Using machine learning and object detection for signal-controlled intersections

Harry Spaabæk Lahrmann, Andreas Berre Eriksen, Jakob Haahr Taankvist, Mikkel Færgemand Hansen, Kim Guldstrand Larsen

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

This paper presents new principles for managing signal-controlled intersections. By using machine learning and object detection as substitutes for point detection and offset between intersections, a controller for signal-controlled intersections has been developed using the optimization program UPPAAL. Using the micro simulation program VISSIM, the controller has been tested in four signal-controlled and coordinated intersections in the street Hobrovej in Aalborg, Denmark. The simulation shows that in comparison with the existing controller, this controller provides a reduction of between 30% and 50% in average delays, queues and number of stops. The fuel consumption and total travel time of cars in the coordinated section are reduced by about 20% in the simulation study.
Original languageEnglish
Title of host publicationITS 2018 Conference Proceedings : Transport network operations
Number of pages10
Volume6
Place of PublicationCopenhagen
PublisherITS World
Publication date2018
Article numberEU-TP1618
Publication statusPublished - 2018
Event25th ITS World Congress - Quality of Life - Bella Center, Copenhagen, Denmark
Duration: 17 Sep 201821 Sep 2018
https://itsworldcongress.com/

Conference

Conference25th ITS World Congress - Quality of Life
LocationBella Center
CountryDenmark
CityCopenhagen
Period17/09/201821/09/2018
Internet address

Fingerprint

Learning systems
Controllers
Travel time
Fuel consumption
Railroad cars
Object detection

Bibliographical note

Paper presented in SP7 - Data and information

Keywords

  • Signal-controlled intersection
  • Machine learning
  • Object detection

Cite this

Lahrmann, H. S., Eriksen, A. B., Taankvist, J. H., Hansen, M. F., & Larsen, K. G. (2018). Using machine learning and object detection for signal-controlled intersections. In ITS 2018 Conference Proceedings: Transport network operations (Vol. 6). [EU-TP1618] Copenhagen: ITS World.
Lahrmann, Harry Spaabæk ; Eriksen, Andreas Berre ; Taankvist, Jakob Haahr ; Hansen, Mikkel Færgemand ; Larsen, Kim Guldstrand. / Using machine learning and object detection for signal-controlled intersections. ITS 2018 Conference Proceedings: Transport network operations. Vol. 6 Copenhagen : ITS World, 2018.
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title = "Using machine learning and object detection for signal-controlled intersections",
abstract = "This paper presents new principles for managing signal-controlled intersections. By using machine learning and object detection as substitutes for point detection and offset between intersections, a controller for signal-controlled intersections has been developed using the optimization program UPPAAL. Using the micro simulation program VISSIM, the controller has been tested in four signal-controlled and coordinated intersections in the street Hobrovej in Aalborg, Denmark. The simulation shows that in comparison with the existing controller, this controller provides a reduction of between 30{\%} and 50{\%} in average delays, queues and number of stops. The fuel consumption and total travel time of cars in the coordinated section are reduced by about 20{\%} in the simulation study.",
keywords = "Signal-controlled intersection, Machine learning, Object detection, Signal-controlled intersection, Machine learning, Object detection",
author = "Lahrmann, {Harry Spaab{\ae}k} and Eriksen, {Andreas Berre} and Taankvist, {Jakob Haahr} and Hansen, {Mikkel F{\ae}rgemand} and Larsen, {Kim Guldstrand}",
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Lahrmann, HS, Eriksen, AB, Taankvist, JH, Hansen, MF & Larsen, KG 2018, Using machine learning and object detection for signal-controlled intersections. in ITS 2018 Conference Proceedings: Transport network operations. vol. 6, EU-TP1618, ITS World, Copenhagen, 25th ITS World Congress - Quality of Life, Copenhagen, Denmark, 17/09/2018.

Using machine learning and object detection for signal-controlled intersections. / Lahrmann, Harry Spaabæk; Eriksen, Andreas Berre; Taankvist, Jakob Haahr; Hansen, Mikkel Færgemand; Larsen, Kim Guldstrand.

ITS 2018 Conference Proceedings: Transport network operations. Vol. 6 Copenhagen : ITS World, 2018. EU-TP1618.

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

TY - GEN

T1 - Using machine learning and object detection for signal-controlled intersections

AU - Lahrmann, Harry Spaabæk

AU - Eriksen, Andreas Berre

AU - Taankvist, Jakob Haahr

AU - Hansen, Mikkel Færgemand

AU - Larsen, Kim Guldstrand

N1 - Paper presented in SP7 - Data and information

PY - 2018

Y1 - 2018

N2 - This paper presents new principles for managing signal-controlled intersections. By using machine learning and object detection as substitutes for point detection and offset between intersections, a controller for signal-controlled intersections has been developed using the optimization program UPPAAL. Using the micro simulation program VISSIM, the controller has been tested in four signal-controlled and coordinated intersections in the street Hobrovej in Aalborg, Denmark. The simulation shows that in comparison with the existing controller, this controller provides a reduction of between 30% and 50% in average delays, queues and number of stops. The fuel consumption and total travel time of cars in the coordinated section are reduced by about 20% in the simulation study.

AB - This paper presents new principles for managing signal-controlled intersections. By using machine learning and object detection as substitutes for point detection and offset between intersections, a controller for signal-controlled intersections has been developed using the optimization program UPPAAL. Using the micro simulation program VISSIM, the controller has been tested in four signal-controlled and coordinated intersections in the street Hobrovej in Aalborg, Denmark. The simulation shows that in comparison with the existing controller, this controller provides a reduction of between 30% and 50% in average delays, queues and number of stops. The fuel consumption and total travel time of cars in the coordinated section are reduced by about 20% in the simulation study.

KW - Signal-controlled intersection

KW - Machine learning

KW - Object detection

KW - Signal-controlled intersection

KW - Machine learning

KW - Object detection

M3 - Article in proceeding

VL - 6

BT - ITS 2018 Conference Proceedings

PB - ITS World

CY - Copenhagen

ER -

Lahrmann HS, Eriksen AB, Taankvist JH, Hansen MF, Larsen KG. Using machine learning and object detection for signal-controlled intersections. In ITS 2018 Conference Proceedings: Transport network operations. Vol. 6. Copenhagen: ITS World. 2018. EU-TP1618