The growing population has accelerated the process of urbanisation, and is putting under stress the urban transport infrastructure. This results in increased traffic congestion, with significant health, economy, and social issues. Artificial Intelligence techniques are increasingly demonstrating their capabilities in predicting and supporting urban traffic control, by extending the abilities of traffic authorities in planning and reacting to different traffic conditions. In this context, our main research topic fits in the autonomic traffic control theme, with the aim of supporting the design of autonomous traffic control systems.

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.

Maritime navigational safety is of utmost importance to prevent vessel collisions in heavily trafficked ports, and avoid environmental costs. In case of a likely near miss among vessels, port traffic controllers provide assistance for safely navigating the waters, often at very short lead times. A better strategy is to avoid such situations from even happening. To achieve this, we a) formalize the decision model for traffic hotspot mitigation including realistic maritime navigational features and constraints through consultations with domain experts; and b) develop a constraint programming based scheduling approach to mitigate hotspots. We model the problem as a variant of the resource constrained project scheduling problem to adjust vessel movement schedules such that the average delay is minimized and navigational safety constraints are also satisfied. We conduct a thorough evaluation on key performance indicators using real world data, and demonstrate the effectiveness of our approach in mitigating high-risk situations.