Estimating the Robustness of Public Transport Systems Using Machine Learning

• URL: http://arxiv.org/abs/2106.08967v1
• Date: Thu, 10 Jun 2021 05:52:56 GMT
• Title: Estimating the Robustness of Public Transport Systems Using Machine Learning
• Authors: Matthias M\"uller-Hannemann and Ralf R\"uckert and Alexander Schiewe and Anita Sch\"obel
• Abstract summary: Planning public transport systems is a highly complex process involving many steps. Integrating robustness from a passenger's point of view makes the task even more challenging. In this paper, we explore a new way of such a scenario-based robustness approximation by using methods from machine learning.
• Score: 62.997667081978825
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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