Robust Railway Network Design based on Strategic Timetables

TL;DR

This paper introduces a mixed-integer linear programming model for designing railway networks based on strategic timetables, incorporating robustness to timetable uncertainties and demand variations.

Contribution

It presents a novel optimization framework that integrates timetable-based demand, infrastructure costs, and robustness considerations for railway network design.

Findings

The model effectively minimizes infrastructure costs while satisfying timetable demand.

Robust optimization accounts for timetable uncertainties and demand fluctuations.

Computational results demonstrate the model's applicability and robustness.

Abstract

Using strategic timetables as input for railway network design has become increasingly popular among western European railway infrastructure operators. Although both railway timetabling and railway network design on their own are well covered by academic research, there is still a gap in the literature concerning timetable-based network design. Therefore, we propose a mixed-integer linear program to design railway infrastructure so that the demand derived from a strategic timetable can be satisfied with minimal infrastructure costs. The demand is given by a list of trains, each featuring start and destination nodes as well as time bounds and a set of frequency and transfer constraints that capture the strategic timetable's main characteristics. During the optimization, the solver decides which railway lines need to be built or expanded and whether travel or headway times must be shortened to meet the demand. Since strategic timetables are subject to uncertainty, we expand the optimization model to a robust version. Uncertain timetables are modelled as discrete scenarios, while uncertain freight train demand is modelled using optional trains, which can be inserted into the resulting timetable if they do not require additional infrastructure. We present computational results for both the deterministic and the robust case and give an outlook on further research.