出行需求预测：一种公平人工智能方法
Travel Demand Forecasting: A Fair AI Approach
AI总结：
针对AI出行需求预测中的多属性公平性问题，本文提出衡量预测准确性与多个受保护属性相关性的新型公平正则化项，并在芝加哥和奥斯汀网约车数据上验证其能兼顾准确性与公平性且优于三种基线方法。
摘要：
人工智能（AI）和机器学习正越来越多地被用于出行需求预测。基于AI的出行需求预测模型虽然能够生成准确预测，但也可能产生预测偏差并引发公平性问题。将此类有偏差的模型用于决策，可能导致加剧社会不平等的交通政策。然而，目前聚焦于解决这些模型公平性问题的研究仍然有限。因此，本研究提出了一种新的方法论，用于开发具有公平意识且高度准确的出行需求预测模型。特别是，所提出的方法论能够同时提升AI模型针对多个受保护属性（例如种族和收入）的公平性。具体而言，我们引入了一个新的公平正则化项，将其加入出行需求预测模型的损失函数中；该项经过明确设计，用于衡量预测准确性与多个受保护属性之间的相关性。我们分别利用伊利诺伊州芝加哥和得克萨斯州奥斯汀的真实网约车出行数据开展了两个案例研究，以评估所提方法论的性能。结果突出表明，我们提出的方法论能够在保持预测准确性的同时，有效提升针对多个受保护属性的公平性。此外，我们将该方法论与三种采用正则化项方法的最先进方法进行了比较，结果表明，我们的方法在保持预测准确性和提升公平性两方面均显著优于这些方法。本研究可为交通专业人员提供一种实现公平且准确的出行需求预测的新工具。
英文摘要：
Artificial Intelligence (AI) and machine learning have been increasingly adopted for travel demand forecasting. The AI-based travel demand forecasting models, though generate accurate predictions, may produce prediction biases and raise fairness issues. Using such biased models for decision-making may lead to transportation policies that exacerbate social inequalities. However, limited studies have been focused on addressing the fairness issues of these models. Therefore, in this study, we propose a novel methodology to develop fairness-aware, highly-accurate travel demand forecasting models. Particularly, the proposed methodology can enhance the fairness of AI models for multiple protected attributes (such as race and income) simultaneously. Specifically, we introduce a new fairness regularization term, which is explicitly designed to measure the correlation between prediction accuracy and multiple protected attributes, into the loss function of the travel demand forecasting model. We conduct two case studies to evaluate the performance of the proposed methodology using real-world ridesourcing-trip data in Chicago, IL and Austin, TX, respectively. Results highlight that our proposed methodology can effectively enhance fairness for multiple protected attributes while preserving prediction accuracy. Additionally, we have compared our methodology with three state-of-the-art methods that adopt the regularization term approach, and the results demonstrate that our approach significantly outperforms them in both preserving prediction accuracy and enhancing fairness. This study can provide transportation professionals with a new tool to achieve fair and accurate travel demand forecasting.
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。
