增强多样性和可行性：利用生成模型从多源数据中联合合成人口
Enhancing Diversity and Feasibility: Joint Population Synthesis from Multi-source Data Using Generative Models
AI总结：
本文提出利用WGAN与梯度惩罚联合生成多源数据，提升合成人口的多样性和可行性，实验显示其在召回率和精确率上均优于传统方法。
摘要：
生成逼真的合成人口对于交通和城市规划中的基于代理模型（ABM）至关重要。当前的方法面临两个主要限制。首先，许多方法依赖单一数据集或遵循顺序数据融合和生成过程，这意味着它们无法捕捉特征之间的复杂相互作用。其次，这些方法在处理零样本（有效但未观察到的属性组合）和结构零样本（由于逻辑约束不可行的组合）时存在困难，这会降低生成数据的多样性和可行性。本研究提出了一种新的方法，利用Wasserstein生成对抗网络（WGAN）与梯度惩罚，同时整合和合成多源数据集。这种联合学习方法通过在生成器损失函数中定义正则化项（逆梯度惩罚）来提高合成数据的多样性和可行性。在评估中，我们实现了一个统一的相似性评估指标，并特别强调通过召回率、精确率和F1分数来测量多样性和可行性。结果表明，所提出的联合方法优于顺序基线方法，召回率提高了7%，精确率提高了15%。此外，正则化项进一步提高了多样性和可行性，体现在召回率增加了10%，精确率增加了1%。我们使用五项指标评估相似性分布。联合方法总体表现更好，达到88.1的分数，而顺序方法为84.6。由于合成人口是ABM的关键输入，这种多源生成方法有潜力显著提高ABM的准确性和可靠性。
英文摘要：
Generating realistic synthetic populations is essential for agent-based models (ABM) in transportation and urban planning. Current methods face two major limitations. First, many rely on a single dataset or follow a sequential data fusion and generation process, which means they fail to capture the complex interplay between features. Second, these approaches struggle with sampling zeros (valid but unobserved attribute combinations) and structural zeros (infeasible combinations due to logical constraints), which reduce the diversity and feasibility of the generated data. This study proposes a novel method to simultaneously integrate and synthesize multi-source datasets using a Wasserstein Generative Adversarial Network (WGAN) with gradient penalty. This joint learning method improves both the diversity and feasibility of synthetic data by defining a regularization term (inverse gradient penalty) for the generator loss function. For the evaluation, we implement a unified evaluation metric for similarity, and place special emphasis on measuring diversity and feasibility through recall, precision, and the F1 score. Results show that the proposed joint approach outperforms the sequential baseline, with recall increasing by 7\% and precision by 15\%. Additionally, the regularization term further improves diversity and feasibility, reflected in a 10\% increase in recall and 1\% in precision. We assess similarity distributions using a five-metric score. The joint approach performs better overall, and reaches a score of 88.1 compared to 84.6 for the sequential method. Since synthetic populations serve as a key input for ABM, this multi-source generative approach has the potential to significantly enhance the accuracy and reliability of ABM.
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。
