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TrafficGPT: Viewing, Processing and Interacting with Traffic Foundation Models

Siyao Zhang, Daocheng Fu, Zhao Zhang, Bin Yu, Pinlong Cai

arXiv:2309.06719v1cs.AIcs.HC

TL;DR

LLMs have difficulty with numerical traffic data and simulation interaction, while specialized traffic models are often limited to specific tasks and single-round exchanges. TrafficGPT combines ChatGPT with traffic foundation models, using prompts to deconstruct tasks and iteratively invoke tools. Case studies describe traffic-data processing, task orchestration, and natural-language decision support.

  • Problem

    LLMs struggle with numerical traffic data and simulation interaction, while specialized traffic foundation models are typically task-specific and offer limited input-output interaction.

    Source: §I · p. 1, A. Applications of LLMs in… · p. 2

  • Method

    TrafficGPT combines ChatGPT with traffic foundation models and uses prompts to plan tasks and iteratively select and invoke tools.

    Source: §I · p. 1, §III · p. 3

  • Results

    Case studies describe TrafficGPT processing traffic data and providing decision support while deconstructing complex tasks and sequentially using traffic foundation models.

    Source: A. Basic Capacity · p. 6, CONCLUSION · p. 8

  • Takeaways & Limitations

    In a traffic-control dialogue, TrafficGPT revised its recommendations based on simulation results and presented an implementable optimization plan.

    Source: C. Insightful Decision-making Assistance · p. 7

Abstract

from arXiv · show

With the promotion of chatgpt to the public, Large language models indeed showcase remarkable common sense, reasoning, and planning skills, frequently providing insightful guidance. These capabilities hold significant promise for their application in urban traffic management and control. However, LLMs struggle with addressing traffic issues, especially processing numerical data and interacting with simulations, limiting their potential in solving traffic-related challenges. In parallel, specialized traffic foundation models exist but are typically designed for specific tasks with limited input-output interactions. Combining these models with LLMs presents an opportunity to enhance their capacity for tackling complex traffic-related problems and providing insightful suggestions. To bridge this gap, we present TrafficGPT, a fusion of ChatGPT and traffic foundation models. This integration yields the following key enhancements: 1) empowering ChatGPT with the capacity to view, analyze, process traffic data, and provide insightful decision support for urban transportation system management; 2) facilitating the intelligent deconstruction of broad and complex tasks and sequential utilization of traffic foundation models for their gradual completion; 3) aiding human decision-making in traffic control through natural language dialogues; and 4) enabling interactive feedback and solicitation of revised outcomes. By seamlessly intertwining large language model and traffic expertise, TrafficGPT not only advances traffic management but also offers a novel approach to leveraging AI capabilities in this domain. The TrafficGPT demo can be found in https://github.com/lijlansg/TrafficGPT.git.

I. INTRODUCTION

LLMs offer reasoning and planning abilities relevant to traffic management, but struggle with numerical traffic data and simulation interaction, while specialized TFMs are difficult to chain for complex tasks. TrafficGPT combines ChatGPT with traffic foundation models and multimodal data to support complex operations and decision suggestions.

  • ChatGPT's common-sense, reasoning, and planning abilities suggest potential applications in urban traffic management and control.Source: §I · p. 1
  • LLMs struggle to process numerical traffic data and interact with simulations, while specialized TFMs typically handle single-round inputs and outputs.Their number and specialization also make it difficult for human operators to identify and chain them for complex tasks.Source: §I · p. 1
  • TrafficGPT combines ChatGPT with traffic foundation models and multimodal data to support complex operations and provide suggestions and decisions to human users.The proposed integration is intended to help the LLM understand the traffic task it is handling.Source: §I · p. 1

II. RELATED WORKS

Prior work applies LLMs to traffic control, signal control, and transportation information processing. The authors identify limited exploration of LLMs for complex traffic tasks involving expansive datasets and supported decision-making.

A. Applications of LLMs in Transportation Field

B. Models for Traffic-related Tasks

The paper surveys traffic foundation models and extends LLM reasoning and action planning to complex tasks over expansive numerical traffic datasets. TrafficGPT then uses prompts and iterative tool calls to deconstruct and execute tasks, retain dialogue context, and deliver a final response.

