Self-Management in Urban Traffic Control

An Automated Planning Perspective

Falilat Jimoh, Thomas McCluskey

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Advanced urban traffic control systems are often based on feed-back algorithms. They use road traffic data which has been gathered from a couple of minutes to several years. For instance, current traffic control systems often operate on the basis of adaptive green phases and flexible co-ordination in road (sub) networks based on measured traffic conditions. However, these approaches are still not very efficient during unforeseen situations such as road incidents when changes in traffic are requested in a short time interval. For such anomalies, we argue that systems are needed that can sense, interpret and deliberate with their actions and goals to be achieved, taking into consideration continuous changes in state, required service level and environmental constraints. The requirement of such systems is that they can plan and act effectively after such deliberation, so that behaviourally they appear self-aware. This chapter focuses on the design of a generic architecture for auto- nomic urban traffic control, to enable the network to manage itself both in normal operation and in unexpected scenarios. The reasoning and self- management aspects are implemented using automated planning techniques inspired by both the symbolic artificial intelligence and traditional control engineering.Preliminary test results of the plan generation phase of the architecture are considered and evaluated.
Original languageEnglish
Title of host publicationAutonomic Road Transport Support Systems
EditorsThomas Leo McCluskey, Apostolos Kotsialos, Jorg P. Muller, Franziska Klugl, Omer Rana, Rene Schumann
Place of PublicationSwitzerland
PublisherBirkhauser Verlag Basel
Pages29-46
Number of pages18
ISBN (Electronic)9783319258089
ISBN (Print)9783319258065
Publication statusPublished - 2016

Publication series

NameAutonomic Systems
PublisherBirkhauser

Fingerprint

Control systems
Planning
Electric current control
Feedback

Cite this

Jimoh, F., & McCluskey, T. (2016). Self-Management in Urban Traffic Control: An Automated Planning Perspective. In T. L. McCluskey, A. Kotsialos, J. P. Muller, F. Klugl, O. Rana, & R. Schumann (Eds.), Autonomic Road Transport Support Systems (pp. 29-46). (Autonomic Systems). Switzerland: Birkhauser Verlag Basel.
Jimoh, Falilat ; McCluskey, Thomas. / Self-Management in Urban Traffic Control : An Automated Planning Perspective. Autonomic Road Transport Support Systems. editor / Thomas Leo McCluskey ; Apostolos Kotsialos ; Jorg P. Muller ; Franziska Klugl ; Omer Rana ; Rene Schumann. Switzerland : Birkhauser Verlag Basel, 2016. pp. 29-46 (Autonomic Systems).
@inbook{c1bb193b6c9c4802880c0c67dce606a3,
title = "Self-Management in Urban Traffic Control: An Automated Planning Perspective",
abstract = "Advanced urban traffic control systems are often based on feed-back algorithms. They use road traffic data which has been gathered from a couple of minutes to several years. For instance, current traffic control systems often operate on the basis of adaptive green phases and flexible co-ordination in road (sub) networks based on measured traffic conditions. However, these approaches are still not very efficient during unforeseen situations such as road incidents when changes in traffic are requested in a short time interval. For such anomalies, we argue that systems are needed that can sense, interpret and deliberate with their actions and goals to be achieved, taking into consideration continuous changes in state, required service level and environmental constraints. The requirement of such systems is that they can plan and act effectively after such deliberation, so that behaviourally they appear self-aware. This chapter focuses on the design of a generic architecture for auto- nomic urban traffic control, to enable the network to manage itself both in normal operation and in unexpected scenarios. The reasoning and self- management aspects are implemented using automated planning techniques inspired by both the symbolic artificial intelligence and traditional control engineering.Preliminary test results of the plan generation phase of the architecture are considered and evaluated.",
keywords = "Automated Planning, Autonomic systems, Urban traffic control",
author = "Falilat Jimoh and Thomas McCluskey",
year = "2016",
language = "English",
isbn = "9783319258065",
series = "Autonomic Systems",
publisher = "Birkhauser Verlag Basel",
pages = "29--46",
editor = "McCluskey, {Thomas Leo} and Apostolos Kotsialos and Muller, {Jorg P.} and Franziska Klugl and Omer Rana and Rene Schumann",
booktitle = "Autonomic Road Transport Support Systems",
address = "Switzerland",

}

Jimoh, F & McCluskey, T 2016, Self-Management in Urban Traffic Control: An Automated Planning Perspective. in TL McCluskey, A Kotsialos, JP Muller, F Klugl, O Rana & R Schumann (eds), Autonomic Road Transport Support Systems. Autonomic Systems, Birkhauser Verlag Basel, Switzerland, pp. 29-46.

