A Cost-Effective Method for the Detection of Queue Lengths at Traffic Lights

  • Thorsten Neumann
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 144)


Limited road capacities and an increasing traffic volume are, or will become a serious problem for urban mobility in many regions of the world such as Europe, China, Japan, or the USA. To ensure an acceptable level of traffic quality, local authorities need reliable traffic state information which can be used for the optimization of traffic management, e.g., for improvements in the control of traffic lights.


Road Network Queue Length Traffic Light Traffic Demand Traffic Situation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The author wants to thank Dr. Peter Wagner for fruitful discussions about the topic of traffic data collection and traffic modeling. By interesting ideas, he and Dr. Rüdiger Ebendt initialized the work on the method described above.


  1. Bernhard J, Riedel T (1999) Erkennung von Stau mit kurzen Schleifendetektoren. In: Tagungsbericht Heureka ´99. FGSV Verlag, KölnGoogle Scholar
  2. Brilon W, Wu N (1990) Delays at fixed-time traffic signals under time-dependent traffic conditions. Traffic Eng Control 12(1990):623–631Google Scholar
  3. Kerner B, Mayer J, Heil M, Rehborn H (2002) Verfahren zur Gewinnung von Verkehrsdaten für ein Verkehrsnetz mit verkehrsgeregelten Netzknoten durch Meldefahrzeuge. Patent specification DE “100 18 562 C 1”Google Scholar
  4. Kerner B, Demir C, Herrtwich RG et al (2005) Traffic state detection with floating car data in road networks. ITS 2005 Proceedings, 44–49Google Scholar
  5. Linauer M (2005) Generierung streckenbezogener Verkehrsdaten als Basis für den Einsatz in Verkehrstelematiksystemen. University of Natural Resources and Applied Life Sciences, ViennaGoogle Scholar
  6. Mück J (2002) Schätzverfahren für den Verkehrszustand an Lichtsignalanlagen unter Verwendung halteliniennaher Detektoren. In: Tagungsbericht Heureka ´02. FGSV Verlag, KölnGoogle Scholar
  7. Nagel K, Schreckenberg M (1992) A cellular automaton model for freeway traffic. J Phys I France 2:2221–2229CrossRefGoogle Scholar
  8. Neumann T (2007) Profile lokaler Verkehrsdichten auf Straßensegmenten mit lichtsignalgesteuertem Abfluss. Technical report (unpublished)Google Scholar
  9. Neumann T, Wagner P (2008) Delay times in a cellular traffic flow model for road sections with periodic outflow. Eur Phys J B 63:255–264CrossRefGoogle Scholar
  10. Neumann T (2009) Efficient queue length detection at traffic signals using probe vehicle data and data fusion. ITS World Congress 2009, Stockholm, Sweden. SubmittedGoogle Scholar
  11. Schäfer RP, Thiessenhusen KU, Brockfeld E, Wagner P (2002) A traffic information system by means of real-time floating-car data. ITS World Congress 2002, Chicago, USAGoogle Scholar
  12. Skwarek V, Lampl D (2007) Verfahren zur Erkennung eines Stauereignisses. Patent specification DE “10 2005 060 856 A 1”Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Thorsten Neumann
    • 1
  1. 1.German Aerospace CenterInstitute of Transportation SystemsBerlinGermany

Personalised recommendations