· 2016
DNN-based prediction model for spatio-temporal data
- 663citations
- 2016year
Short summary
A novel Deep-Spatio-Temporal (DeepST) model leverages deep learning to predict spatio-temporal data by simultaneously modeling spatial dependencies and temporal patterns (closeness, period, trend), outperforming four baseline methods.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces DeepST, a deep learning model for spatio-temporal data prediction.
- DeepST's architecture comprises spatio-temporal and global components.
- The spatio-temporal component uses CNNs to model spatial dependencies and temporal patterns (closeness, period, trend).
- The global component captures external factors like day of the week.
- DeepST outperforms four baseline methods in experiments on diverse ST datasets.
AI-generated from the title and abstract; the full text is not read.
Abstract
Advances in location-acquisition and wireless communication technologies have led to wider availability of spatio-temporal (ST) data, which has unique spatial properties (i.e. geographical hierarchy and distance) and temporal properties (i.e. closeness, period and trend). In this paper, we propose a <u>Deep</u>-learning-based prediction model for <u>S</u>patio-<u>T</u>emporal data (DeepST). We leverage ST domain knowledge to design the architecture of DeepST, which is comprised of two components: spatio-temporal and global. The spatio-temporal component employs the framework of convolutional neural networks to simultaneously model spatial near and distant dependencies, and temporal closeness, period and trend. The global component is used to capture global factors, such as day of the week, weekday or weekend. Using DeepST, we build a real-time crowd flow forecasting system called UrbanFlow1. Experiment results on diverse ST datasets verify DeepST's ability to capture ST data's spatio-temporal properties, showing the advantages of DeepST beyond four baseline methods.
The authors' abstract, as published at the source., 2016 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Transportation
TransportationSocial Sciences