Exploring the Trade-off Between Flexible and Deployable Models for PDDL+ Urban Traffic Control (Extended Abstract)

• Published in: Proceedings of the International Symposium on Combinatorial Search Vol. 18; pp. 253 - 254
• Main Authors: Castellanos-Paez, Sandra, Percassi, Francesco, Vallati, Mauro
• Format: Journal Article
• Language: English
• Published: 20.07.2025
• ISSN: 2832-9171; 2832-9163; 2832-9163
• DOI: 10.1609/socs.v18i1.36005

The problem of traffic signal optimisation has been successfully tackled using the PDDL+ planning formalism, which also provides an ideal ground for simulating traffic behaviour and performing what-if analysis to assess and compare alternative scenarios. This line of research leads to approaches that can efficiently generate high-quality signal plans with significant benefits in terms of congestion and emissions reduction, as demonstrated both in simulations and real-world deployments. Existing models for automated planning-based traffic signal control can be roughly divided into two classes. (i) Models maximising the flexibility of the traffic controller, where the planning system can dynamically adjust the duration of traffic stages without constraints on the overall cycles and on the differences between subsequent cycles. (ii) Models that guarantee the deployability of traffic signal control techniques also on legacy infrastructure, by forcing the AI approach to select the cycle configurations of traffic signals for the controlled junctions from a given pre-defined set. Of course, both classes offer valuable properties and benefits: the extreme flexibility helps shed light on the potential gains achievable through investment in brand-new infrastructure, whereas the deployable approaches ensure the immediate usability of tools to maximise short-term impact. To bridge the gap between different model classes and explore the trade-off between flexibility and deployability, we present the Trade model. It enables the enforcement of key constraints required for deployability while preserving a level of flexibility that surpasses the capabilities of traditional traffic control infrastructure.