Knowledge discovery and causality in urban city traffic: A study using planet scale vehicular imagery data

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.