Partial outer convexification for traffic light optimization in road networks

We consider the problem of computing optimal traffic light programs for urban road intersections using traffic flow conservation laws on networks. Based on a partial outer convexification approach, which has been successfully applied in the area of mixed-integer optimal control for systems of ordina...

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Hauptverfasser: Göttlich, Simone (VerfasserIn) , Potschka, Andreas (VerfasserIn) , Ziegler, Ute (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: February 1, 2017
In: SIAM journal on scientific computing
Year: 2017, Jahrgang: 39, Heft: 1, Pages: B53-B75
ISSN:1095-7197
DOI:10.1137/15M1048197
Online-Zugang:Verlag, Volltext: http://dx.doi.org/10.1137/15M1048197
Verlag, Volltext: https://epubs.siam.org/doi/10.1137/15M1048197
Volltext
Verfasserangaben:SimoneGöttlich, Andreas Potschka, and Ute Ziegler

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520 |a We consider the problem of computing optimal traffic light programs for urban road intersections using traffic flow conservation laws on networks. Based on a partial outer convexification approach, which has been successfully applied in the area of mixed-integer optimal control for systems of ordinary or differential algebraic equations, we develop a computationally tractable two-stage solution heuristic. The two-stage approach consists of the solution of a (smoothed) nonlinear programming problem with dynamic constraints and a reconstruction mixed-integer linear program without dynamic constraints. The two-stage approach is founded on a discrete approximation lemma for partial outer convexification, whose grid-independence properties for (smoothed) conservation laws are investigated. We use the two-stage approach to compute traffic light programs for two scenarios on different discretizations and demonstrate that the solution candidates cannot be improved in a reasonable amount of time by global state-of-the-art mixed-integer nonlinear programming solvers. The two-stage solution candidates are not only better than results obtained by global optimization of piecewise linearized traffic flow models but also can be computed at a faster rate. 
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