Datasets › Traffic
Traffic (Traffic Flow Forecasting Data Set)
Abstract: The task for this dataset is to forecast the spatio-temporal traffic volume based on the historical traffic volume and other features in neighboring locations.
| Data Set Characteristics | Number of Instances | Area | Attribute Characteristics | Number of Attributes | Date Donated | Associated Tasks | Missing Values |
|---|---|---|---|---|---|---|---|
| Multivariate | 2101 | Computer | Real | 47 | 2020-11-17 | Regression | N/A |
Source:
Liang Zhao, liang.zhao '@' emory.edu, Emory University.
Data Set Information:
The task for this dataset is to forecast the spatio-temporal traffic volume based on the historical traffic volume and other features in neighboring locations. Specifically, the traffic volume is measured every 15 minutes at 36 sensor locations along two major highways in Northern Virginia/Washington D.C. capital region. The 47 features include: 1) the historical sequence of traffic volume sensed during the 10 most recent sample points (10 features), 2) week day (7 features), 3) hour of day (24 features), 4) road direction (4 features), 5) number of lanes (1 feature), and 6) name of the road (1 feature). The goal is to predict the traffic volume 15 minutes into the future for all sensor locations. With a given road network, we know the spatial connectivity between sensor locations. For the detailed data information, please refer to the file README.docx.
Attribute Information:
The 47 features include: (1) the historical sequence of traffic volume sensed during the 10 most recent sample points (10 features), (2) week day (7 features), (3) hour of day (24 features), (4) road direction (4 features), (5) number of lanes (1 feature), and (6) name of the road (1 feature).
Relevant Papers:
Liang Zhao, Olga Gkountouna, and Dieter Pfoser. 2019. Spatial Auto-regressive Dependency Interpretable Learning Based on Spatial Topological Constraints. ACM Trans. Spatial Algorithms Syst. 5, 3, Article 19 (August 2019), 28 pages. DOI:[Web Link]
Citation Request:
To use these datasets, please cite the papers:
Liang Zhao, Olga Gkountouna, and Dieter Pfoser. 2019. Spatial Auto-regressive Dependency Interpretable Learning Based on Spatial Topological Constraints. ACM Trans. Spatial Algorithms Syst. 5, 3, Article 19 (August 2019), 28 pages. DOI:[Web Link]
Benchmarks archive 2025-07-28
All 2 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| GLinear | Traffic | GLinear MSE 0.3222 | Bridging Simplicity and Sophistication using GLinear: A... | t-rizvi/GLinear | 1 | Compare |
| Multivariate Time Series Forecasting | Traffic | GLinear MSE 0.3222 | Bridging Simplicity and Sophistication using GLinear: A... | t-rizvi/GLinear | 1 | Compare |
Papers archive 2025-07-28
1 shown of 1 paper with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 16. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| Bridging Simplicity and Sophistication using GLinear: A Novel Architecture for Enhanced Time Series Prediction | 1 | 2 | 2 Jan 2025 | not harvested |
Dataset loaders archive 2025-07-28
No loader listed in the archive.
Tasks archive 2025-07-28
License archive 2025-07-28
Creative Commons Attribution 4.0 International
Modalities archive 2025-07-28
Languages archive 2025-07-28
Variants archive 2025-07-28
- Traffic
1 variant name, as the archive lists them.
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