Percassi, Francesco (University of Huddersfield), Vallati, Mauro (University of Huddersfield)

Leveraging AI Planning in a What-If Analysis Framework for Assessing Traffic Signal Strategies

Scheduled for presentation during the Poster Session "Road Traffic Control I" (WeBT15), Wednesday, September 25, 2024, 14:30−16:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 14, 2024

Abstract

The widespread availability of data is allowing traffic authorities to increasingly exploit Artificial Intelligence (AI) techniques for urban traffic management and control. This requires the ability to understand the strategies generated by automated approaches, and to assess their suitability to changing or unexpected circumstances. What-if analysis is a well-established method for evaluating the adaptability of strategies to changing conditions and exploring hypothetical scenarios.

This paper introduces a framework for conducting what-if analysis using AI Planning, aimed at supporting traffic operators and authorities. AI Planning is well-positioned for supporting what-if analysis, thanks to its capacity for concise knowledge representation, its support for validation and verification of encoded knowledge, and its efficiency in simulating described conditions. We characterise the classes of what-if scenarios addressable by the proposed framework, and we demonstrate its ability to handle a range of cases using real-world data.