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Framework for the Selection of Loop Detectors for Macroscopic Fundamental Diagram Estimation

EasyChair Preprint no. 10424

19 pagesDate: June 20, 2023


The Macroscopic Fundamental Diagram (MFD) represents an increasingly established model for assessing the quality of traffic flow in networks. However, the uniqueness of an empirically estimated MFD cannot be guaranteed due to the problem of detector selection. Instationarity and varying flow patterns make it difficult to select the link flows that are representative of the traffic state in the whole network. This paper developed a new method for selecting loop detectors that represent a particular traffic state of a road network. The method relies on a metric of heterogeneity characterizing the role of a network link over the time of a day. The dispersion indicates the heterogeneity in traffic conditions and the dynamic role of each time interval. The heterogeneity-weighted saturation level of links is used to determine a ranking of links. The high-ranked links in the ranking represent the most homogenous sample of subset links. The study used the loop detector data of Zurich and London and a simulated network to compare both equal (classical) and dynamic weights (proposed) by selecting the sample links based on different saturation levels. Moreover, associating the saturation level with the heterogeneity level specified the links creating the heterogeneity in the road network primarily.

Keyphrases: entropy weights, Heterogeneity, loop detectors, Macroscopic Fundamental Diagram, TOPSIS

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Syed Muzammil Abbas Rizvi},
  title = {Framework for the Selection of Loop Detectors for Macroscopic Fundamental Diagram Estimation},
  howpublished = {EasyChair Preprint no. 10424},

  year = {EasyChair, 2023}}
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