BO4Mob: Bayesian Optimization Benchmarks for High-Dimensional Urban Mobility Problem

Seunghee Ryu, Donghoon Kwon, Seongjin Choi, Aryan Deshwal, Seungmo Kang, Carolina Osorio

Keywords: Bayesian Optimization, High-dimensional BO, Urban Mobility Problem, OD estimation, Simulation-based Optimization

TL;DR: We introduce BO4Mob, a new Bayesian Optimization (BO) benchmark framework for origin-destination (OD) travel demand estimation as high-dimensional urban mobility problem.

Abstract: We introduce 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.

Dataset URL: https://github.com/UMN-Choi-Lab/BO4Mob_data

Code URL: https://github.com/UMN-Choi-Lab/BO4Mob

Primary Area: Dataset and Benchmark for Optimization (e.g., convex and non-convex, stochastic, robust, metrics for optimization, scaling of datasets, benchmarks)

Submission Number: 623