A Visual Analytics System for Exploring, Monitoring, and Forecasting Road Traffic Congestion

Chunggi Lee, Yeonjun Kim, Seungmin Jin, Dongmin Kim, Ross Maciejewski, David Ebert, Sungahn Ko

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

We present an interactive visual analytics system that enables traffic congestion exploration, surveillance, and forecasting based on vehicle detector data. Through domain expert collaboration, we have extracted task requirements, incorporated the Long Short-Term Memory (LSTM) model for congestion forecasting, and designed a weighting method for detecting the causes of congestion and congestion propagation directions. Our visual analytics system is designed to enable users to explore congestion causes, directions, and severity. Congestion conditions of a city are visualized using a Volume-Speed Rivers (VSRivers) visualization that simultaneously presents traffic volumes and speeds. To evaluate our system, we report performance comparison results, wherein our model is more accurate than other forecasting algorithms. We demonstrate the usefulness of our system in the traffic management and congestion broadcasting domains through three case studies and domain expert feedback.

Original languageEnglish (US)
Article number8735916
Pages (from-to)3133-3146
Number of pages14
JournalIEEE Transactions on Visualization and Computer Graphics
Volume26
Issue number11
DOIs
StatePublished - Nov 1 2020

Keywords

  • LSTM
  • Traffic
  • congestion
  • deep learning
  • forecasting
  • predictive analysis
  • road
  • surveillance
  • visualization

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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