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Graph-Based Sparse Neural Networks for Traffic Signal Optimization

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cris.lastimport.scopus2024-02-12T20:10:57Z
dc.abstract.enWe investigate the performance of sparsely connected neural networks, with connectivity determined by road network graphs, for solving the Traffic Signal Setting optimization problem. We conducted experiments on three realistic road network topologies and found these types of graph neural networks superior to fully connected ones, both in terms of generalization properties on fixed test sets and—more importantly—near target function minima obtained in the gradient descent optimization process. We additionally confirm the soundness of our method by showing that random perturbations of the actual graph lead to consistent deterioration of model performance.
dc.affiliationUniwersytet Warszawski
dc.conference.countryNiemcy
dc.conference.datefinish2021-09-28
dc.conference.datestart2021-09-27
dc.conference.placeonline
dc.conference.series29th International Workshop on Concurrency, Specification and Programming (CS&P 2021)
dc.conference.series29th International Workshop on Concurrency, Specification and Programming (CS&P 2021)
dc.conference.seriesweblinkhttps://www2.informatik.hu-berlin.de/csp2021/
dc.conference.shortcutCS@P
dc.conference.weblinkhttps://www2.informatik.hu-berlin.de/csp2021/
dc.contributor.authorKlemenko, Arkadiusz
dc.contributor.authorMożejko, Marcin
dc.contributor.authorGora, Paweł
dc.contributor.authorSkowronek, Łukasz
dc.date.accessioned2024-01-25T02:22:34Z
dc.date.available2024-01-25T02:22:34Z
dc.date.issued2023
dc.description.financePublikacja bezkosztowa
dc.identifier.doi10.1007/978-3-031-26651-5_9
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/108110
dc.identifier.weblinkhttps://link.springer.com/chapter/10.1007/978-3-031-26651-5_9
dc.languageeng
dc.relation.pages203–227
dc.rightsClosedAccess
dc.sciencecloudnosend
dc.titleGraph-Based Sparse Neural Networks for Traffic Signal Optimization
dc.typeJournalArticle
dspace.entity.typePublication