Surrogate-assisted microscopic traffic simulation-based optimisation of routing parameters

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3 Citations (Scopus)

Abstract

Reactive and predictive routing algorithms have to work fast and reliably for a large number of traffic participants. Therefore, simple rules and thresholds guide the routing decisions rather than extensive data collection and machine learning. In this paper, we optimise some of the thresholds governing the behaviour of a reactive and predictive routing algorithm by using the microscopic traffic simulator TraffSim. Microscopic traffic simulation is more exact than its macroscopic counterpart and very well suited to test the efficiency of a reactive and predictive routing algorithm. Unfortunately, it is also tremendously more computationally expensive, impairing the applicability of 'conventional' heuristic optimisation techniques like genetic algorithms or evolution strategies. Extensive use of surrogate models in an optimisation procedure is a promising alternative. Several variations of the efficient global optimisation (EGO) algorithm are tested and compared. Furthermore, a new type of surrogate model geared towards the parameter optimisation is presented.

Original languageEnglish
Pages (from-to)223-233
Number of pages11
JournalInternational Journal of Simulation and Process Modelling
Volume14
Issue number3
DOIs
Publication statusPublished - 2019

Keywords

  • Efficient global optimisation
  • EGO
  • HeuristicLab
  • Microscopic traffic simulation
  • Noisy optimisation
  • Routing algorithms
  • Surrogate assisted optimisation
  • TraffSim

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