Self-Management in Urban Traffic Control: An Automated Planning Perspective

Falilat Jimoh, Thomas McCluskey

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Advanced urban traffic control (UTC) systems are often based on feedback 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 coordination 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 that can sense, interpret and deliberate with their actions and goals to be achieved are needed, 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 autonomic UTC, 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
Number of pages18
ISBN (Electronic)9783319258089
ISBN (Print)9783319258065
Publication statusPublished - 3 May 2016

Publication series

NameAutonomic Systems
ISSN (Print)2504-3862
ISSN (Electronic)2504-3870


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