It is an enduring question how to combine revealed preference (RP) and stated preference (SP) data to analyze individual choices. While the nested logit (NL) model is the classical way to address the question, this study presents multitask learning deep neural networks (MTLDNNs) as an alternative framework, and discusses its theoretical foundation, empirical performance, and behavioral intuition. We first demonstrate that the MTLDNNs are theoretically more general than the NL models because of MTLDNNs’ automatic feature learning, flexible regularizations, and diverse architectures. By analyzing the adoption of autonomous vehicles (AVs), we illustrate that the MTLDNNs outperform the NL models in terms of prediction accuracy but underperform in terms of cross-entropy losses. To interpret the MTLDNNs, we compute the elasticities and visualize the relationship between choice probabilities and input variables. The MTLDNNs reveal that AVs mainly substitute driving and ride hailing, and that the variables specific to AVs are more important than the socio-economic variables in determining AV adoption. Overall, this work demonstrates that MTLDNNs are theoretically appealing in leveraging the information shared by RP and SP and capable of revealing meaningful behavioral patterns, although its performance gain over the classical NL model is still limited. To improve upon this work, future studies can investigate the inconsistency between prediction accuracy and cross-entropy losses, novel MTLDNN architectures, regularization design for the RP-SP question, MTLDNN applications to other choice scenarios, and deeper theoretical connections between choice models and the MTLDNN framework.

如何结合揭示的偏好（RP）和陈述的偏好（SP）数据来分析个人选择是一个持久的问题。虽然嵌套logit（NL）模型是解决问题的经典方法，但本研究提出了多任务学习深度神经网络（MTLDNN）作为替代框架，并讨论了其理论基础，经验性能和行为直觉。我们首先证明了MTLDNN在理论上比NL模型更通用，这是由于MTLDNN的自动特征学习，灵活的正则化和多样化的体系结构。通过分析自动驾驶汽车（AVs）的采用，我们说明MTLDNNs在预测准确度方面优于NL模型，但在交叉熵损失方面则不如NL模型。为了解释MTLDNN，我们计算弹性并可视化选择概率和输入变量之间的关系。MTLDNNs显示，自动驾驶汽车主要替代驾驶和乘车称呼，在确定自动驾驶汽车的采用率方面，针对自动驾驶汽车的变量比社会经济变量更为重要。总体而言，这项工作表明，MTLDNN在利用RP和SP共享的信息方面具有理论吸引力，并且能够揭示有意义的行为模式，尽管其在经典NL模型上的性能提升仍然有限。为了改进这项工作，未来的研究可以调查预测准确性和交叉熵损失之间的矛盾，新颖的MTLDNN体系结构，针对RP-SP问题的正则化设计，MTLDNN在其他选择方案中的应用，