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

Francesco Doria, Francesco Percassi, Marco Maratea, Mauro Vallati

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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 scalability, leading to slow solution generation and unclear plan quality. To overcome these challenges and unlock the widespread adoption of planning-based traffic control, we introduce hCAFE, a domain-specific heuristic for PDDL+-based traffic signal optimisation. Unlike prior approaches, hCAFE 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 hCAFE against historical data from existing traffic control systems and a non-deployable benchmark, confirming the high quality of the resulting plans.
Original languageEnglish
Title of host publicationThe Fortieth AAAI Conference on Artificial Intelligence
Subtitle of host publicationThirty-Eighth Conference on Innovative Applications of Artificial Intelligence, Sixteenth Symposium on Educational Advances in Artificial Intelligence
EditorsSven Koenig, Chad Jenkins, Matthew E. Taylor
PublisherAAAI press
Pages36207-36216
Number of pages10
Volume40
ISBN (Print)1577359062, 9781577359067
DOIs
Publication statusPublished - 14 Mar 2026
EventThe 40th Annual AAAI Conference on Artificial Intelligence - Singapore EXPO, Singapore
Duration: 20 Jan 202627 Jan 2026
https://aaai.org/conference/aaai/aaai-26/

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
PublisherAAAI
Number43
Volume40
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

ConferenceThe 40th Annual AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI-26
Country/TerritorySingapore
Period20/01/2627/01/26
Internet address

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

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