英文标题：
《Multitask Learning Deep Neural Networks to Combine Revealed and Stated
  Preference Data》
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作者：
Shenhao Wang, Qingyi Wang, Jinhua Zhao
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最新提交年份：
2019
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英文摘要：
  It is an enduring question how to combine revealed preference (RP) and stated preference (SP) data to analyze travel behavior. This study presents a framework of multitask learning deep neural networks (MTLDNNs) for this question, and demonstrates that MTLDNNs are more generic than the traditional nested logit (NL) method, due to its capacity of automatic feature learning and soft constraints. About 1,500 MTLDNN models are designed and applied to the survey data that was collected in Singapore and focused on the RP of four current travel modes and the SP with autonomous vehicles (AV) as the one new travel mode in addition to those in RP. We found that MTLDNNs consistently outperform six benchmark models and particularly the classical NL models by about 5% prediction accuracy in both RP and SP datasets. This performance improvement can be mainly attributed to the soft constraints specific to MTLDNNs, including its innovative architectural design and regularization methods, but not much to the generic capacity of automatic feature learning endowed by a standard feedforward DNN architecture. Besides prediction, MTLDNNs are also interpretable. The empirical results show that AV is mainly the substitute of driving and AV alternative-specific variables are more important than the socio-economic variables in determining AV adoption. Overall, this study introduces a new MTLDNN framework to combine RP and SP, and demonstrates its theoretical flexibility and empirical power for prediction and interpretation. Future studies can design new MTLDNN architectures to reflect the speciality of RP and SP and extend this work to other behavioral analysis.
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中文摘要：
如何结合显示偏好（RP）和陈述偏好（SP）数据来分析旅游行为是一个长期存在的问题。本研究针对这个问题提出了一个多任务学习深度神经网络（MTLDNNs）的框架，并证明了MTLDNNs比传统的嵌套logit（NL）方法更通用，因为它具有自动特征学习能力和软约束。设计了约1500个MTLDNN模型，并将其应用于在新加坡收集的调查数据，重点关注四种当前出行模式的RP，以及将自动驾驶汽车（AV）作为RP之外的一种新出行模式的SP。我们发现，MTLDNN始终优于六个基准模型，尤其是经典NL模型，在这两种RP中的预测精度约为5%和SP数据集。这种性能的提高主要归功于MTLDNNs特有的软约束，包括其创新的体系结构设计和正则化方法，但与标准前馈DNN体系结构赋予的自动特征学习的通用能力无关。除了预测之外，MTLDNN也是可解释的。实证结果表明，AV主要是驾驶的替代品，AV替代品的特定变量在决定AV采用方面比社会经济变量更重要。总体而言，本研究引入了一个新的MTLDNN框架，将RP和SP结合起来，并展示了其理论灵活性和预测和解释的经验能力。未来的研究可以设计新的MTLDNN架构来反映RP和SP的特性，并将此工作扩展到其他行为分析。
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分类信息：

一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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