论文标题
综合旅行生成的对抗网络，用于表格和顺序种群合成
Composite Travel Generative Adversarial Networks for Tabular and Sequential Population Synthesis
论文作者
论文摘要
基于代理的运输建模已成为模拟旅行行为，移动性选择和活动偏好的标准，并使用整个人群的旅行需求数据进行分类，这些数据通常不容易获得。为此，已经提出了各种方法来合成人口数据。我们提出了一个复合旅行生成的对抗网络（CTGAN），这是一种新型的深层生成模型，以估计人群的潜在关节分布，该模型能够重建具有表格（例如年龄和性别）以及顺序迁移率数据的复合合成剂（例如，旅行轨迹和序列）。将CTGAN模型与其他最近提出的方法（例如变异自动编码器（VAE）方法）进行了比较，该方法已显示在高维表格种群合成中的成功。我们根据分布相似性，多变量相关性和时空指标评估合成输出的性能。结果表明，合成种群的一致，准确的产生及其表格和空间顺序属性，这些属性是在不同的空间尺度和尺寸上产生的。
Agent-based transportation modelling has become the standard to simulate travel behaviour, mobility choices and activity preferences using disaggregate travel demand data for entire populations, data that are not typically readily available. Various methods have been proposed to synthesize population data for this purpose. We present a Composite Travel Generative Adversarial Network (CTGAN), a novel deep generative model to estimate the underlying joint distribution of a population, that is capable of reconstructing composite synthetic agents having tabular (e.g. age and sex) as well as sequential mobility data (e.g. trip trajectory and sequence). The CTGAN model is compared with other recently proposed methods such as the Variational Autoencoders (VAE) method, which has shown success in high dimensional tabular population synthesis. We evaluate the performance of the synthesized outputs based on distribution similarity, multi-variate correlations and spatio-temporal metrics. The results show the consistent and accurate generation of synthetic populations and their tabular and spatially sequential attributes, generated over varying spatial scales and dimensions.
