{"ID":2852363,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.18824","arxiv_id":"2510.18824","title":"BO4Mob: Bayesian Optimization Benchmarks for High-Dimensional Urban Mobility Problem","abstract":"We introduce \\textbf{BO4Mob}, a new benchmark framework for high-dimensional Bayesian Optimization (BO), driven by the challenge of origin-destination (OD) travel demand estimation in large urban road networks. Estimating OD travel demand from limited traffic sensor data is a difficult inverse optimization problem, particularly in real-world, large-scale transportation networks. This problem involves optimizing over high-dimensional continuous spaces where each objective evaluation is computationally expensive, stochastic, and non-differentiable. BO4Mob comprises five scenarios based on real-world San Jose, CA road networks, with input dimensions scaling up to 10,100. These scenarios utilize high-resolution, open-source traffic simulations that incorporate realistic nonlinear and stochastic dynamics. We demonstrate the benchmark's utility by evaluating five optimization methods: three state-of-the-art BO algorithms and two non-BO baselines. This benchmark is designed to support both the development of scalable optimization algorithms and their application for the design of data-driven urban mobility models, including high-resolution digital twins of metropolitan road networks. Code and documentation are available at https://github.com/UMN-Choi-Lab/BO4Mob.","short_abstract":"We introduce \\textbf{BO4Mob}, a new benchmark framework for high-dimensional Bayesian Optimization (BO), driven by the challenge of origin-destination (OD) travel demand estimation in large urban road networks. Estimating OD travel demand from limited traffic sensor data is a difficult inverse optimization problem, par...","url_abs":"https://arxiv.org/abs/2510.18824","url_pdf":"https://arxiv.org/pdf/2510.18824v1","authors":"[\"Seunghee Ryu\",\"Donghoon Kwon\",\"Seongjin Choi\",\"Aryan Deshwal\",\"Seungmo Kang\",\"Carolina Osorio\"]","published":"2025-10-21T17:22:28Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":607990,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2852363,"paper_url":"https://arxiv.org/abs/2510.18824","paper_title":"BO4Mob: Bayesian Optimization Benchmarks for High-Dimensional Urban Mobility Problem","repo_url":"https://github.com/UMN-Choi-Lab/BO4Mob","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}