B. Models for Traffic-related Tasks

  • Traffic foundation models address network topology, volume, and performance analysis, visualization, and traffic management tasks, but are often limited to specific tasks and single-round inputs and outputs.Combining multiple such models is presented as a basis for using LLMs on complex traffic issues and decision support.Source: B. Models for Traffic-related Tasks · p. 2

C. Empowering LLMs for Complex Traffic-related Tasks

  • TrafficGPT extends Chain-of-Thought and ReAct-style reasoning and action planning from text-focused decision tasks to expansive numerical traffic datasets, including processing, visualization, management, and control.Source: C. Empowering LLMs for Complex… · pp. 2–3

III. TRAFFICGPT

  • Natural-language task input and prompt management supply the LLM with the request, available tools, reasoning history, and dialogue history for planning.The agent uses its thoughts to select TFMs and formulate parameters according to each tool's requirements.Source: §III · p. 3
  • The agent turns tool outputs into intermediate answers, iterates through prompt and tool steps if a task remains unresolved, and generates a final user response when complete.Stored user inputs and LLM outputs provide dialogue context in subsequent interactions.Source: §III · p. 3

A. System Prefix

TrafficGPT's system principles define its role, available tools, and task-planning behavior. The LLM selects TFMs and can deconstruct a request into subtasks requiring multiple tools.

A. System Prefix

  • TrafficGPT is framed as an assistant for traffic-data analysis and display, supported management decisions, and traffic simulation modeling and control.Source: A. System Prefix · p. 3
  • The LLM autonomously selects among available TFMs, with the toolset designed to allow new models and traffic tasks to be incorporated.Source: A. System Prefix · p. 3
  • A request to show a current network heatmap requires both the Get Current Time and Plot Heatmap TFMs, illustrating task decomposition across tools.The Thought-Action-Observation mechanism helps determine, use, and distribute multiple TFMs across subtasks.Source: A. System Prefix · pp. 3–4

B. Traffic Foundation Models

TrafficGPT can incorporate traffic foundation models (TFMs) spanning data access, analysis, visualization, and signal optimization. Prompts describe each tool’s function and requirements, with optional priorities to distinguish similar tools.

B. Traffic Foundation Models

  • TFMs cover functions including database access, traffic-flow counting, trajectory extraction, performance evaluation, resilience quantification, visualization, and signal optimization.Source: B. Traffic Foundation Models · p. 4
  • Because TFMs can share functions while differing in scenarios and input requirements, the LLM must distinguish tools and apply them appropriately.Source: B. Traffic Foundation Models · p. 4
  • Tool prompts specify names, usage, inputs, and outputs; invocation requires an identical tool name, while input descriptions and examples clarify required formats.Source: B. Traffic Foundation Models · p. 4
  • Optional priorities can steer selection among similar TFMs; when no algorithm is specified for signal optimization, TrafficGPT prefers the Webster-based TFM.Source: B. Traffic Foundation Models · pp. 4–5

C. Ensuring Reliability

TrafficGPT uses prompt principles to support dependable handling of complex traffic tasks. These address fabricated tool outputs, redundant calls, human clarification, task focus, and incomplete inputs.

C. Ensuring Reliability

  • Prompts instruct TrafficGPT not to fabricate TFM names, inputs, or output data, supporting data authenticity.Source: C. Ensuring Reliability · p. 5
  • Prompts discourage repetitive tool use to support coherent problem-solving and mitigate error risk.Source: C. Ensuring Reliability · p. 5
  • When available information and TFMs cannot complete a task, the Human Intervention Protocol directs TrafficGPT to stop and request additional information.Source: C. Ensuring Reliability · p. 5
  • Prompts keep the primary task in focus to avoid extraneous requirements and support precision and timely responses.Source: C. Ensuring Reliability · p. 5
  • For insufficient user inputs, prompts guide TrafficGPT to use default parameters or ask the user for more information, as appropriate.Source: C. Ensuring Reliability · p. 5

CASE STUDY

Two case studies illustrate TrafficGPT’s handling of traffic data and simulation control. They use a city-scale trip dataset and a SUMO-based simulation rather than a real Xuancheng road network.

