Bayesian Optimization of Area-based Road Pricing

TL;DR

This paper introduces a Bayesian Optimization framework to efficiently determine optimal area- and time-based road pricing schemes in urban networks, improving social welfare through a data-driven approach.

Contribution

It develops a novel Bayesian Optimization method tailored for complex, high-dimensional road pricing problems incorporating heterogeneous demand and network models.

Findings

Bayesian Optimization quickly learns pricing-welfare relationships.

Optimal TODP schemes significantly enhance social welfare.

Method handles high-dimensional decision spaces effectively.

Abstract

This study presents a Bayesian Optimization framework for area- and distance-based time-of-day pricing (TODP) for urban networks. The road pricing optimization problem can reach high level of complexity depending on the pricing scheme considered, its associated detailed network properties and the affected heterogeneous demand features. We consider heterogeneous travellers with individual-specific trip attributes and departure-time choice parameters together with a Macroscopic Fundamental Diagram (MFD) model for the urban network. Its mathematical formulation is presented and an agent-based simulation framework is constructed as evaluation function for the TODP optimization problem. The latter becomes highly nonlinear and relying on an expensive-to-evaluate objective function. We then present and test a Bayesian Optimization approach to compute different time-of-day pricing schemes by maximizing social welfare. Our proposed method learns the relationship between the prices and welfare within a few iterations and is able to find good solutions even in scenarios with high dimensionality in the decision variables space, setting a path for complexity reduction in more realistic road pricing optimization problems. Furthermore and as expected, the simulation results show that TODP improves the social welfare against the no-pricing case.