In Defence of Good Old-Fashioned Artificial Intelligence Approaches in Intelligent Transportation Systems

In recent years, Artificial Intelligence (AI) has been increasingly used in traffic management and control, particularly in the smart city context. However, the vast majority of recent AI-based approaches rely on data-driven black-box models that hinder the ability to understand the behaviour and dynamics that lead to a given output. On the contrary, Good Old-Fashioned Artificial Intelligence approaches that are based on symbolic models, such as automated planning, can provide the transparency and explainability needed in real-world applications. This paper focuses on the benefits of using automated planning techniques in Intelligent Transportation Systems (ITS), with a focus on explainability. A case study is presented to demonstrate how the components of an automated planning system can support explainability, the types of explanations that can be obtained, and the way in which such explanations can be generated.