Data fusion algorithm for macroscopic fundamental diagram estimation

Lukas Ambühl, Monica Menendez

Research output: Contribution to journalArticlepeer-review


A promising framework that describes traffic conditions in urban networks is the macroscopic fundamental diagram (MFD), relating average flow and average density in a relatively homogeneous urban network. It has been shown that the MFD can be used, for example, for traffic access control. However, an implementation requires an accurate estimation of the MFD with the available data sources. Most scientific literature has considered the estimation of MFDs based on either loop detector data (LDD) or floating car data (FCD). In this paper, however, we propose a methodology for estimating the MFD based on both data sources simultaneously. To that end, we have defined a fusion algorithm that separates the urban network into two sub-networks, one with loop detectors and one without. The LDD and the FCD are then fused taking into account the accuracy and network coverage of each data type. Simulations of an abstract grid network and the network of the city of Zurich show that the fusion algorithm always reduces the estimation error significantly with respect to an estimation where only one data source is used. This holds true, even when we account for the fact that the probe penetration rate of FCD needs to be estimated with loop detectors, hence it might also include some errors depending on the number of loop detectors, especially when probe vehicles are not homogeneously distributed within the network.

Original languageEnglish (US)
Pages (from-to)184-197
Number of pages14
JournalTransportation Research Part C: Emerging Technologies
StatePublished - Oct 1 2016


  • Floating car data (FCD)
  • Fusion
  • Loop detector data (LDD)
  • MFD estimation
  • Probe penetration estimation
  • Simulation

ASJC Scopus subject areas

  • Transportation
  • Automotive Engineering
  • Civil and Structural Engineering
  • Management Science and Operations Research


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