Robust Line Planning in case of Multiple Pools and Disruptions

TL;DR

This paper introduces a robust line planning mechanism for public transportation that efficiently adapts to multiple pools and disruptions, ensuring rapid convergence to optimal solutions in various scenarios.

Contribution

It presents a novel mechanism for robust line planning with multiple pools and differing utility functions, validated through extensive experiments on synthetic and real data.

Findings

Fast convergence to optimal solutions in diverse scenarios

Effective online recovery from disruptions

Versatility across synthetic and real-world data

Abstract

We consider the line planning problem in public transportation, under a robustness perspective. We present a mechanism for robust line planning in the case of multiple line pools, when the line operators have a different utility function per pool. We conduct an experimental study of our mechanism on both synthetic and real-world data that shows fast convergence to the optimum. We also explore a wide range of scenarios, varying from an arbitrary initial state (to be solved) to small disruptions in a previously optimal solution (to be recovered). Our experiments with the latter scenario show that our mechanism can be used as an online recovery scheme causing the system to re-converge to its optimum extremely fast.