Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2018

Abstract

As road transportation supports both economic and social activities in developed cities, it is important to maintain smooth traffic on all highways and local roads. Whenever possible, traffic congestions should be detected early and resolved quickly. While existing traffic monitoring dashboard systems have been put in place in many cities, these systems require high-cost vehicle speed monitoring instruments and detect traffic congestion as independent events. There is a lack of low-cost dashboards to inspect and analyze the lifecycle of traffic congestion which is critical in assessing the overall impact of congestion, determining the possible the source(s) of congestion and its evolution. In the absence of publicly available sophisticated road sensor data which measures on-road vehicle speed, we make use of publicly available vehicle trajectory data to detect the lifecycle of traffic congestion, also known as congestion cascade. We have developed Traffic-Cascade, a dashboard system to identify traffic congestion events, compile them into congestion cascades, and visualize them on a web dashboard. Traffic-Cascade unveils spatio-temporal insights of the congestion cascades.

Keywords

Data mining, Anomaly detection, Visualization, Traffic congestion

Discipline

Databases and Information Systems | Transportation

Research Areas

Data Science and Engineering

Publication

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management: Torino, Italy, October 22-26

First Page

1955

Last Page

1958

ISBN

9781450360142

Identifier

10.1145/3269206.3269216

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher/LARC

Additional URL

https://doi.org/10.1145/3269206.3269216

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