Chat2SPaT converts natural-language plan descriptions into exact signal phase and timing plans, reporting 86 to 94 percent accuracy across four LLMs on a 306-case bilingual test set.
LLMs are used to convert user descriptions to keywords, with which scripts are designed to generate simulation networks
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.AI 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Chat2SPaT: A Large Language Model Based Tool for Automating Traffic Signal Control Plan Management
Chat2SPaT converts natural-language plan descriptions into exact signal phase and timing plans, reporting 86 to 94 percent accuracy across four LLMs on a 306-case bilingual test set.