Traffic Flow Forecasting
Donated on 6/17/2021
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
Multivariate
Subject Area
Engineering
Associated Tasks
Regression
Feature Type
-
# Instances
2101
# Features
-
Dataset 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 in this dataset represent?
traffic surveillance signals
Are there recommended data splits?
training vs testing
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
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).
Has Missing Values?
No
Introductory Paper
By Liang Zhao, Olga Gkountouna, and Dieter Pfoser. 2019
Published in ACM Transactions on Spatial Algorithms System
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset traffic_flow_forecasting = fetch_ucirepo(id=608) # data (as pandas dataframes) X = traffic_flow_forecasting.data.features y = traffic_flow_forecasting.data.targets # metadata print(traffic_flow_forecasting.metadata) # variable information print(traffic_flow_forecasting.variables)
Zhao,Liang. (2021). Traffic Flow Forecasting. UCI Machine Learning Repository. https://doi.org/10.24432/C57897.
@misc{misc_traffic_flow_forecasting_608,
author = {Zhao,Liang},
title = {{Traffic Flow Forecasting}},
year = {2021},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C57897}
}Keywords
Creators
Liang Zhao
lzhao9@gmu.edu
Emory University
DOI
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.