  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.
