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. The MTLDNNs reveal that AVs mainly substitute driving and ride hailing, and that the variables specific to AVs are more important than the socioeconomic variables in determining AV adoption. To interpret the MTLDNNs, we compute the elasticities and visualize the relationship between choice probabilities and input variables. 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.

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. 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.

It is an enduring question how to combine revealed preference (RP) and stated preference (SP) data to analyze individual choices.

Fresh Cut by Kayette la Mane