Robustness as a Third Dimension for Evaluating Public Transport Plans

Friedrich, Markus; Müller‐Hannemann, Matthias; Rückert, Ralf; Schiewe, Alexander; Schöbel, Anita

doi:10.4230/oasics.atmos.2018.4

Cited by 2 publications

(6 citation statements)

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“…To do that, we implemented a realistic model, where passengers do not know the route choices of other passengers. In particular, they cannot base their own decisions on the available free vehicle capacity which in turn depends on the routes of the other [16], see Fig. 2 Table 1: Robustness tests RT1-RT4 with a description and a motivation, as well as the parameters used in our experiments.…”

Section: Public Transport Systemsmentioning

confidence: 99%

“…Friedrich et al [15,16] introduced several robustness tests for evaluating public transport systems on which this work is based on. However, we would like to point out that the machine learning approach which we present in the following can also be used with other ways to evaluate the robustness of a public transport system.…”

Section: Robustness Testsmentioning

confidence: 99%

“…We call this the robustness value of the simulation. We use the following four robustness tests [16]. For a better understanding of how these tests work we give a detailed explanation of the first test as well as a short description of the other three.…”

Section: Robustness Testsmentioning

confidence: 99%

“…Measuring the effect of disruptions for passengers has been provided, for example, in De-Los-Santos et al [10], Cats [5], and Lu [30]. Friedrich et al [15,16] simulate delays and measure the occurring changes in arrival time for passengers.…”

Section: Introductionmentioning

confidence: 99%

“…We demonstrate our results using the robustness evaluation of Friedrich et al [16], but our learning approach can also be applied to other evaluation routines as long as the evaluation is based on simulating delay scenarios. Based on experiments with real-world and artificial benchmark instances we show that an excellent estimation of robustness measures for several robustness tests can be achieved.…”

Section: Introductionmentioning

confidence: 99%

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Estimating the Robustness of Public Transport Systems Using Machine Learning

Müller‐Hannemann¹,

Rückert²,

Schiewe³

et al. 2021

Preprint

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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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Section: Public Transport Systemsmentioning

confidence: 99%

Section: Robustness Testsmentioning

confidence: 99%

Section: Robustness Testsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Estimating the Robustness of Public Transport Systems Using Machine Learning

Müller‐Hannemann¹,

Rückert²,

Schiewe³

et al. 2021

Preprint

Self Cite

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Robustness and disturbances in public transport

Voß

Xie

2022

Public Transp

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Network-based systems are at the core of our everyday life. Whether it is electronic networking, electricity grids or transportation, users expect the networks to function properly and provide a feeling of safety and security. However, there may be disturbances. In this paper, we consider disturbances in the context of public transportation. The focus in this respect is on public transport planning and operations. To classify and cope with disturbances, one can find many ideas, including robustness, resilience, vulnerability, disruption mitigation or delay management. We survey related streams of literature and put them into perspective. As a major insight we show that different strands of literature exist that may benefit from becoming better connected and intertwined. Together with recent advances in information technology and solution methods, more integrated problem settings incorporating robustness and disturbances can play a major role in future planning and operations.

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Robustness as a Third Dimension for Evaluating Public Transport Plans

Cited by 2 publications

References 0 publications

Estimating the Robustness of Public Transport Systems Using Machine Learning

Estimating the Robustness of Public Transport Systems Using Machine Learning

Robustness and disturbances in public transport

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