Effective Real-Time Urban Traffic Routing: An Automated Planning Approach

Mauro Vallati; Lukáš Chrpa

doi:10.1109/MT-ITS49943.2021.9529329

Effective Real-Time Urban Traffic Routing: An Automated Planning Approach

Mauro Vallati, Lukáš Chrpa

Czech Technical University in Prague

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

7 Citations (Scopus)

Abstract

One of the pivotal challenges presented to urban road traffic controllers is the effective utilisation of transport infrastructure, as a result of growing urbanisation, the finite network capacity, and of the increasing number of road vehicles. In this context, the arrival of connected vehicles present a unique opportunity for a fundamental change in urban traffic control. Urban traffic control approaches should then take an active role in integrating connected vehicles into the mobility ecosystem in order to maximise benefits. To support such integration, in this work we propose to leverage automated planning, a well-studied branch of artificial intelligence, to perform real-time traffic routing in urban areas. We describe the proposed approach, and we demonstrate its effectiveness using real-world historical data of a UK town.

Original language	English
Title of host publication	Proceedings of the 7th International IEEE Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS 2021)
Publisher	IEEE Computer Society
Number of pages	6
ISBN (Electronic)	9781728189956
ISBN (Print)	9781728189963
DOIs	https://doi.org/10.1109/MT-ITS49943.2021.9529329
Publication status	Published - 7 Sept 2021
Event	7th International IEEE Conference on Models and Technologies for Intelligent Transportation Systems - Aquila Atlantis Hotel, Heraklion, Crete, Greece Duration: 16 Jun 2021 → 17 Jun 2021 Conference number: 7 https://www.mt-its2021.tse.bgu.tum.de/

Conference

Conference	7th International IEEE Conference on Models and Technologies for Intelligent Transportation Systems
Abbreviated title	MT-ITS 2021
Country/Territory	Greece
City	Heraklion, Crete
Period	16/06/21 → 17/06/21
Internet address	https://www.mt-its2021.tse.bgu.tum.de/

UN SDGs

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

SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities

Access to Document

10.1109/MT-ITS49943.2021.9529329Licence: Unspecified

Planning_MT_ITS(1)Accepted author manuscript, 245 KBLicence: Unspecified

Cite this

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title = "Effective Real-Time Urban Traffic Routing: An Automated Planning Approach",

abstract = "One of the pivotal challenges presented to urban road traffic controllers is the effective utilisation of transport infrastructure, as a result of growing urbanisation, the finite network capacity, and of the increasing number of road vehicles. In this context, the arrival of connected vehicles present a unique opportunity for a fundamental change in urban traffic control. Urban traffic control approaches should then take an active role in integrating connected vehicles into the mobility ecosystem in order to maximise benefits. To support such integration, in this work we propose to leverage automated planning, a well-studied branch of artificial intelligence, to perform real-time traffic routing in urban areas. We describe the proposed approach, and we demonstrate its effectiveness using real-world historical data of a UK town. ",

keywords = "Traffic Routing, Artificial Intelligence, Automated Planning, Urban Traffic Control",

author = "Mauro Vallati and Luk{\'a}{\v s} Chrpa",

note = "Funding Information: Mauro Vallati was partially funded by the EPSRC grant EP/R51343X/1 (AI4ME). Luk{\'a}sˇ Chrpa was partially funded by the Czech Science Foundation (project no. 18-07252S). 1https://inrix.com/press-releases/scorecard-2018-uk/ Publisher Copyright: {\textcopyright} 2021 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 7th International IEEE Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2021 ; Conference date: 16-06-2021 Through 17-06-2021",

year = "2021",

month = sep,

day = "7",

doi = "10.1109/MT-ITS49943.2021.9529329",

language = "English",

isbn = "9781728189963",

booktitle = "Proceedings of the 7th International IEEE Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS 2021)",

publisher = "IEEE Computer Society",

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url = "https://www.mt-its2021.tse.bgu.tum.de/",

}

Vallati, M & Chrpa, L 2021, Effective Real-Time Urban Traffic Routing: An Automated Planning Approach. in Proceedings of the 7th International IEEE Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS 2021)., 9529329, IEEE Computer Society, 7th International IEEE Conference on Models and Technologies for Intelligent Transportation Systems, Heraklion, Crete, Greece, 16/06/21. https://doi.org/10.1109/MT-ITS49943.2021.9529329

Effective Real-Time Urban Traffic Routing: An Automated Planning Approach. / Vallati, Mauro; Chrpa, Lukáš.
Proceedings of the 7th International IEEE Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS 2021). IEEE Computer Society, 2021. 9529329.

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

TY - GEN

T1 - Effective Real-Time Urban Traffic Routing

T2 - 7th International IEEE Conference on Models and Technologies for Intelligent Transportation Systems

AU - Vallati, Mauro

AU - Chrpa, Lukáš

N1 - Conference code: 7

PY - 2021/9/7

Y1 - 2021/9/7

N2 - One of the pivotal challenges presented to urban road traffic controllers is the effective utilisation of transport infrastructure, as a result of growing urbanisation, the finite network capacity, and of the increasing number of road vehicles. In this context, the arrival of connected vehicles present a unique opportunity for a fundamental change in urban traffic control. Urban traffic control approaches should then take an active role in integrating connected vehicles into the mobility ecosystem in order to maximise benefits. To support such integration, in this work we propose to leverage automated planning, a well-studied branch of artificial intelligence, to perform real-time traffic routing in urban areas. We describe the proposed approach, and we demonstrate its effectiveness using real-world historical data of a UK town.

AB - One of the pivotal challenges presented to urban road traffic controllers is the effective utilisation of transport infrastructure, as a result of growing urbanisation, the finite network capacity, and of the increasing number of road vehicles. In this context, the arrival of connected vehicles present a unique opportunity for a fundamental change in urban traffic control. Urban traffic control approaches should then take an active role in integrating connected vehicles into the mobility ecosystem in order to maximise benefits. To support such integration, in this work we propose to leverage automated planning, a well-studied branch of artificial intelligence, to perform real-time traffic routing in urban areas. We describe the proposed approach, and we demonstrate its effectiveness using real-world historical data of a UK town.

KW - Traffic Routing

KW - Artificial Intelligence

KW - Automated Planning

KW - Urban Traffic Control

UR - https://www.scopus.com/pages/publications/85115850563

U2 - 10.1109/MT-ITS49943.2021.9529329

DO - 10.1109/MT-ITS49943.2021.9529329

M3 - Conference contribution

SN - 9781728189963

BT - Proceedings of the 7th International IEEE Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS 2021)

PB - IEEE Computer Society

Y2 - 16 June 2021 through 17 June 2021

ER -

Effective Real-Time Urban Traffic Routing: An Automated Planning Approach

Abstract

Conference

UN SDGs

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Fingerprint

AI traffic management system expands into China with granting of new patent

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Effective Real-Time Urban Traffic Routing: An Automated Planning Approach

Abstract

Conference

UN SDGs

Access to Document

Other files and links

Fingerprint

Press/Media

AI traffic management system expands into China with granting of new patent

Cite this