Real Time Urban Traffic Data Pinpointing Most Important Crossroads

Designing and deploying intelligent transportation systems in a city always requires finding the important intersections, which is itself a difficult and subjective task. Using real-time data collected from a multitude of data sources we propose a new data fusion method and introduce a new metric for evaluating node importance based on the traffic volume. We correlate this metric with the one of network betweenness, proving the possibility of using the latter with good enough results for practical applications.