Existing deep learning approaches for travel mode choice modeling fail to
inform modelers about their prediction uncertainty. Even when facing scenarios
that are out of the distribution of training data, which implies high
prediction uncertainty, these approaches still provide deterministic answers,
potentially leading to misguidance. To address this limitation, this study
introduces the concept of uncertainty from the field of explainable artificial
intelligence into travel mode choice modeling. We propose a Bayesian neural
network-based travel mode prediction model (BTMP) that quantifies the
uncertainty of travel mode predictions, enabling the model itself to "know" and
"tell" what it doesn't know. With BTMP, we further propose an
uncertainty-guided active survey framework, which dynamically formulates survey
questions representing travel mode choice scenarios with high prediction
uncertainty. Through iterative collection of responses to these dynamically
tailored survey questions, BTMP is iteratively trained to achieve the desired
accuracy faster with fewer questions, thereby reducing survey costs.
Experimental validation using synthetic datasets confirms the effectiveness of
BTMP in quantifying prediction uncertainty. Furthermore, experiments, utilizing
both synthetic and real-world data, demonstrate that the BTMP model, trained
with the uncertainty-guided active survey framework, requires 20% to 50% fewer
survey responses to match the performance of the model trained on randomly
collected survey data. Overall, the proposed BTMP model and active survey
framework innovatively incorporate uncertainty quantification into travel mode
choice modeling, providing model users with essential insights into prediction
reliability while optimizing data collection for deep learning model training
in a cost-efficient manner.

将可解释人工智能领域的不确定性概念引入交通方式选择建模，提出了基于贝叶斯神经网络的交通方式预测模型（BTMP），该模型能够量化预测的不确定性，并通过一种不确定性引导的主动调查框架，动态地制定具有高预测不确定性的调查问题，通过减少调查问题数量降低调查成本，加快模型训练速度。对合成数据集和真实数据集进行实验验证，结果表明，利用不确定性引导的主动调查框架训练的 BTMP 模型，只需使用 20% 至 50% 的调查响应数据，即可达到与随机收集的调查数据训练的模型相当的性能。总体而言，提出的 BTMP 模型和主动调查框架将不确定性量化纳入交通方式选择建模中，为模型用户提供了关于预测可靠性的重要见解，并以成本高效的方式优化深度学习模型训练的数据收集。

将不确定性量化纳入出行方式选择建模：一种贝叶斯神经网络（BNN）方法和基于不确定性引导的主动调查框架

Incorporating uncertainty quantification into travel mode choice  modeling: a Bayesian neural network (BNN) approach and an uncertainty-guided  active survey framework