BO4Mob: Bayesian Optimization Benchmarks for High-Dimensional Urban Mobility Problem
This provides a benchmark for developing scalable optimization algorithms to address high-dimensional, computationally expensive problems in urban mobility, such as digital twins, but it is incremental as it builds on existing BO methods.
The authors tackled the problem of high-dimensional Bayesian Optimization (BO) for origin-destination travel demand estimation in urban road networks by introducing BO4Mob, a benchmark framework with scenarios scaling up to 10,100 dimensions, and demonstrated its utility by evaluating five optimization methods.
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.