Comparing Speed Data from Stationary Detectors Against Floating-Car Data^⁎

This paper compares speed data measured by induction loops of stationary detectors with reported speeds from floating-car data, which are based on most recent GPS observations of probe vehicles. Detector data are aggregated over a one minute time interval and that means a 30 s delay occurs on average. The time delay issues with respect to floating-car data are quite convoluted with many influences: (i) the update frequencies from vehicles to the backend server, (ii) the fleet size of floating cars, (iii) the current traffic flow, and (iv) the provider treatment. The floating-car dataset has a high spatial resolution with an average segment length of 100 m suited for large-scale traffic observation and management. The spatial dimension of detector data can only be reconstructed ex-post from spotty positions (mean detector positions distance approx. 1.3 km). The paper analyzes which source is more advantageous in terms of detecting traffic jams, high temporal availability of detector data or detailed spatial resolution of floating-car data. An algorithm is presented to compute the jam detection duration, which means we are able to recognize which data source detects the jam earliest. The results demonstrate that there exist regions along certain road stretches where floating-car data clearly outperform stationary data. However, in regions where detectors are densely placed, stationary sensor data recognize a jam situation approx. 2 min earlier than floating-car based speed data. The datasets cover a period of 80 days in 2015 for both driving directions on the German autobahn A9 in the north of Munich.