Reconstruction of Congested Traffic Patterns Using Traffic State Detection in Autonomous Vehicles Based on Kerner’s Three Phase Traffic Theory

In recent years the models ASDA and FOTO based on Kerner’s three-phase traffic theory have been successfully applied to the detection and reconstruction of spatio-temporal congested traffic patterns using empirical data measured with stationary inductive loop detectors. Both models have been used in laboratory and online environments on highways in several countries. Another way of measuring traffic conditions in addition to stationary loop detectors is the use of either Floating Car Data (FCD) or Floating Phone Data (FPD), where the individual vehicles represent moving probes. In this paper an approach to the spatio-temporal interpretation of vehicle probe data is described. Instead of transmitting raw data to a processing centre, each probe performs beforehand a local traffic state detection according to Kerner’s congested phase definitions of “Synchronized Flow” and “Wide Moving Jams” and sends only these events to a processing centre or other probes. There a global aggregation of all events received by the probes is performed in order to detect and reconstruct complete spatio-temporal congested traffic patterns. One of the most important parameters is the number of required probe vehicles in order to be able to detect and reconstruct spatio-temporal congested traffic patterns with a specific quality demanded by future vehicle applications. Based on simulation environments this paper evaluates the required probe density and draws conclusions on the achievable reconstruction quality.

Language

  • English

Media Info

  • Media Type: CD-ROM
  • Features: Figures; References;
  • Pagination: 8p
  • Monograph Title: ITS in Daily Life

Subject/Index Terms

Filing Info

  • Accession Number: 01149321
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jan 28 2010 1:00PM