Learning Micro-Macro Models for Traffic Control Using Microscopic Data

Jonathan Krook, Mladen Čičic, Karl Henrik Johansson

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Connected and Automated Vehicles (CAVs) are likely to have a large impact on the traffic in the near future. Assuming we are able to communicate some commands directly to them, it is of interest to know how CAVs can be used for traffic control. In order to achieve this, we need to understand how such controls affect the rest of the traffic. In this work, we study the influence of a CAV acting as a moving bottleneck, using the CAV's speed as a control input. We discuss the interpretation of the microscopic traffic data in the macroscopic framework, and propose nonparametric methods for learning the micro-macro model describing the interaction between the CAV and the surrounding traffic. We use only the local traffic data in the vicinity of the CAV, and design simple, targeted data collection experiments. This learned model is then used to predict the evolution of the traffic, and the predictions are compared with corresponding data from microscopic simulations.
Original languageEnglish (US)
Title of host publication2022 European Control Conference (ECC)
Number of pages6
ISBN (Print)9783907144077
StatePublished - Aug 5 2022
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-09-09
Acknowledged KAUST grant number(s): OSR-2019-CRG8-4033
Acknowledgements: This work has received funding from the KAUST Office of Sponsored Research under Award No. OSR-2019-CRG8-4033, VINNOVA within the FFI program under contract 2014-06200, the Swedish Research Council, the Swedish Foundation for Strategic Research, Knut and Alice Wallenberg Foundation, and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement 694209).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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