Knowledge Discovery and Causality in Urban City Traffic: A study using Planet Scale Vehicular Imagery Data

  • Authors:
  • Damien Fay;Gautam S. Thakur;Pan Hui;Ahmed Helmy

  • Affiliations:
  • Computer Science Department, University College Cork, Ireland;Oak Ridge National Laboratory, Oak Ridge, USA;Dept of Computer Science and Engineering, HKUST, Hong Kong;CISE, University of Florida, Gainesville, USA

  • Venue:
  • Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Computational Transportation Science
  • Year:
  • 2013

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Abstract

The increase in number of vehicles has created problems in many cities across the globe. Building comprehensive knowledge base about global city dynamics and traffic distribution is a key step to provide fundamental solution to the problems. In this paper, we examine a readily available data source; the existing infrastructure of traffic cameras around the world. We have collected real time traffic data from 2,700 public online traffic camera distributed across 10 cities in four continents for a duration of six months. Our platform allows us to automatically search public cameras, collect and process imagery data, remove outliers, and extract traffic density from those images in a highly scalable way. A time series model employing a co-integrated vector autoregression model is presented in which traffic forecasts may be produced and regions of the city not well observed may be suggested. In addition, a topological comparison of six of these networks is presented.