Surrogate-based simulation optimization approach for day-to-day dynamics model calibration with real data

Qixiu Cheng, Shuaian Wang, Zhiyuan Liu*, Yu Yuan

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

44 Citations (Scopus)

Abstract

This paper investigates the day-to-day dynamics model from the perspective of travelers’ actual route choice behaviors, and calibrates and validates the route-based day-to-day dynamics model with the real-world license plate recognition (LPR) data. Due to the highly nonlinear and multi-modal response function in the calibration of the optimization problem, traditional gradient-based nonlinear regression algorithms or other analytical optimization approaches are inapplicable to deal with the calibration work. In this paper, a surrogate-based simulation optimization approach is proposed to deal with the expensive-to-evaluate response function in the day-to-day dynamics calibration work. More specifically, the kriging metamodel is adopted to surrogate the optimization function of the calibration process. With this meta-modeling approach, a sound solution can be achieved with only a few sampling points in a comfortably afforded computation burden, thus giving a valid estimation of the parameters in the day-to-day dynamics model. Finally, a case study based on the real-world LPR data is conducted to validate the proposed model and calibration method.

Original languageEnglish
Pages (from-to)422-438
Number of pages17
JournalTransportation Research Part C: Emerging Technologies
Volume105
DOIs
Publication statusPublished - Aug 2019

Bibliographical note

Funding Information:
This study is supported by the General Projects (No. 71771050), the Key Projects (No. 51638004) of the National Natural Science Foundation of China , and the Scientific Research Foundation of Graduate School of Southeast University (No. YBPY1885 ).

Funding Information:
This study is supported by the General Projects (No. 71771050), the Key Projects (No. 51638004) of the National Natural Science Foundation of China, and the Scientific Research Foundation of Graduate School of Southeast University (No. YBPY1885).

Publisher Copyright:
© 2019 Elsevier Ltd

Keywords

  • Calibration
  • Day-to-day dynamics
  • License Plate Recognition (LPR) data
  • Simulation-based optimization
  • Traffic

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