Publication Cover
Transportation Letters
The International Journal of Transportation Research
Volume 13, 2021 - Issue 4
561
Views
4
CrossRef citations to date
0
Altmetric
Article

Calibrating microscopic traffic simulators using machine learning and particle swarm optimization

, ORCID Icon, , &
Pages 295-307 | Received 31 Jul 2019, Accepted 02 Feb 2020, Published online: 11 Feb 2020
 

ABSTRACT

When performing microscopic traffic simulations, a premise is to have appropriately calibrated parameter values. This is often computationally expensive as it requires repeatedly running simulations. In this paper, we propose a machine learning (ML) + particle swarm optimization (PSO)-based methodology for calibrating microscopic traffic simulator parameters to improve computational efficiency. We first develop ML models that input the parameters to predict simulation outputs. Four machine learning models: decision tree, support vector machine, Gaussian process regression, and artificial neural networks are considered. The best-performing model is then embedded in PSO to seek the set of parameters that minimizes the difference between the predicted simulation outputs and the field observations. The ML+PSO methodology is applied to TransModeler using field data in Shanghai, China. We find that artificial neural networks yield the best prediction accuracy. Furthermore, PSO with embedded artificial neural networks shows superior computational efficiency and effectiveness..

Disclosure statement

No potential conflict of interest was reported by the authors.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.