基于深度神经网络和离散选择模型的出行行为预测机会均等性
Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models
摘要：
尽管研究人员越来越多地采用机器学习来建模旅行行为，但他们主要关注预测准确性，忽视了机器学习算法中嵌入的伦理挑战。本研究引入了一个重要的缺失维度-计算公平性-旅游行为分析。我们首先通过机会均等将计算公平性操作化，然后区分数据中固有的偏见和建模引入的偏见。然后，我们使用2017年全国家庭旅行调查（NHTS）和2018-2019年我在芝加哥的每日旅行调查来展示旅行行为建模的预测差异。从实证上看，深度神经网络和离散选择模型（DCM）在多个社会群体中表现出一致的预测差异：对少数民族、低收入人群和残疾人群体的频繁驾驶的假阴性率都有过高的预测，而对社会弱势群体和农村人口的出行负担都有过高的预测。比较DNN和DCM，我们发现DNN在预测差异方面优于DCM，因为DNN的误指定误差更小。为了减轻预测差异，本研究引入了绝对相关正则化方法，该方法用合成和真实世界的数据进行评估。结果表明，在旅行行为建模中预测差异的普遍性，并且差异仍然存在于各种模型细节中，例如DNN层的数量，批量大小和权重初始化。由于这些预测差异，如果没有公平调整的预测结果用于交通政策制定，会加剧社会不公平，我们主张认真考虑公平问题的出行行为建模，并使用偏见缓解算法公平的交通决策。
英文摘要：
Although researchers increasingly adopt machine learning to model travel behavior, they predominantly focus on prediction accuracy, ignoring the ethical challenges embedded in machine learning algorithms. This study introduces an important missing dimension - computational fairness - to travel behavior analysis. We first operationalize computational fairness by equality of opportunity, then differentiate between the bias inherent in data and the bias introduced by modeling. We then demonstrate the prediction disparities in travel behavior modeling using the 2017 National Household Travel Survey (NHTS) and the 2018-2019 My Daily Travel Survey in Chicago. Empirically, deep neural network (DNN) and discrete choice models (DCM) reveal consistent prediction disparities across multiple social groups: both over-predict the false negative rate of frequent driving for the ethnic minorities, the low-income and the disabled populations, and falsely predict a higher travel burden of the socially disadvantaged groups and the rural populations than reality. Comparing DNN with DCM, we find that DNN can outperform DCM in prediction disparities because of DNN's smaller misspecification error. To mitigate prediction disparities, this study introduces an absolute correlation regularization method, which is evaluated with synthetic and real-world data. The results demonstrate the prevalence of prediction disparities in travel behavior modeling, and the disparities still persist regarding a variety of model specifics such as the number of DNN layers, batch size and weight initialization. Since these prediction disparities can exacerbate social inequity if prediction results without fairness adjustment are used for transportation policy making, we advocate for careful consideration of the fairness problem in travel behavior modeling, and the use of bias mitigation algorithms for fair transport decisions.
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。
