Since its inception, the choice modelling field has been dominated by theory-driven modelling approaches. Machine learning offers an alternative data-driven approach for modelling choice behaviour and is increasingly drawing interest in our field. Cross-pollination of machine learning models, techniques and practices could help overcome problems and limitations encountered in the current theory-driven modelling paradigm, such as subjective labour-intensive search processes for model selection, and the inability to work with text and image data. However, despite the potential benefits of using the advances of machine learning to improve choice modelling practices, the choice modelling field has been hesitant to embrace machine learning. This discussion paper aims to consolidate knowledge on the use of machine learning models, techniques and practices for choice modelling, and discuss their potential. Thereby, we hope not only to make the case that further integration of machine learning in choice modelling is beneficial, but also to further facilitate it. To this end, we clarify the similarities and differences between the two modelling paradigms; we review the use of machine learning for choice modelling; and we explore areas of opportunities for embracing machine learning models and techniques to improve our practices. To conclude this discussion paper, we put forward a set of research questions which must be addressed to better understand if and how machine learning can benefit choice modelling.
翻译:自建立以来,选择建模领域一直以理论驱动的建模方法为主。机器学习为模拟选择行为提供了一种由数据驱动的替代方法,并日益吸引对本领域的兴趣。机械学习模式、技术和做法的交叉分布有助于克服当前理论驱动的建模模式遇到的问题和限制,例如选择模式的主观劳动密集型搜索过程,以及无法与文本和图像数据合作。然而,尽管利用机器学习的进步改进选择建模做法的潜在好处,但选择建模领域却不愿接受机器学习。本讨论文件旨在巩固关于使用机器学习模型、技术和做法进行选择建模的知识,并讨论其潜力。因此,我们希望不仅证明在选择建模中进一步整合机器学习是有益的,而且还能进一步促进它。为此,我们澄清两种建模模式的相似性和差异;我们审查机器学习用于选择建模的情况;我们探索接受机器学习模型和技术来改进我们做法的机会。为完成这一讨论文件,我们提出了一套研究问题,如果能够更好地了解和学习机器如何受益,就必须研究如何进行建模。