TrafficGPT: Viewing, Processing and Interacting with Traffic Foundation Models

• URL: http://arxiv.org/abs/2309.06719v1
• Date: Wed, 13 Sep 2023 04:47:43 GMT
• Title: TrafficGPT: Viewing, Processing and Interacting with Traffic Foundation Models
• Authors: Siyao Zhang, Daocheng Fu, Zhao Zhang, Bin Yu and Pinlong Cai
• Abstract summary: TrafficGPT is a fusion of ChatGPT and traffic foundation models. By seamlessly intertwining large language model and traffic expertise, TrafficGPT offers a novel approach to leveraging AI capabilities in this domain.
• Score: 10.904594811905778
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• TrafficGPT: Towards Multi-Scale Traffic Analysis and Generation with Spatial-Temporal Agent Framework [3.947797359736224]
  We have designed a multi-scale traffic generation system, TrafficGPT, using three AI agents to process multi-scale traffic data. TrafficGPT consists of three essential AI agents: 1) a text-to-demand agent to interact with users and extract prediction tasks through texts; 2) a traffic prediction agent that leverages multi-scale traffic data to generate temporal features and similarity; and 3) a suggestion and visualization agent that uses the prediction results to generate suggestions and visualizations.
  arXiv  Detail & Related papers  (2024-05-08T07:48:40Z)
• Towards Explainable Traffic Flow Prediction with Large Language Models [36.86937188565623]
  We propose a Traffic flow Prediction model based on Large Language Models (LLMs) to generate explainable traffic predictions. By transferring multi-modal traffic data into natural language descriptions, xTP-LLM captures complex time-series patterns and external factors from comprehensive traffic data. Empirically, xTP-LLM shows competitive accuracy compared with deep learning baselines, while providing an intuitive and reliable explanation for predictions.
  arXiv  Detail & Related papers  (2024-04-03T07:14:15Z)
• BjTT: A Large-scale Multimodal Dataset for Traffic Prediction [49.93028461584377]
  Traditional traffic prediction methods rely on historical traffic data to predict traffic trends. In this work, we explore how generative models combined with text describing the traffic system can be applied for traffic generation. We propose ChatTraffic, the first diffusion model for text-to-traffic generation.
  arXiv  Detail & Related papers  (2024-03-08T04:19:56Z)
• Multi-scale Traffic Pattern Bank for Cross-city Few-shot Traffic Forecasting [15.123457772023238]
  We propose a solution for the cross-city few-shot traffic forecasting problem called Multi-scale Traffic Pattern Bank. The framework employs advanced clustering techniques to systematically generate a multi-scale traffic pattern bank. Empirical assessments conducted on real-world traffic datasets affirm the superior performance of MTPB.
  arXiv  Detail & Related papers  (2024-02-01T07:33:31Z)
• Open-TI: Open Traffic Intelligence with Augmented Language Model [23.22301632003752]
  Open-TI is an innovative model targeting the goal of Turing Indistinguishable Traffic Intelligence. It is the first method capable of conducting exhaustive traffic analysis from scratch. Open-TI is able to conduct task-specific embodiment like training and adapting the traffic signal control policies.
  arXiv  Detail & Related papers  (2023-12-30T11:50:11Z)
• Traffic-Domain Video Question Answering with Automatic Captioning [69.98381847388553]
  Video Question Answering (VidQA) exhibits remarkable potential in facilitating advanced machine reasoning capabilities. We present a novel approach termed Traffic-domain Video Question Answering with Automatic Captioning (TRIVIA), which serves as a weak-supervision technique for infusing traffic-domain knowledge into large video-language models.
  arXiv  Detail & Related papers  (2023-07-18T20:56:41Z)
• A Study of Situational Reasoning for Traffic Understanding [63.45021731775964]
  We devise three novel text-based tasks for situational reasoning in the traffic domain. We adopt four knowledge-enhanced methods that have shown generalization capability across language reasoning tasks in prior work. We provide in-depth analyses of model performance on data partitions and examine model predictions categorically.
  arXiv  Detail & Related papers  (2023-06-05T01:01:12Z)
• Building Transportation Foundation Model via Generative Graph Transformer [12.660129805049664]
  We propose a novel approach, Transportation Foundation Model (TFM), which integrates the principles of traffic simulation into traffic prediction. TFM uses graph structures and dynamic graph generation algorithms to capture the participatory behavior and interaction of transportation system actors. This data-driven and model-free simulation method addresses the challenges faced by traditional systems in terms of structural complexity and model accuracy.
  arXiv  Detail & Related papers  (2023-05-24T07:34:15Z)
• Intelligent Traffic Monitoring with Hybrid AI [78.65479854534858]
  We introduce HANS, a neuro-symbolic architecture for multi-modal context understanding. We show how HANS addresses the challenges associated with traffic monitoring while being able to integrate with a wide range of reasoning methods.
  arXiv  Detail & Related papers  (2022-08-31T17:47:22Z)
• End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning [63.56464608571663]
  Navigating through intersections is one of the main challenging tasks for an autonomous vehicle. In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided. We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.
  arXiv  Detail & Related papers  (2021-04-28T07:54:40Z)
• Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline [85.9210953301628]
  Control of traffic signals is fundamental and critical to alleviate traffic congestion in urban areas. Because of the high complexity of modelling the problem, experimental settings of current works are often inconsistent. We propose a novel and strong baseline model based on deep reinforcement learning with the encoder-decoder structure.
  arXiv  Detail & Related papers  (2021-01-24T03:55:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.