STREETS: a benchmark dataset for suburban traffic forecasting

In this work, we introduce and benchmark STREETS, a novel traffic flow dataset from publicly available web cameras in the suburbs of Chicago, IL. STREETS addresses multiple limitations of existing vehicular traffic datasets. Many current datasets lack a coherent traffic network graph to describe the relationship between sensors. The datasets that do provide a graph depict traffic flow in urban population centers or highway systems and use costly sensors like induction loops. These contexts differ from that of a suburban traffic body. Our dataset provides over 4 million still images across 2.5 months and 100 web cameras in suburban Lake County, IL. We divide the cameras into two distinct communities, provide directed and undirected graphical representations of these traffic networks, and count vehicles to aggregate traffic statistics. Our goal is to give researchers a benchmark dataset for exploring the capabilities of inexpensive and non-invasive sensors like web cameras to understand complex traffic bodies in communities of any size. We perform thorough traffic forecasting experiments to benchmark several traffic forecasting models on STREETS and define evaluation metrics that are pertinent to understanding performance on our dataset.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2021

Author dc:creator

• Snyder, Corey

Contributors dc:contributor

• Do, Minh

• dataset
• computer vision
• graph signal processing
• machine learning
• traffic forecasting

Statement dc:rights

• Copyright 2020 Corey Snyder

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/109440

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/109440