A Geomagnetic Sensor Dataset for Traffic Flow Prediction

Huanchen Wang1; Quanjun Chen2; Zheng Dong1; Xuan Song1; Hao Tian3; Donglong Yang3; Manxia Liu3

10.1109/BigData55660.2022.10020763

2022

978-1-6654-8046-8

2022 IEEE International Conference on Big Data (Big Data)

2419-2422

17-20 Dec. 2022

Osaka, Japan

Traffic state prediction is essential in Intelligent Transportation Systems for surveillance, management, and daily commuting. For developing high-accuracy prediction models, real-world traffic state datasets are necessary for training model parameters and evaluating prediction results. However, limited by the existing traffic collection devices, most of the current open datasets for traffic state prediction cannot obtain accurate traffic flow information. In contrast, some datasets directly use detection devices in freeway systems, so they cannot reflect complex urban traffic states. Therefore, a dataset from advanced devices that can record the flow from point to point on an urban road network attracts more attention and drives the progress of research on traffic state prediction models. To deal with the above issues, we introduce a Suburban Traffic Flow dataset using Geomagnetic sensors, or STF-G dataset, constructed for traffic flow prediction. The STF-G dataset consists of 2.5 billion vehicle driving scenarios and 319 corresponding geomagnetic sensors. The data was collected over 20 months and processed with two regional road graphs. We also do the Benchmark experiments in STF-G for analyzing and evaluating the performance of graph neural network models in traffic flow prediction and compare them to the other datasets with the same baseline.

Geomagnetic Sensor Dataset Benchmark Traffic State Prediction Graph Neural Networks

第一

IEEE

1.Southern University of Science and Technology
2.Center for Spatial Information Science, The University of Tokyo
3.Transport Bureau of Shenzhen Municipality

Huanchen Wang,Quanjun Chen,Zheng Dong,et al. A Geomagnetic Sensor Dataset for Traffic Flow Prediction[C],2022:2419-2422.