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.

翻译：我们提出了 **BO4Mob**，一个针对高维贝叶斯优化（BO）的新基准测试框架，其设计源于大型城市路网中起点-终点（OD）出行需求估计的挑战。从有限的交通传感器数据中估计OD出行需求是一个困难的逆优化问题，尤其是在现实世界的大规模交通网络中。该问题涉及在高维连续空间上进行优化，其中每次目标函数评估都计算成本高昂、具有随机性且不可微。BO4Mob包含基于美国加州圣何塞市真实路网的五个场景，其输入维度最高可达10,100维。这些场景利用了高分辨率、开源的交通仿真，其中包含了真实的非线性和随机动态。我们通过评估五种优化方法（三种先进的BO算法和两种非BO基线方法）展示了该基准的实用性。该基准旨在支持可扩展优化算法的开发及其在数据驱动的城市交通模型设计中的应用，包括大都市路网的高分辨率数字孪生。代码与文档可在 https://github.com/UMN-Choi-Lab/BO4Mob 获取。