Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143197
Title: Model preemption based on dynamic analysis of simulation data to accelerate traffic light timing optimisation
Authors: Andelfinger, Philip
Udayakumar, Sajeev
Cai, Wentong
Eckhoff, David
Knoll, Alois
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Andelfinger, P., Udayakumar, S., Cai, W., Eckhoff, D., & Knoll, A. (2018). Model preemption based on dynamic analysis of simulation data to accelerate traffic light timing optimisation. Proceedings of the 2018 Winter Simulation Conference (WSC), 652-663. doi:10.1109/WSC.2018.8632411
Abstract: Since simulation-based optimisation typically requires large numbers of runs to identify sufficiently good solutions, the costs in terms of time and hardware can be enormous. To avoid unnecessary simulation runs, surrogate models can be applied, which estimate the simulation output under a given parameter combination. Model preemption is a related technique that dynamically analyses the simulation state at runtime to identify runs unlikely to result in a high-quality solution and terminates such runs early. However, existing work on model preemption relies on model-specific termination rules. In this paper, we describe an architecture for simulation-based optimisation using model preemption based on estimations of the simulation output. In a case study, the approach is applied to the optimisation of traffic light timings in a traffic simulation. We show that within a given time and hardware budget, model preemption enables the identification of higher-quality solutions than those found through traditional simulation-based optimisation.
URI: https://hdl.handle.net/10356/143197
ISBN: 978-1-5386-6573-2
DOI: 10.1109/WSC.2018.8632411
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/WSC.2018.8632411.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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