been dominated by theory-based discrete choice models. Simultaneously, machine learning has demonstrated improved predictive performance to other fields of discrete choice modeling. The objective of this research was to compare machine learning models and a multinomial logit model in predicting destination choice. The models were assessed on their predictive performance using metrics for both binary classification and probabilistic classification. The results indicate that machine learning models, especially a random forest model, could bring improvements in prediction accuracy. The more data was used in training the models, the better the machine learning models tended to perform compared to the multinomial logit model. With less data, the multinomial logit model performed comparatively well. The findings are relevant for the field of destination choice modeling, where evidence on the use of machine learning models is very limited. In addition, the unbalanced choice sets of destination choice models with multiple non-chosen alternatives increases the need for further research in model fit and parameter tuning.
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来源期刊
European Transport Research Review
European Transport Research Review Engineering-Mechanical Engineering
CiteScore
8.60
自引率
4.70%
发文量
49
审稿时长
13 weeks
期刊介绍: European Transport Research Review (ETRR) is a peer-reviewed open access journal publishing original high-quality scholarly research and developments in areas related to transportation science, technologies, policy and practice. Established in 2008 by the European Conference of Transport Research Institutes (ECTRI), the Journal provides researchers and practitioners around the world with an authoritative forum for the dissemination and critical discussion of new ideas and methodologies that originate in, or are of special interest to, the European transport research community. The journal is unique in its field, as it covers all modes of transport and addresses both the engineering and the social science perspective, offering a truly multidisciplinary platform for researchers, practitioners, engineers and policymakers. ETRR is aimed at a readership including researchers, practitioners in the design and operation of transportation systems, and policymakers at the international, national, regional and local levels.
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