Utilizing Bayesian Optimization for Timetable-Independent Railway Junction Performance Determination

Abstract

The efficiency of railway infrastructure is significantly influenced by the mix of trains that utilize it, as different service types have competing operational requirements. While freight services might require extended service times, passenger services demand more predictable schedules. Traditional methods for addressing long-term traffic assignment problems often rely on fixed-value capacity limitations, determined based on specific assumptions about traffic composition. This paper introduces a methodology for determining timetable-independent capacity within the traffic rate assignment problem, enabling the calculation of junction capacities under dynamic traffic distributions. We solve the underlying non-linear constrained optimization problem maximizing the traffic throughput using Bayesian optimization (BO). This setting combines a known objective function with expensive- to-compute capacity constraints, motivating an adaption of standard BO problems, where objective functions are usually unknown. We tailor the acquisition process in BO to this specific setting and increase performance by incorporating prior knowledge about the shape of the constraint functions into the Gaussian process surrogate model. Our derived approaches are benchmarked on a railway junction near Paris, significantly outperforming fixed traffic composition models and highlighting the benefits of dynamic capacity allocation.