Traffic Flow Forecasting

Donated on 5/15/2022

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.

Dataset Characteristics

Other

Subject Area

Computer Science

Associated Tasks

Regression

Attribute Type

-

# Instances

2101

# Attributes

-

Information

For what purpose was the dataset created?

To share the research community with a benchmark dataset for spatiotemporal prediction

Who funded the creation of the dataset?

National Science Foundation

What do the instances that comprise the dataset represent?

traffic surveillance signals

Was there any data preprocessing performed?

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

Has the dataset been used for any tasks already?

It has been used for predicting the traffic flow

Additional Information

Attribute information: The 47 attributes 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).

Citation Requests/Acknowledgements

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:https://doi.org/10.1145/3339823

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Keywords

traffic flow predictionspatialspatiotemporal

Creators

Liang Zhao

liang.zhao@emory.edu

DOI

10.1145/3339823

License

This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.

This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.

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