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Traffic Performance GPT (TP-GPT): Real-Time Data Informed Intelligent ChatBot for Transportation Surveillance and Management

Bingzhang Wang, Zhiyu Cai, Muhammad Monjurul Karim, Chenxi Liu, Yinhai Wang

TL;DR

The paper tackles the challenge of exploiting vast real-time traffic data stored in large multi-table warehouses while preserving privacy and interpretability. It introduces Traffic Performance GPT (TP-GPT), an LLM-powered online chatbot that uses Chain-of-Thought prompting, few-shot learning, a multi-agent collaboration framework, and chat memory to translate natural language queries into executable SQL and generate transportation-focused narratives. On the TransQuery benchmark of 50 complex tasks, TP-GPT significantly outperforms GPT-4 Turbo, PaLM 2, and SQLCoder, achieving an average score of 0.87 and 80% flawless responses, aided by a specialized domain prompt design and iterative query refinement. The work demonstrates a practical, privacy-preserving, real-time analytics platform with potential extensions to traffic forecasting and map-based visual integration.

Abstract

The digitization of traffic sensing infrastructure has significantly accumulated an extensive traffic data warehouse, which presents unprecedented challenges for transportation analytics. The complexities associated with querying large-scale multi-table databases require specialized programming expertise and labor-intensive development. Additionally, traditional analysis methods have focused mainly on numerical data, often neglecting the semantic aspects that could enhance interpretability and understanding. Furthermore, real-time traffic data access is typically limited due to privacy concerns. To bridge this gap, the integration of Large Language Models (LLMs) into the domain of traffic management presents a transformative approach to addressing the complexities and challenges inherent in modern transportation systems. This paper proposes an intelligent online chatbot, TP-GPT, for efficient customized transportation surveillance and management empowered by a large real-time traffic database. The innovative framework leverages contextual and generative intelligence of language models to generate accurate SQL queries and natural language interpretations by employing transportation-specialized prompts, Chain-of-Thought prompting, few-shot learning, multi-agent collaboration strategy, and chat memory. Experimental study demonstrates that our approach outperforms state-of-the-art baselines such as GPT-4 and PaLM 2 on a challenging traffic-analysis benchmark TransQuery. TP-GPT would aid researchers and practitioners in real-time transportation surveillance and management in a privacy-preserving, equitable, and customizable manner.

Traffic Performance GPT (TP-GPT): Real-Time Data Informed Intelligent ChatBot for Transportation Surveillance and Management

TL;DR

The paper tackles the challenge of exploiting vast real-time traffic data stored in large multi-table warehouses while preserving privacy and interpretability. It introduces Traffic Performance GPT (TP-GPT), an LLM-powered online chatbot that uses Chain-of-Thought prompting, few-shot learning, a multi-agent collaboration framework, and chat memory to translate natural language queries into executable SQL and generate transportation-focused narratives. On the TransQuery benchmark of 50 complex tasks, TP-GPT significantly outperforms GPT-4 Turbo, PaLM 2, and SQLCoder, achieving an average score of 0.87 and 80% flawless responses, aided by a specialized domain prompt design and iterative query refinement. The work demonstrates a practical, privacy-preserving, real-time analytics platform with potential extensions to traffic forecasting and map-based visual integration.

Abstract

The digitization of traffic sensing infrastructure has significantly accumulated an extensive traffic data warehouse, which presents unprecedented challenges for transportation analytics. The complexities associated with querying large-scale multi-table databases require specialized programming expertise and labor-intensive development. Additionally, traditional analysis methods have focused mainly on numerical data, often neglecting the semantic aspects that could enhance interpretability and understanding. Furthermore, real-time traffic data access is typically limited due to privacy concerns. To bridge this gap, the integration of Large Language Models (LLMs) into the domain of traffic management presents a transformative approach to addressing the complexities and challenges inherent in modern transportation systems. This paper proposes an intelligent online chatbot, TP-GPT, for efficient customized transportation surveillance and management empowered by a large real-time traffic database. The innovative framework leverages contextual and generative intelligence of language models to generate accurate SQL queries and natural language interpretations by employing transportation-specialized prompts, Chain-of-Thought prompting, few-shot learning, multi-agent collaboration strategy, and chat memory. Experimental study demonstrates that our approach outperforms state-of-the-art baselines such as GPT-4 and PaLM 2 on a challenging traffic-analysis benchmark TransQuery. TP-GPT would aid researchers and practitioners in real-time transportation surveillance and management in a privacy-preserving, equitable, and customizable manner.
Paper Structure (16 sections, 2 equations, 5 figures, 3 tables)

This paper contains 16 sections, 2 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Traffic Management with LLMs
  4. LLMs in Database SQL Query
  5. Methodology
  6. Problem Statement
  7. System Design
  8. Input Prompt Generation
  9. Multi-Agent Strategy
  10. Few-shot Learning Prompting
  11. Chat Memory
  12. Experiments
  13. Experimental Setup
  14. Performance Comparison
  15. Ablation Study
  16. ...and 1 more sections

Figures (5)

  • Figure 1: Inductive loop Detector locations shown as blue points
  • Figure 2: Overall system architecture design demonstrating traffic analysis pipeline (right), example input prompts (left), and incorporated Chain of Thought iterative prompting (center)
  • Figure 3: TP-GPT multi-agent collaboration framework for traffic data analysis
  • Figure 4: Online web application interface of TP-GPT ChatBot
  • Figure 5: Ablation study of TP-GPT on TransQuery by removing prompts, few-shot learning, or multi-agent strategy