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

TL;DR

BO4Mob is a comprehensive benchmark framework for high-dimensional Bayesian Optimization, specifically designed to address the complex challenge of origin-destination travel demand estimation in large urban road networks using realistic simulations.

Contribution

It introduces a new high-dimensional BO benchmark with real-world scenarios, facilitating development and evaluation of scalable optimization methods for urban mobility problems.

Findings

Demonstrated the benchmark's utility with five optimization methods.

Provided insights into the performance of BO algorithms in high-dimensional, stochastic settings.

Enabled future research in scalable urban mobility optimization.

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.