A Domain-specific Heuristic for PDDL+-based Traffic Signal Optimisation

Optimising traffic signals is crucial for mitigating urban congestion, and automated planning, particularly with PDDL+, has shown promise for real-world deployment due to its flexibility and centralised perspective. While existing PDDL+ models guarantee deployability on current infrastructure, they face significant limitations: reliance on domain-independent heuristics restricts their applicability and their scalability, and leads to slow solution generation and unclear quality of plans. To overcome these challenges and unlock the widespread adoption of planning-based traffic control, we introduce $h^{\text{CAFE}}$, a domain-specific heuristic for PDDL+-based traffic signal optimisation. Unlike prior approaches, $h^{\text{CAFE}}$ is designed to work effectively across multiple problem encodings, addressing a key limitation of traditional domain-specific heuristics. We demonstrate its capabilities on real-world data from a region of the UK, showing significant improvements in solution generation time and search space exploration. Our evaluation also compares the strategies generated by $h^{\text{CAFE}}$ against historical data from existing traffic control systems and a non-deployable benchmark, confirming the high quality of the resulting plans.