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

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

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 realworld data.
Original languageEnglish
Title of host publication27th IEEE International Conference on Intelligent Transportation Systems
PublisherIEEE
Publication statusAccepted/In press - 9 Jul 2024
Event27th IEEE International Conference on Intelligent Transportation Systems - Edmonton, Canada
Duration: 24 Sep 202427 Sep 2024
Conference number: 27

Conference

Conference27th IEEE International Conference on Intelligent Transportation Systems
Abbreviated titleITSC 2024
Country/TerritoryCanada
CityEdmonton
Period24/09/2427/09/24

Cite this