Calibrating heterogeneous car-following models for human drivers in oscillatory traffic conditions

Mingfeng Shang, Raphael Stern

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

1 Scopus citations

Abstract

Accurately modeling the realistic and unstable traffic dynamics of human-driven traffic flow is crucial to being able to to understand how traffic dynamics evolve, and how new agents such as autonomous vehicles might influence traffic flow stability. This work is motivated by a recent dataset that allows us to calibrate accurate models, specifically in conditions when traffic waves arise. Three microscopic carfollowing models are calibrated using a microscopic vehicle trajectory dataset that is collected with the intent of capturing oscillatory driving conditions. For each model, five traffic flow metrics are constructed to compare the flow-level characteristics of the simulated traffic with experimental data. The results show that the optimal velocity-follow the leader (OV-FTL) model and the optimal velocity relative velocity model (OVRV) model are both able to reproduce the traffic flow oscillations, while the intelligent driver model (IDM) model requires substantially more noise in each driver's speed profile to exhibit the same waves.

Original languageEnglish (US)
Title of host publication2020 Forum on Integrated and Sustainable Transportation Systems, FISTS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages101-106
Number of pages6
ISBN (Electronic)9781728195032
DOIs
StatePublished - Nov 3 2020
Event2020 Forum on Integrated and Sustainable Transportation Systems, FISTS 2020 - Delft, Netherlands
Duration: Nov 3 2020Nov 5 2020

Publication series

Name2020 Forum on Integrated and Sustainable Transportation Systems, FISTS 2020

Conference

Conference2020 Forum on Integrated and Sustainable Transportation Systems, FISTS 2020
CountryNetherlands
CityDelft
Period11/3/2011/5/20

Bibliographical note

Funding Information:
This work is supported by the University of Minnesota Center for Transportation Studies Faculty Fellows Program.

Publisher Copyright:
© 2020 IEEE.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Big Data and Naturalistic Datasets
  • Control and Simulation; Big Data and Naturalistic Datasets
  • Modeling
  • Modeling, Control and Simulation

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