Estimating the Robustness of Public Transport Systems Using Machine Learning

TL;DR

This paper presents a machine learning-based method to efficiently estimate the robustness of public transport systems under various scenarios, enabling faster and accurate planning decisions.

Contribution

It introduces a neural network approach that predicts transport system robustness from key features, significantly reducing computational costs compared to traditional simulation methods.

Findings

High accuracy in robustness prediction across benchmark instances

Prediction time is constant, enabling real-time decision support

Method outperforms existing simulation-based approaches in efficiency

Abstract

The planning of attractive and cost efficient public transport systems is a highly complex optimization process involving many steps. Integrating robustness from a passenger's point of view makes the task even more challenging. With numerous different definitions of robustness in literature, a real-world acceptable evaluation of the robustness of a public transport system is to simulate its performance under a large number of possible scenarios. Unfortunately, this is computationally very expensive. In this paper, we therefore explore a new way of such a scenario-based robustness approximation by using methods from machine learning. We achieve a fast approach with a very high accuracy by gathering a subset of key features of a public transport system and its passenger demand and training an artificial neural network to learn the outcome of a given set of robustness tests. The network is then able to predict the robustness of untrained instances with high accuracy using only its key features, allowing for a robustness oracle for transport planners that approximates the robustness in constant time. Such an oracle can be used as black box to increase the robustness within a local search framework for integrated public transportation planning. In computational experiments with different benchmark instances we demonstrate an excellent quality of our predictions.