Chat2SPaT: A Large Language Model Based Tool for Automating Traffic Signal Control Plan Management

2025-07-04

AI Summary

Traffic signal timing plan development relies heavily on manual configuration, leading to error-prone and inefficient processes—especially in multi-period/multi-day scheduling scenarios. To address this, we propose the first end-to-end natural language (NL)–to–SPaT (Signal Phase and Timing) generation framework for traffic signal control, leveraging large language models (LLMs) with structured prompt engineering to accurately parse semi-structured, ambiguous user descriptions into standardized phase sequences and timing parameters (output as JSON). The framework supports both ring-and-barrier and phase-based plan generation, iterative conversational editing, and precise intra-cycle phase positioning. We further introduce the first NL understanding benchmark for traffic signal control, releasing open-source code, prompt templates, and a bilingual test set of 300+ instances. Experiments demonstrate >94% plan generation accuracy, establishing a reusable, LLM-driven automation paradigm for intelligent transportation systems.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Pre-timed traffic signal control, commonly used for operating signalized intersections and coordinated arterials, requires tedious manual work for signaling plan creating and updating. When the time-of-day or day-of-week plans are utilized, one intersection is often associated with multiple plans, leading to further repetitive manual plan parameter inputting. To enable a user-friendly traffic signal control plan management process, this study proposes Chat2SPaT, a method to convert users' semi-structured and ambiguous descriptions on the signal control plan to exact signal phase and timing (SPaT) results, which could further be transformed into structured stage-based or ring-based plans to interact with intelligent transportation system (ITS) software and traffic signal controllers. With curated prompts, Chat2SPaT first leverages large language models' (LLMs) capability of understanding users' plan descriptions and reformulate the plan as a combination of phase sequence and phase attribute results in the json format. Based on LLM outputs, python scripts are designed to locate phases in a cycle, address nuances of traffic signal control, and finally assemble the complete traffic signal control plan. Within a chat, the pipeline can be utilized iteratively to conduct further plan editing. Experiments show that Chat2SPaT can generate plans with an accuracy of over 94% for both English and Chinese cases, using a test dataset with over 300 plan descriptions. As the first benchmark for evaluating LLMs' capability of understanding traffic signal control plan descriptions, Chat2SPaT provides an easy-to-use plan management pipeline for traffic practitioners and researchers, serving as a potential new building block for a more accurate and versatile application of LLMs in the field of ITS. The source codes, prompts and test dataset are openly accessible at https://github.com/yuewangits/Chat2SPaT.

Problem

• Automates tedious manual traffic signal plan creation and updates
• Converts ambiguous user descriptions to exact signal phase and timing
• Provides accurate and versatile LLM application in traffic management

Innovation

• LLM converts user descriptions to SPaT
• Python scripts assemble signal control plans
• Chat2SPaT achieves 94% plan accuracy