# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 5 Traffic Prediction-Related Datasets
### Mobile Traffic Data  
As the demand for real-time and dynamic traffic information continues to increase, traditional fixed traffic data collection technologies and information processing methods, despite being relatively mature, face issues such as low coverage, high maintenance costs, and poor reliability. As a result, mobile traffic data collection technologies have gained \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Dataset** & **Features** & **Time Interval** & **Traffic Data** & **Source** \\ \hline \multirow{4}{*}{Stationary traffic data} & PeMS & Flow, speed & 5 min & 39,000 & [[https://github.com//[PERSON]/ASTGCN](https://github.com//[PERSON]/ASTGCN)]([https://github.com//[PERSON]/ASTGCN](https://github.com//[PERSON]/ASTGCN)) (accessed on 1 February 2025) \\ \cline{2-6}  & METR-LA & Speed & 5 min & 207 & [[https://github.com//ilyaguang/DCRNN](https://github.com//ilyaguang/DCRNN)]([https://github.com//ilyaguang/DCRNN](https://github.com//ilyaguang/DCRNN)) (accessed on 1 February 2025) \\ \cline{2-6}  & Loop & Speed & 5 min & 323 & [[https://github.com/zhiyong/Seattle-Loop-Data](https://github.com/zhiyong/Seattle-Loop-Data)]([https://github.com/zhiyong/Seattle-Loop-Data](https://github.com/zhiyong/Seattle-Loop-Data)) (accessed on 1 February 2025) \\ \cline{2-6}  & UVDS & Flow, speed & 5 min & 104 & [17] \\ \hline \multirow{4}{*}{Mobile traffic data} & TaxiBJ & Flow & 30 min & 34,000 vehicles & [[https://github.com/TolicWang/DeepST/tree/master/data/TaxiBJ](https://github.com/TolicWang/DeepST/tree/master/data/TaxiBJ)]([https://github.com/TolicWang/DeepST/tree/master/data/TaxiBJ](https://github.com/TolicWang/DeepST/tree/master/data/TaxiBJ)) (accessed on 1 February 2025) \\ \cline{2-6}  & SZ-taxi & Flow, speed & 15 min & 156 roads & [[https://paperswithcode.com/dataset/sz-taxi](https://paperswithcode.com/dataset/sz-taxi)]([https://paperswithcode.com/dataset/sz-taxi](https://paperswithcode.com/dataset/sz-taxi)) (accessed on 1 February 2025) \\ \cline{2-6}  & NYC Bike & Flow & 60 min & 13,000 bicycles & [[https://paperswithcode.com/dataset/nycbike1](https://paperswithcode.com/dataset/nycbike1)]([https://paperswithcode.com/dataset/nycbike1](https://paperswithcode.com/dataset/nycbike1)) (accessed on 1 February 2025) \\ \cline{2-6}  & Shanghai Taxi Dataset & Speed & 1 min & 4000 vehicles & [[https://gitcode.com/open-source-toolkit/659](https://gitcode.com/open-source-toolkit/659) ba]([https://gitcode.com/open-source-toolkit/659](https://gitcode.com/open-source-toolkit/659) ba) (accessed on 1 February 2025) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset summary. significant attention. Common data collection methods include floating car data collection, drone-based collection, and crowdsourcing techniques. While such data require processing, they are easier to obtain, highly accurate, and widely used in traffic flow prediction. 1. TaxiBJ Dataset: TaxiBJ is a dataset of Beijing taxi data that includes trajectory and meteorological data from over 34,000 taxis in the Beijing area over a period of 3 years. The data are converted into inflow and outflow traffic for various regions. The sampling interval for the dataset is 30 min, and it is primarily used for traffic demand prediction. 2. Shanghai Taxi Dataset: This dataset, proposed by the Smart City Research Group at the Hong Kong University of Science and Technology, contains GPS reports from 4000 taxis in Shanghai on 20 February 2007. The vehicle data are sampled at 1 min intervals and include information such as the vehicle ID, timestamp, longitude and latitude, and speed. 3. SZ-taxi Dataset: The SZ-taxi dataset consists of taxi trajectory data from Shenzhen, covering the period from 1 January to 31 January 2015. The dataset focuses on the Luohu District of Shenzhen and includes data from 156 main roads. Traffic speeds for each road are calculated every 15 min in this dataset. 4. NYC Bike Dataset: The NYC Bike dataset records bicycle trajectories collected from the New York City Bike Share system.