TranspoBot: A Natural Language Dialogue Assistant for Urban Traffic Management

TranspoBot is an LLM-based dialogue system for urban traffic data management, designed to address the barrier faced by non-technical staff in urban public transport operations who need to master SQL to query data. It connects structured traffic data through natural language understanding capabilities, allowing operators to directly query and analyze data via dialogue without writing SQL, thereby improving decision-making efficiency.

Urban public transport operations involve a large amount of complex data such as vehicle scheduling, passenger flow, line efficiency, and equipment maintenance, which are stored in relational databases. Traditional queries require professional SQL skills, which impose high learning costs and are prone to errors for frontline operation managers without technical backgrounds, leading to a high threshold for data access.

TranspoBot adopts an LLM-enhanced architecture, with the bottom layer connected to the traffic operation database and the upper layer interacting via a dialogue interface. The processing flow includes: 1. Parsing the user's natural language question to identify key entities and query intent; 2. Generating precise SQL by combining the database schema; 3. Executing the query and presenting the results in a readable way (including visual charts).

TranspoBot's application scenarios include scheduling management (e.g., vehicle demand during morning peak hours, line punctuality rate query), passenger flow analysis (e.g., passenger flow growth at stations on weekends), and equipment maintenance (e.g., vehicle failure rate, maintenance needs). Traditional queries require technical personnel intervention and take a long time, while this system can provide results in a few seconds, greatly improving efficiency.

In practical applications, three major challenges are faced: 1. Accurate identification of professional terms and abbreviations in the traffic field, which requires special training for the model; 2. Data security and privacy protection, which requires strict permission control and auditing; 3. Complex queries (multi-table association, aggregate calculation, etc.) impose requirements on the model's reasoning ability, requiring a balance between flexibility and accuracy.

TranspoBot represents an important direction for AI applications in vertical industries, proving that the combination of LLM and specific fields can create productivity value. Its popularization will allow operators to easily access data, promote the transformation of the traffic industry towards data-driven decision-making, and is expected to become a standard configuration of intelligent traffic systems in the future, helping to provide efficient and humanized public transport services.