通过嵌入重新思考旅行行为建模表示
Rethinking travel behavior modeling representations through embeddings
摘要：
本文介绍了出行行为嵌入的概念，这是一种重新表示离散变量的方法，这些离散变量通常用于出行需求建模，例如模式、出行目的、教育水平、家庭类型或职业。这个重新表示过程实质上是将这些变量映射到一个称为\emph{embedding space}的潜在空间中。这样做的好处是，这样的空间允许比分类变量中使用的典型变换（例如，虚拟编码，对比编码，主成分分析）更丰富的细微差别。虽然在出行需求建模中使用潜在变量表示本身并不新鲜，但这里提出的想法带来了几个创新：它是一个完全数据驱动的算法;它是信息丰富和一致的，因为潜在空间可以基于不同类别之间的距离进行可视化和解释;它保留了系数的可解释性，尽管基于神经网络原理;并且它是可移植的，因为从一个数据集学习的嵌入可以被重用于其他数据集，只要旅行行为在数据集之间保持一致。这个想法受到了自然语言处理技术的强烈启发，即word 2 vec算法。这种算法是最近的发展，如在自动翻译或下一个词预测。我们的方法证明使用模型选择模型，并显示改进高达60%的初始可能性，和高达20%的可能性，相应的传统模型（即使用虚拟变量）在样本外的评估。我们提供了一个新的Python包，称为PyTre（PYthon TRavel Embeddings），其他人可以直接使用它来复制我们的结果或改进他们自己的模型。我们的实验本身是基于一个开放的数据集（瑞士地铁）。
英文摘要：
This paper introduces the concept of travel behavior embeddings, a method for re-representing discrete variables that are typically used in travel demand modeling, such as mode, trip purpose, education level, family type or occupation. This re-representation process essentially maps those variables into a latent space called the \emph{embedding space}. The benefit of this is that such spaces allow for richer nuances than the typical transformations used in categorical variables (e.g. dummy encoding, contrasted encoding, principal components analysis). While the usage of latent variable representations is not new per se in travel demand modeling, the idea presented here brings several innovations: it is an entirely data driven algorithm; it is informative and consistent, since the latent space can be visualized and interpreted based on distances between different categories; it preserves interpretability of coefficients, despite being based on Neural Network principles; and it is transferrable, in that embeddings learned from one dataset can be reused for other ones, as long as travel behavior keeps consistent between the datasets. The idea is strongly inspired on natural language processing techniques, namely the word2vec algorithm. Such algorithm is behind recent developments such as in automatic translation or next word prediction. Our method is demonstrated using a model choice model, and shows improvements of up to 60\% with respect to initial likelihood, and up to 20% with respect to likelihood of the corresponding traditional model (i.e. using dummy variables) in out-of-sample evaluation. We provide a new Python package, called PyTre (PYthon TRavel Embeddings), that others can straightforwardly use to replicate our results or improve their own models. Our experiments are themselves based on an open dataset (swissmetro).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
