ChatTraffic: Text-to-Traffic Generation via Diffusion Model
- URL: http://arxiv.org/abs/2311.16203v3
- Date: Mon, 5 Feb 2024 02:46:11 GMT
- Title: ChatTraffic: Text-to-Traffic Generation via Diffusion Model
- Authors: Chengyang Zhang, Yong Zhang, Qitan Shao, Bo Li, Yisheng Lv, Xinglin
Piao, Baocai Yin
- Abstract summary: 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.
- Score: 45.82932883802526
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Traffic prediction is one of the most significant foundations in Intelligent
Transportation Systems (ITS). Traditional traffic prediction methods rely only
on historical traffic data to predict traffic trends and face two main
challenges. 1) insensitivity to unusual events. 2) limited performance in
long-term prediction. In this work, we explore how generative models combined
with text describing the traffic system can be applied for traffic generation,
and name the task Text-to-Traffic Generation (TTG). The key challenge of the
TTG task is how to associate text with the spatial structure of the road
network and traffic data for generating traffic situations. To this end, we
propose ChatTraffic, the first diffusion model for text-to-traffic generation.
To guarantee the consistency between synthetic and real data, we augment a
diffusion model with the Graph Convolutional Network (GCN) to extract spatial
correlations of traffic data. In addition, we construct a large dataset
containing text-traffic pairs for the TTG task. We benchmarked our model
qualitatively and quantitatively on the released dataset. The experimental
results indicate that ChatTraffic can generate realistic traffic situations
from the text. Our code and dataset are available at
https://github.com/ChyaZhang/ChatTraffic.
Related papers
- Cross-domain Few-shot In-context Learning for Enhancing Traffic Sign Recognition [49.20086587208214]
We propose a cross-domain few-shot in-context learning method based on the MLLM for enhancing traffic sign recognition.
By using description texts, our method reduces the cross-domain differences between template and real traffic signs.
Our approach requires only simple and uniform textual indications, without the need for large-scale traffic sign images and labels.
arXiv Detail & Related papers (2024-07-08T10:51:03Z) - 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) - FDTI: Fine-grained Deep Traffic Inference with Roadnet-enriched Graph [10.675666104503119]
We propose Fine-grained Deep Traffic Inference, as termedI.
We construct a fine-grained traffic graph based on traffic signals to model the inter-road relations.
We are the first to conduct the city-level fine-grained traffic prediction.
arXiv Detail & Related papers (2023-06-19T14:03:42Z) - TraffNet: Learning Causality of Traffic Generation for What-if Prediction [4.604622556490027]
Real-time what-if traffic prediction is crucial for decision making in intelligent traffic management and control.
Here, we present a simple deep learning framework called TraffNet that learns the mechanisms of traffic generation for what-if pre-diction.
arXiv Detail & Related papers (2023-03-28T13:12:17Z) - D2-TPred: Discontinuous Dependency for Trajectory Prediction under
Traffic Lights [68.76631399516823]
We present a trajectory prediction approach with respect to traffic lights, D2-TPred, using a spatial dynamic interaction graph (SDG) and a behavior dependency graph (BDG)
Our experimental results show that our model achieves more than 20.45% and 20.78% in terms of ADE and FDE, respectively, on VTP-TL.
arXiv Detail & Related papers (2022-07-21T10:19:07Z) - STCGAT: Spatial-temporal causal networks for complex urban road traffic
flow prediction [12.223433627287605]
Traffic data are highly nonlinear and have complex spatial correlations between road nodes.
Existing approaches usually use fixed traffic road network topology maps and independent time series modules to capture Spatial-temporal correlations.
We propose a new prediction model which captures the spatial dependence of the traffic network through a Graph Attention Network(GAT) and then analyzes the causal relationship of the traffic data.
arXiv Detail & Related papers (2022-03-21T06:38:34Z) - Road Network Guided Fine-Grained Urban Traffic Flow Inference [108.64631590347352]
Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem.
We propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that exploits the prior knowledge of road networks.
Our method can generate high-quality fine-grained traffic flow maps.
arXiv Detail & Related papers (2021-09-29T07:51:49Z) - Deep traffic light detection by overlaying synthetic context on
arbitrary natural images [49.592798832978296]
We propose a method to generate artificial traffic-related training data for deep traffic light detectors.
This data is generated using basic non-realistic computer graphics to blend fake traffic scenes on top of arbitrary image backgrounds.
It also tackles the intrinsic data imbalance problem in traffic light datasets, caused mainly by the low amount of samples of the yellow state.
arXiv Detail & Related papers (2020-11-07T19:57:22Z)
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.