There is a growing interest in the use of AI techniques for urban traffic control, with a particular focus on traffic signal optimisation. Model-based approaches such as planning demonstrated to be capable of dealing in real-time with unexpected or unusual traffic conditions, as well as with the usual traffic patterns. Further, the knowledge models on which such techniques rely to generate traffic signal strategies are in fact simulation models of traffic, hence can be used by traffic authorities to test and compare different approaches. In this work, we present a framework that relies on automated planning to generate and simulate traffic signal strategies in a urban region. To demonstrate the capabilities of the framework, we consider real-world data collected from sensors deployed in a major corridor of the Kirklees region of the United Kingdom.

Automated planning is a prominent AI challenge, and it is now exploited in a range of real-world applications. There are three crucial aspects of automated planning: the planning engine, the domain model, and the problem instance. While the planning engine and the domain model can be engineered and optimised offline, in many applications there is the need to generate problem instances on the fly. In this paper we focus on the challenges of on-the-fly knowledge acquisition for complex and variegated problem instances. We consider as a case study the application of planning to urban traffic control and we describe the designed and developed knowledge acquisition process. This allows us to discuss a range of lessons learned from the experience, and to point to important lines of research to support the knowledge acquisition process for automated planning applications.