Coping with Randomness in Highly Complex Sys-tems Using the Example of Quantum-Inspired Traffic Flow Optimization

Authors

DOI:

https://doi.org/10.52825/scp.v4i.216

Keywords:

Statistics, Traffic simulation, Optimisation

Abstract

Developing new solutions to complicated large-scale problems typically requires large-scale numerical simulation. Therefore, traffic simulations often run against randomized simulations instead of real-world traffic situations. This paper demonstrates a method to calculate the statistical significance of numerical simulations and optimizations in the presence of numerous random variables in complex systems using one-sided paired t-tests. While the paper covers a specific Fujitsu traffic-optimization project which uses SUMO for simulating the traffic situation, the method can be applied to many similar projects where a complete investigation of the solution space is not feasible due to the size of the solution space.

Downloads

Download data is not yet available.

References

Aramon, M., Rosenberg, G., Valiante, E., Miyazawa, T., Tamura, H., & Katzgraber, H., Physics-Inspired Optimization for Quadratic Unconstrained Problems Using a Digi-tal Annealer. Front. Phys., 05 April 2019, doi: https://doi.org/10.3389/fphy.2019.00048. DOI: https://doi.org/10.3389/fphy.2019.00048

F. Schinkel, I. Schwende, R. Schade, E. Cerny, M. Fellendorf, Traffic management through traffic signal control by Quantum-Inspired optimization. 27th ITS World Con-gress, Hamburg, Germany, 2021.

Pablo Alvarez Lopez, Michael Behrisch, Laura Bieker-Walz, Jakob Erdmann, Yun-Pang Flötteröd, Robert Hilbrich, Leonhard Lücken, Johannes Rummel, Peter Wag-ner, Evamarie Wießner, Microscopic Traffic Simulation using SUMO. IEEE Intelligent Transportation Systems Conference (ITSC), 2018, doi: https://doi.org/10.1109/ITSC.2018.8569938. DOI: https://doi.org/10.1109/ITSC.2018.8569938

Forschungsgesellschaft für Straßen- und Verkehrswesen. Richtlinien für Lichtsignal-anlagen - Lichtzeichenanlagen für den Straßenverkehr. Berlin: FGSV. 2015.

M. Matsumoto, T. Nishimura, Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Transactions on Modeling and Computer Simulation, 1998, doi: https://doi.org/10.1145/272991.272995. DOI: https://doi.org/10.1145/272991.272995

J. Hedderich, L. Sachs, Hypothesentest, Angewandte Statistik. Berlin, Heidelberg: Springer Spektrum, 2018. DOI: https://doi.org/10.1007/978-3-662-56657-2

J. Frost, Cohens D: Definition, Using & Examples, Statistics by Jim: https://statisticsbyjim.com/basics/cohens-d/, (04/2023).

Hemmerich, W., Statistik Guru: Cohen’s d für den gepaarten t-Test berechnen, Statis-tics Guru: https://statistikguru.de/rechner/cohens-d-gepaarter-t-test.html, (04/2023).

B. Walther, T-Test bei abhängigen Stichproben in Excel durchführen, https://bjoernwalther.com/t-test-bei-abhaengigen-stichproben-in-excel/, (04/2023).

Downloads

Published

2023-06-29

How to Cite

Haberland, M., & Hohmuth, L. (2023). Coping with Randomness in Highly Complex Sys-tems Using the Example of Quantum-Inspired Traffic Flow Optimization. SUMO Conference Proceedings, 4, 65–74. https://doi.org/10.52825/scp.v4i.216

Conference Proceedings Volume

Section

Conference papers