为高维城市交通优化设计新基准，解决真实路网中出行需求估计难题。
BO4Mob: Bayesian Optimization Benchmarks for High-Dimensional Urban Mobility Problem
- 基于旧金山真实路网构建五种高维优化场景，输入维度达10,100。
- 采用高分辨率仿真模拟真实非线性、随机动态，评估五种优化算法性能。
- 适合研究可扩展优化算法与城市数字孪生建模的学者使用。
我们提出 extbf{BO4Mob}，一个面向高维贝叶斯优化（BO）的新基准框架，旨在应对大规模城市路网中起讫点（OD）出行需求估计这一挑战。从有限交通传感器数据中估计出行需求是一个困难的逆向优化问题，尤其在真实世界的大规模交通网络中更为复杂。该问题涉及高维连续空间的优化，每次目标评估都计算昂贵、具有随机性且不可微。BO4Mob 包含五个基于美国圣何塞市真实路网构建的场景，输入维度最高达10,100。这些场景利用高分辨率开源交通仿真，包含真实的非线性与随机动态。通过评估三种前沿贝叶斯优化算法和两种非贝叶斯基线方法，验证了该基准的实用性。该框架旨在支持可扩展优化算法的研发及其在数据驱动城市交通模型中的应用，包括大都市路网的高分辨率数字孪生系统。代码与文档已公开于 https://github.com/UMN-Choi-Lab/BO4Mob。
原文摘要 · Abstract (English)
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.
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