BjTT: A Large-Scale Multimodal Dataset for Traffic Prediction

Traffic prediction plays a significant role in Intelligent Transportation Systems (ITS). Although many datasets have been introduced to support the study of traffic prediction, most of them only provide time-series traffic data. However, urban transportation systems are always susceptible to various factors, including unusual weather and traffic accidents. Therefore, relying solely on historical data for traffic prediction greatly limits the accuracy of the prediction. In this paper, we introduce Beijing Text-Traffic (BjTT), a large-scale multimodal dataset for traffic prediction. BjTT comprises over 32,000 time-series traffic records, capturing velocity and congestion levels on more than 1,200 roads within the 5th ring area of Beijing. Meanwhile, each piece of traffic data is coupled with a text describing the traffic system (including time, location, and events). We detail the data collection and processing procedures and present a statistical analysis of the BjTT dataset. Furthermore, we conduct comprehensive experiments on the dataset with state-of-the-art traffic prediction methods and text-guided generative models, which reveal the unique characteristics of the BjTT. The dataset is available at https://github.com/ChyaZhang/BjTT.

I. Introduction

Traffic prediction stands as a prominent and vital task within the Intelligent Transportation Systems (ITS). The precise and timely prediction of traffic situations plays a significant role in refining individual travel decisions and alleviating traffic congestion. With increasing attention from researchers on traffic prediction, numerous datasets have been introduced to support the development of this field. Since traffic prediction relies heavily on historical data collected from road sensors, the majority of existing datasets [1], [2], [3], [4] focus on providing more historical traffic data.