Comparison of Floating-Car Based Speed Data with Stationary Detector 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 one minute so they are 30s old on average. The time delay of floating-car data is more complex. Significant influences are (i) the update frequencies from vehicles to the backend server, (ii) the fleet size of the 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 100m 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. 1.3km). 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. The analysis includes spatiotemporal dynamics with traffic jam patterns. Furthermore, an algorithm is presented to compute the jam detection duration meaning which data source recognizes a jam earlier. The results show that regions exist along the considered road stretch where floating-car data clearly outperform stationary data because of their disadvantageous positions but in regions where detectors are placed densely, 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 German autobahn A9 in the north of Munich.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 13p

Subject/Index Terms

Filing Info

  • Accession Number: 01664300
  • Record Type: Publication
  • Report/Paper Numbers: 18-01034
  • Files: TRIS, TRB, ATRI
  • Created Date: Jan 8 2018 10:15AM