Omkar2118/datasets huggingface dataset:
Traffic
Full name: 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](https://doi.org/10.1145/3339823)]

### 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](https://doi.org/10.1145/3339823)]