Reducing Traffic Congestion in Urban Areas via Real-Time Re-Routing: A Simulation Study

Mauro Vallati; Lukáš Chrpa

doi:10.1007%2F978-3-030-64984-5_6

Reducing Traffic Congestion in Urban Areas via Real-Time Re-Routing: A Simulation Study

Mauro Vallati, Lukáš Chrpa

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

Traffic congestion problems of urban road networks are having a strong impact on economy, due to losses from accidents and delays, and to public health. The recent progress in connected vehicles is expanding the approaches that can be exploited to tackle traffic congestion, particularly in urban regions. Connected vehicles pave the way to centralised real-time re-routing, where a urban traffic controller can suggest alternative routes to be followed in order to reduce delays and mitigate congestion issues in the network. In this work, we introduce a centralised architecture and we compare in simulation a number of approaches that can be exploited for re-routing vehicles.

Original language	English
Title of host publication	Proceedings of the 33rd Australasian Joint Conference on Artificial Intelligence
Subtitle of host publication	(AI2020)
Editors	Marcus Gallegher, Nour Moustafa, Erandi Lakshika
Place of Publication	Cham
Publisher	Springer Nature Switzerland AG
Pages	69-81
Number of pages	12
Volume	12576 LNCS/LNAI
ISBN (Electronic)	9783030649845
ISBN (Print)	9783030649838
DOIs	https://doi.org/10.1007%2F978-3-030-64984-5_6
Publication status	Published - 27 Nov 2020
Event	33rd Australasian Joint Conference on Artificial Intelligence - Virtual conference due to COVID-19 Duration: 29 Nov 2020 → 30 Nov 2020 Conference number: 33 http://www.ajcai2020.net/

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Nature Switzerland AG
Volume	12576 LNCS/LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	33rd Australasian Joint Conference on Artificial Intelligence
Abbreviated title	AI2020
Period	29/11/20 → 30/11/20
Internet address	http://www.ajcai2020.net/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007%2F978-3-030-64984-5_6Licence: Unspecified

Traffic_Rerouting___AJCAI_2020Accepted author manuscript, 442 KBLicence: Unspecified

Cite this

Vallati, M., & Chrpa, L. (2020). Reducing Traffic Congestion in Urban Areas via Real-Time Re-Routing: A Simulation Study. In M. Gallegher, N. Moustafa, & E. Lakshika (Eds.), Proceedings of the 33rd Australasian Joint Conference on Artificial Intelligence: (AI2020) (Vol. 12576 LNCS/LNAI, pp. 69-81). (Lecture Notes in Computer Science; Vol. 12576 LNCS/LNAI). Springer Nature Switzerland AG. https://doi.org/10.1007%2F978-3-030-64984-5_6

Vallati, Mauro ; Chrpa, Lukáš. / Reducing Traffic Congestion in Urban Areas via Real-Time Re-Routing : A Simulation Study. Proceedings of the 33rd Australasian Joint Conference on Artificial Intelligence: (AI2020). editor / Marcus Gallegher ; Nour Moustafa ; Erandi Lakshika. Vol. 12576 LNCS/LNAI Cham : Springer Nature Switzerland AG, 2020. pp. 69-81 (Lecture Notes in Computer Science).

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abstract = "Traffic congestion problems of urban road networks are having a strong impact on economy, due to losses from accidents and delays, and to public health. The recent progress in connected vehicles is expanding the approaches that can be exploited to tackle traffic congestion, particularly in urban regions. Connected vehicles pave the way to centralised real-time re-routing, where a urban traffic controller can suggest alternative routes to be followed in order to reduce delays and mitigate congestion issues in the network. In this work, we introduce a centralised architecture and we compare in simulation a number of approaches that can be exploited for re-routing vehicles.",

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Vallati, M & Chrpa, L 2020, Reducing Traffic Congestion in Urban Areas via Real-Time Re-Routing: A Simulation Study. in M Gallegher, N Moustafa & E Lakshika (eds), Proceedings of the 33rd Australasian Joint Conference on Artificial Intelligence: (AI2020). vol. 12576 LNCS/LNAI, Lecture Notes in Computer Science, vol. 12576 LNCS/LNAI, Springer Nature Switzerland AG, Cham, pp. 69-81, 33rd Australasian Joint Conference on Artificial Intelligence, 29/11/20. https://doi.org/10.1007%2F978-3-030-64984-5_6

Reducing Traffic Congestion in Urban Areas via Real-Time Re-Routing : A Simulation Study. / Vallati, Mauro; Chrpa, Lukáš.

Proceedings of the 33rd Australasian Joint Conference on Artificial Intelligence: (AI2020). ed. / Marcus Gallegher; Nour Moustafa; Erandi Lakshika. Vol. 12576 LNCS/LNAI Cham : Springer Nature Switzerland AG, 2020. p. 69-81 (Lecture Notes in Computer Science; Vol. 12576 LNCS/LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Traffic congestion problems of urban road networks are having a strong impact on economy, due to losses from accidents and delays, and to public health. The recent progress in connected vehicles is expanding the approaches that can be exploited to tackle traffic congestion, particularly in urban regions. Connected vehicles pave the way to centralised real-time re-routing, where a urban traffic controller can suggest alternative routes to be followed in order to reduce delays and mitigate congestion issues in the network. In this work, we introduce a centralised architecture and we compare in simulation a number of approaches that can be exploited for re-routing vehicles.

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Vallati M, Chrpa L. Reducing Traffic Congestion in Urban Areas via Real-Time Re-Routing: A Simulation Study. In Gallegher M, Moustafa N, Lakshika E, editors, Proceedings of the 33rd Australasian Joint Conference on Artificial Intelligence: (AI2020). Vol. 12576 LNCS/LNAI. Cham: Springer Nature Switzerland AG. 2020. p. 69-81. (Lecture Notes in Computer Science). Epub 2020 Nov 27. doi: 10.1007%2F978-3-030-64984-5_6

Reducing Traffic Congestion in Urban Areas via Real-Time Re-Routing: A Simulation Study

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UN SDGs

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