Self-Management in Urban Traffic Control : An Automated Planning Perspective. / Jimoh, Falilat; McCluskey, Thomas.

Autonomic Road Transport Support Systems. ed. / Thomas Leo McCluskey; Apostolos Kotsialos; Jorg P. Muller; Franziska Klugl; Omer Rana; Rene Schumann. Switzerland : Birkhauser Verlag Basel, 2016. p. 29-46 (Autonomic Systems).

Research output: Chapter in Book/Report/Conference proceedingChapter

TY - CHAP

T1 - Self-Management in Urban Traffic Control

T2 - An Automated Planning Perspective

AU - Jimoh, Falilat

AU - McCluskey, Thomas

PY - 2016

Y1 - 2016

N2 - Advanced urban traffic control systems are often based on feed-back algorithms. They use road traffic data which has been gathered from a couple of minutes to several years. For instance, current traffic control systems often operate on the basis of adaptive green phases and flexible co-ordination in road (sub) networks based on measured traffic conditions. However, these approaches are still not very efficient during unforeseen situations such as road incidents when changes in traffic are requested in a short time interval. For such anomalies, we argue that systems are needed that can sense, interpret and deliberate with their actions and goals to be achieved, taking into consideration continuous changes in state, required service level and environmental constraints. The requirement of such systems is that they can plan and act effectively after such deliberation, so that behaviourally they appear self-aware. This chapter focuses on the design of a generic architecture for auto- nomic urban traffic control, to enable the network to manage itself both in normal operation and in unexpected scenarios. The reasoning and self- management aspects are implemented using automated planning techniques inspired by both the symbolic artificial intelligence and traditional control engineering.Preliminary test results of the plan generation phase of the architecture are considered and evaluated.

AB - Advanced urban traffic control systems are often based on feed-back algorithms. They use road traffic data which has been gathered from a couple of minutes to several years. For instance, current traffic control systems often operate on the basis of adaptive green phases and flexible co-ordination in road (sub) networks based on measured traffic conditions. However, these approaches are still not very efficient during unforeseen situations such as road incidents when changes in traffic are requested in a short time interval. For such anomalies, we argue that systems are needed that can sense, interpret and deliberate with their actions and goals to be achieved, taking into consideration continuous changes in state, required service level and environmental constraints. The requirement of such systems is that they can plan and act effectively after such deliberation, so that behaviourally they appear self-aware. This chapter focuses on the design of a generic architecture for auto- nomic urban traffic control, to enable the network to manage itself both in normal operation and in unexpected scenarios. The reasoning and self- management aspects are implemented using automated planning techniques inspired by both the symbolic artificial intelligence and traditional control engineering.Preliminary test results of the plan generation phase of the architecture are considered and evaluated.

KW - Automated Planning

KW - Autonomic systems

KW - Urban traffic control

UR - http://www.springer.com/gb/book/9783319258065

M3 - Chapter

SN - 9783319258065

T3 - Autonomic Systems

SP - 29

EP - 46

BT - Autonomic Road Transport Support Systems

A2 - McCluskey, Thomas Leo

A2 - Kotsialos, Apostolos

A2 - Muller, Jorg P.

A2 - Klugl, Franziska

A2 - Rana, Omer

A2 - Schumann, Rene

PB - Birkhauser Verlag Basel

CY - Switzerland

ER -

Jimoh F, McCluskey T. Self-Management in Urban Traffic Control: An Automated Planning Perspective. In McCluskey TL, Kotsialos A, Muller JP, Klugl F, Rana O, Schumann R, editors, Autonomic Road Transport Support Systems. Switzerland: Birkhauser Verlag Basel. 2016. p. 29-46. (Autonomic Systems).