Bayesian Optimization for High-Dimensional Urban Mobility Problems
Abstract
This workshop talk presents a class of important optimization problems that arise in the design of urban mobility digital twins. It presents the open questions in the field and identifies key research opportunities for the communities of Bayesian optimization, uncertainty quantification, and inverse optimization. It shares the code to tackle a travel demand estimation problem for two road networks: an illustrative toy network and the San Francisco metropolitan network.
Cite
Text
Choi et al. "Bayesian Optimization for High-Dimensional Urban Mobility Problems." NeurIPS 2024 Workshops: BDU, 2024.Markdown
[Choi et al. "Bayesian Optimization for High-Dimensional Urban Mobility Problems." NeurIPS 2024 Workshops: BDU, 2024.](https://mlanthology.org/neuripsw/2024/choi2024neuripsw-bayesian/)BibTeX
@inproceedings{choi2024neuripsw-bayesian,
title = {{Bayesian Optimization for High-Dimensional Urban Mobility Problems}},
author = {Choi, Seongjin and Rodriguez, Sergio and Osorio, Carolina},
booktitle = {NeurIPS 2024 Workshops: BDU},
year = {2024},
url = {https://mlanthology.org/neuripsw/2024/choi2024neuripsw-bayesian/}
}