Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach

Qu, X; Yu, Y; Zhou, M; Lin, CT; Wang, X

Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach

Qu, X Yu, Y Zhou, M Lin, CT Wang, X

Permalink

Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Applied Energy, 2020, 257
Issue Date:: 2020-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

The embargo period expires on 1 Jan 2022

Adobe PDF

Download Accepted Manuscript VersionAdobe PDF (1.1 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Qu, X
dc.contributor.author	Yu, Y
dc.contributor.author	Zhou, M
dc.contributor.author	Lin, CT
dc.contributor.author	Wang, X
dc.date.accessioned	2021-02-24T20:30:26Z
dc.date.available	2021-02-24T20:30:26Z
dc.date.issued	2020-01-01
dc.identifier.citation	Applied Energy, 2020, 257
dc.identifier.issn	0306-2619
dc.identifier.issn	1872-9118
dc.identifier.uri	http://hdl.handle.net/10453/146412
dc.description.abstract	© 2019 Elsevier Ltd It has been well recognized that human driver's limits, heterogeneity, and selfishness substantially compromise the performance of our urban transport systems. In recent years, in order to deal with these deficiencies, our urban transport systems have been transforming with the blossom of key vehicle technology innovations, most notably, connected and automated vehicles. In this paper, we develop a car following model for electric, connected and automated vehicles based on reinforcement learning with the aim to dampen traffic oscillations (stop-and-go traffic waves) caused by human drivers and improve electric energy consumption. Compared to classical modelling approaches, the proposed reinforcement learning based model significantly reduces the modelling constraints and has the capability of self-learning and self-correction. Experiment results demonstrate that the proposed model is able to improve travel efficiency by reducing the negative impact of traffic oscillations, and it can also reduce the average electric energy consumption.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Applied Energy
dc.relation.isbasedon	10.1016/j.apenergy.2019.114030
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	09 Engineering, 14 Economics
dc.subject.classification	Energy
dc.title	Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach
dc.type	Journal Article
utslib.citation.volume	257
utslib.for	09 Engineering
utslib.for	14 Economics
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2022-01-01T00:00:00+1000Z
dc.date.updated	2021-02-24T20:30:23Z
pubs.publication-status	Published
pubs.volume	257

Abstract:

© 2019 Elsevier Ltd It has been well recognized that human driver's limits, heterogeneity, and selfishness substantially compromise the performance of our urban transport systems. In recent years, in order to deal with these deficiencies, our urban transport systems have been transforming with the blossom of key vehicle technology innovations, most notably, connected and automated vehicles. In this paper, we develop a car following model for electric, connected and automated vehicles based on reinforcement learning with the aim to dampen traffic oscillations (stop-and-go traffic waves) caused by human drivers and improve electric energy consumption. Compared to classical modelling approaches, the proposed reinforcement learning based model significantly reduces the modelling constraints and has the capability of self-learning and self-correction. Experiment results demonstrate that the proposed model is able to improve travel efficiency by reducing the negative impact of traffic oscillations, and it can also reduce the average electric energy consumption.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/146412