CASE STUDY

  • The case studies examine extensive traffic-data processing and traffic simulation and control, using ChatGPT (gpt-35-turbo) guided by LangChain.Source: CASE STUDY · p. 5
  • The data-processing bot retrieves results through tailored TFMs rather than directly accessing the PostgreSQL database, as a data-security and integrity measure.Source: CASE STUDY · p. 5
  • The Xuancheng dataset contains 1,829,218 individual vehicle trips covering one week, with minute-level departure times, traffic-zone origins and destinations, and road paths.Source: CASE STUDY · p. 6
  • The simulation-control case uses SUMO instead of Xuancheng’s real road network and includes simulation control, performance assessment, visualization, and Webster signal-optimization models.Source: CASE STUDY · p. 6
  • The dialogue cases progress through basic capability, fuzzy instructions with human intervention, and decision-making assistance; Fig. 4 illustrates ambiguity handling and requests for user input.Source: CASE STUDY · p. 6, Figure 4 · p. 6

A. Basic Capacity

TrafficGPT executes natural-language traffic tasks by combining specialized tools with multi-round dialogue. The examples cover numerical traffic analysis, simulation interaction, and context-based inference.

A. Basic Capacity

  • Its fundamental capability is multi-round conversation and performing basic tasks from users’ instructions.Source: A. Basic Capacity · p. 6
  • TrafficGPT uses TFMs to analyze and visualize city-wide OD flows without sending large numerical datasets directly to the LLM, and can retrieve simulation status or write a signal scheme.Source: A. Basic Capacity · p. 6
  • Across dialogue turns, TrafficGPT uses the user’s initial request to infer the time period meant by “the same time period.”Source: A. Basic Capacity · p. 6

B. Fuzzy Instruction and Human Intervention

TrafficGPT handles fuzzy requests by either using defaults and revising results after feedback or asking users for missing information. A six-round example presents the dialogue alongside the bot’s first-round reasoning.

B. Fuzzy Instruction and Human Intervention

  • Users may make fuzzy requests because they lack detailed knowledge of available tools or their required inputs.The section introduces two dialogue cases to demonstrate these situations.Source: B. Fuzzy Instruction and Human… · p. 6
  • Fig. 5 presents six rounds of user questions and TrafficGPT answers, with the bot’s first-round reasoning shown separately.The reasoning appears in the left column; questions and answers appear in the middle and right columns.Source: Figure 5 · p. 7
  • For an underspecified network heatmap request missing a specific time point, TrafficGPT uses default parameters and can correct its initial response in a user-directed follow-up; for a missing target intersection, it requests specifics.Source: B. Fuzzy Instruction and Human… · pp. 6–7

C. Insightful Decision-making Assistance

For a broad, open-ended problem about a simulated road network, TrafficGPT plans and carries out analysis, then revises its recommendations in light of simulation results and user discussion.

C. Insightful Decision-making Assistance

  • The example starts from an open-ended network problem that requires TrafficGPT to deconstruct the task and plan a sequence of actions.The bot initially lacks specific information about network performance.Source: C. Insightful Decision-making Assistance · p. 7
  • TrafficGPT first assesses traffic conditions, identifies three poorly performing intersections, marks them on a map, and recommends signal optimizations.Its plan draws on performance data and general traffic-management knowledge.Source: C. Insightful Decision-making Assistance · p. 7
  • In later rounds, it finds that only two of the three recommended intersections were optimized, explains its reasoning, and revises the plan into an implementable recommendation.The revision is based on simulation results and follows ambiguity in the assessments of optimization outcomes.Source: C. Insightful Decision-making Assistance · p. 7

CONCLUSION

The conclusion presents TrafficGPT as a prompt-based integration of LLMs and traffic foundation models for traffic data and control tasks. Its case studies illustrate decision support, task decomposition, dialogue, and interactive revision.

CONCLUSION

  • TrafficGPT uses prompts to connect LLMs with TFMs, supporting traffic-data analysis, decision support, task decomposition, natural-language control dialogue, and revised outcomes.The conclusion attributes these capabilities to its case studies and says the prompts help inject interaction with traffic data and systems into ChatGPT.Source: CONCLUSION · pp. 7–8
  • The system can incorporate TFMs for specific business needs, with the LLM selecting and executing models in response to task requirements.The conclusion presents this adaptability as relevant to complex transportation and urban-planning challenges.Source: CONCLUSION · p. 8
  • The authors state that the approach enhances AI task-execution effectiveness and reliability and adds insight and creativity for decision support.This is presented as a conclusion about the approach, not a quantified evaluation result.Source: CONCLUSION · p. 8
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