Implementing traffic agent based on LangGraph
haitian chen, ye ding · 2025
As urbanization accelerates, the rapid increase in urban populations and vehicle numbers poses unprecedented challenges to city traffic. The demand for intelligent transportation systems, a key component of smart city construction, is growing day by day. This system is a highly integrated interdisciplinary field that combines complex computer algorithms, which to some extent limits its extensive application. The emergence of large language middleware has reduced the deployment barriers for related applications. This article proposes an innovative intelligent transportation system that combines a large language model (LLM) with Amap API through LangGraph, providing strong support for urban traffic. This system not only assists users in making travel decisions through natural language dialogue based on the superior reasoning and planning capabilities of LLM, but also enhances the semantic understanding ability of LLM by constructing a graph structure through LangGraph, it improves the capture capability for urban traffic tasks and implements a self-feedback mechanism for the large language model. This innovative approach offers a more convenient and efficient method for the application of large language models in the field of Intelligent Traffic System.