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 proceedingConference contributionpeer-review

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 languageEnglish
Title of host publicationProceedings of the 33rd Australasian Joint Conference on Artificial Intelligence
Subtitle of host publication(AI2020)
EditorsMarcus Gallegher, Nour Moustafa, Erandi Lakshika
Place of PublicationCham
PublisherSpringer Nature Switzerland AG
Pages69-81
Number of pages12
Volume12576 LNCS/LNAI
ISBN (Electronic)9783030649845
ISBN (Print)9783030649838
DOIs
Publication statusPublished - 27 Nov 2020
Event33rd Australasian Joint Conference on Artificial Intelligence - Virtual conference due to COVID-19
Duration: 29 Nov 202030 Nov 2020
Conference number: 33
http://www.ajcai2020.net/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Nature Switzerland AG
Volume12576 LNCS/LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference33rd Australasian Joint Conference on Artificial Intelligence
Abbreviated titleAI2020
Period29/11/2030/11/20
Internet address

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